From aeb3b6a4d3661df79caa27710c605fdc7d4dce4f Mon Sep 17 00:00:00 2001 From: ybc Date: Thu, 22 Oct 2020 21:07:14 -0700 Subject: [PATCH 01/44] Add hierachical_neighbor_allreduce python interface --- bluefog/torch/mpi_ops.cc | 2 +- bluefog/torch/mpi_ops.h | 2 +- bluefog/torch/mpi_ops.py | 84 +++++++++++++++++++++++++++++++++++++-- examples/pytorch_mnist.py | 6 +++ 4 files changed, 89 insertions(+), 5 deletions(-) diff --git a/bluefog/torch/mpi_ops.cc b/bluefog/torch/mpi_ops.cc index fe62582e..b4f5f0ea 100644 --- a/bluefog/torch/mpi_ops.cc +++ b/bluefog/torch/mpi_ops.cc @@ -306,7 +306,7 @@ int DoNeighborAllgather(::torch::Tensor tensor, ::torch::Tensor output, int DoNeighborAllreduce(::torch::Tensor tensor, ::torch::Tensor output, double self_weight, const std::unordered_map& neighbor_weights, const std::vector& send_neighbors, bool enable_topo_check, - bool avg_computation, const std::string& name) { + bool avg_computation, bool is_hierarchical, const std::string& name) { ThrowIfError(common::CheckInitialized()); auto handle = handle_manager.AllocateHandle(); diff --git a/bluefog/torch/mpi_ops.h b/bluefog/torch/mpi_ops.h index df6d755e..ba495641 100644 --- a/bluefog/torch/mpi_ops.h +++ b/bluefog/torch/mpi_ops.h @@ -115,7 +115,7 @@ NEIGHBOR_ALLGATHER_H(torch_cuda_DoubleTensor, THCudaDoubleTensor) THTensor* tensor, THTensor* output, double self_weight, \ const std::unordered_map& neighbor_weights, \ const std::vector& send_neighbors, bool enable_topo_check, \ - bool avg_computation, char* name); + bool avg_computation, bool is_hierarchical, char* name); NEIGHBOR_ALLREDUCE_H(torch_HalfTensor, THHalfTensor) NEIGHBOR_ALLREDUCE_H(torch_FloatTensor, THFloatTensor) diff --git a/bluefog/torch/mpi_ops.py b/bluefog/torch/mpi_ops.py index b78671fe..f6550962 100644 --- a/bluefog/torch/mpi_ops.py +++ b/bluefog/torch/mpi_ops.py @@ -414,7 +414,8 @@ def _neighbor_allreduce_nonblocking(tensor, output, self_weight, neighbor_weight handle = getattr(mpi_lib, function)(tensor, output, self_weight, neighbor_weights, send_neighbors, enable_topo_check, avg_computation, - name.encode() if name is not None else "") + is_hierarchical=False, + name=name.encode() if name is not None else "") _handle_map[handle] = (tensor, output) return handle @@ -518,9 +519,86 @@ def neighbor_allreduce_nonblocking(tensor: torch.Tensor, else: first_dim = tensor.shape[0] * len(neighbor_weights) new_shape = torch.Size([first_dim] + list(tensor.shape[1:])) - output = tensor.new(new_shape) # Pre-allocate the memory for the output. + output = tensor.new(new_shape) # Pre-allocate the memory for the output. return _neighbor_allreduce_nonblocking(tensor, output, self_weight, neighbor_weights, - send_neighbors, enable_topo_check, name) + send_neighbors, enable_topo_check, name=name) + + +def hierachical_neighbor_allreduce(tensor: torch.Tensor, + self_weight: float, + neighbor_machine_weights: Dict[int, float], + send_neighbor_machines: List[int], + enable_topo_check: bool = True, + name: Optional[str] = None) -> torch.Tensor: + # TODO(ybc) add check for self_weight and neighbor_machine_weights. + if (self_weight is None and neighbor_machine_weights is not None) or \ + (self_weight is not None and send_neighbor_machines is None): + raise ValueError("Arguments self_weight and neighbor_machine_weights have to " + "be presented at the same time") + handle = hierachical_neighbor_allreduce_nonblocking( + tensor, self_weight, neighbor_machine_weights, send_neighbor_machines, + enable_topo_check, name) + return synchronize(handle) + + +def hierachical_neighbor_allreduce_nonblocking(tensor: torch.Tensor, + self_weight: float, + neighbor_machine_weights: Dict[int, float], + send_neighbor_machines: List[int], + enable_topo_check: bool = True, + name: Optional[str] = None) -> int: + if (self_weight is None or neighbor_machine_weights is None): + raise ValueError("Arguments self_weight and neighbor_weights cannot be empty or None.") + if (self_weight is None and neighbor_machine_weights is not None) or \ + (self_weight is not None and neighbor_machine_weights is None): + raise ValueError("Arguments self_weight and neighbor_weights have to be presented at " + "the same time") + + first_dim = tensor.shape[0] * len(neighbor_machine_weights) + new_shape = torch.Size([first_dim] + list(tensor.shape[1:])) + output = tensor.new(new_shape) # Pre-allocate the memory for the output. + + return _hierachical_neighbor_allreduce_nonblocking( + tensor, output, self_weight, neighbor_machine_weights, + send_neighbor_machines, enable_topo_check, name=name) + + +def _hierachical_neighbor_allreduce_nonblocking( + tensor, output, self_weight, neighbor_machine_weights, + send_neighbor_machines, enable_topo_check, name): + assert not is_homogeneous, \ + "hierachical_neighbor_allreduce should be used under homogeneous environment only" + function = _check_function(_neighbor_allreduce_function_factory, tensor) + if self_weight is not None and neighbor_machine_weights is not None: + if not isinstance(neighbor_machine_weights, dict): + raise ValueError("Argument neighbor_weights has to be a dictionary map from the " + "(in-)neighbor rank to the weights.") + if not isinstance(self_weight, float): + raise ValueError( + "Argument self_weight has to be a float for self rank.") + uniform_weights = 1.0/(len(neighbor_machine_weights)+1) + avg_computation = False + if abs(self_weight - uniform_weights) > 1e-6: + avg_computation = True + for n_weights in neighbor_machine_weights.values(): + if abs(n_weights - uniform_weights) > 1e-6: + avg_computation = True + break + else: + raise ValueError("Arguments self_weight and neighbor_weights cannot be empty or None.") + + # Translate machine id into rank id. + node_per_machine = local_size() + neighbor_weights = { + node_per_machine*m: weights for (m, weights) in neighbor_machine_weights.items()} + send_neighbors = [node_per_machine*m for m in send_neighbor_machines] + avg_computation = False + handle = getattr(mpi_lib, function)(tensor, output, self_weight, neighbor_weights, + send_neighbors, enable_topo_check, avg_computation, + is_hierarchical=True, + name=name.encode() if name is not None else "") + _handle_map[handle] = (tensor, output) + return handle def _pair_gossip_nonblocking_function_factory(tensor): diff --git a/examples/pytorch_mnist.py b/examples/pytorch_mnist.py index 67b6c9d9..460f9fe7 100644 --- a/examples/pytorch_mnist.py +++ b/examples/pytorch_mnist.py @@ -270,6 +270,9 @@ def train(epoch): if args.cuda: data, target = data.cuda(), target.cuda() + if args.dist_optimizer == 'neighbor_allreduce' and (batch_idx % 100 == 99): + optimizer.use_allreduce_in_communication() + optimizer.zero_grad() output = model(data) loss = F.nll_loss(output, target) @@ -289,6 +292,9 @@ def train(epoch): ) ) + if args.dist_optimizer == 'neighbor_allreduce' and (batch_idx % 100 == 99): + optimizer.use_neighbor_allreduce_in_communication() + def metric_average(val, name): tensor = torch.tensor(val) # pylint: disable=not-callable From 84e6975b964bbce0d8499ca66f46fc9aa9ca4f25 Mon Sep 17 00:00:00 2001 From: ybc Date: Thu, 22 Oct 2020 21:39:09 -0700 Subject: [PATCH 02/44] Pass down is_hierachical down to perform --- bluefog/common/common.h | 3 +++ bluefog/common/mpi_controller.cc | 10 ++++++++++ bluefog/common/operations.cc | 5 +++++ bluefog/common/operations.h | 1 + bluefog/torch/mpi_ops.cc | 29 ++++++++++++++++++----------- bluefog/torch/mpi_ops.py | 6 ++---- 6 files changed, 39 insertions(+), 15 deletions(-) diff --git a/bluefog/common/common.h b/bluefog/common/common.h index e11a156c..5dd3bcae 100644 --- a/bluefog/common/common.h +++ b/bluefog/common/common.h @@ -278,6 +278,9 @@ struct TensorTableEntry { // Boolean value for enabling topology check. bool enable_topo_check = false; + // Boolean value for hierarchical operation or not. + bool is_hierarchical = false; + // The ops requires the mutex. bool require_mutex = false; diff --git a/bluefog/common/mpi_controller.cc b/bluefog/common/mpi_controller.cc index ba878ed5..efc2a215 100644 --- a/bluefog/common/mpi_controller.cc +++ b/bluefog/common/mpi_controller.cc @@ -359,6 +359,11 @@ std::string GenerateNeighborAllreduceErrorMessage(const std::vector& void MPIController::NeighborAllreduce(TensorTableEntry& entry) { const void* sendbuf = entry.tensor->data(); int num_elements = entry.tensor->shape().num_elements(); + // TODO(ybc) support this + if (entry.is_hierarchical) { + throw std::runtime_error( + "Hierachical neighbor_allreduce is only supported under NCCL case."); + } Timeline* timeline_ptr; Status timeline_status = GetBluefogTimeline(timeline_ptr); @@ -484,6 +489,11 @@ void MPIController::Allreduce(std::vector& entries) { void MPIController::NeighborAllreduce(std::vector& entries) { auto& first_entry = entries[0]; with_device device_guard(first_entry.device); + // TODO(ybc) support this + if (first_entry.is_hierarchical) { + throw std::runtime_error( + "Hierachical neighbor_allreduce is only supported under NCCL case."); + } void* buffer_data; size_t buffer_len = 0; diff --git a/bluefog/common/operations.cc b/bluefog/common/operations.cc index bf1d5d56..ae4fb970 100644 --- a/bluefog/common/operations.cc +++ b/bluefog/common/operations.cc @@ -296,6 +296,8 @@ Response ConstructResponse(MessageTable* message_table, std::string name) { } } + // TODO(ybc) add check for is_hierarchical for neighbor_allreduce. + // If we are doing an (neighbor_)allreduce or broadcast, check that all tensor // shapes are identical. if (!error) { @@ -954,6 +956,7 @@ void NegotiateOfRequestOfMaster(BluefogGlobalState& state, if (response.response_type() == new_response.response_type() && response.devices() == new_response.devices() && entry.tensor->dtype() == new_entry.tensor->dtype() && + entry.is_hierarchical == new_entry.is_hierarchical && IsSameNeighborList(entry.send_neighbors, new_entry.send_neighbors) && IsSameNeighborList(entry.recv_neighbors, @@ -1484,6 +1487,7 @@ Status EnqueueTensorNeighborAllreduce(std::shared_ptr tensor, std::shared_ptr ready_event, std::shared_ptr> recv_neighbors, std::shared_ptr> send_neighbors, + bool is_hierarchical, bool enable_topo_check, const std::string& name, const int device, StatusCallback callback) { @@ -1505,6 +1509,7 @@ Status EnqueueTensorNeighborAllreduce(std::shared_ptr tensor, e.ready_event = ready_event; e.recv_neighbors = recv_neighbors; e.send_neighbors = send_neighbors; + e.is_hierarchical = is_hierarchical; e.enable_topo_check = enable_topo_check; e.device = device; e.callback = callback; diff --git a/bluefog/common/operations.h b/bluefog/common/operations.h index db20a1f0..66293cde 100644 --- a/bluefog/common/operations.h +++ b/bluefog/common/operations.h @@ -139,6 +139,7 @@ Status EnqueueTensorNeighborAllreduce(std::shared_ptr tensor, std::shared_ptr ready_event, std::shared_ptr> recv_neighbors, std::shared_ptr> send_neighbors, + bool is_hierarchical, bool enable_topo_check, const std::string& name, const int device, StatusCallback callback); diff --git a/bluefog/torch/mpi_ops.cc b/bluefog/torch/mpi_ops.cc index b4f5f0ea..07ea7266 100644 --- a/bluefog/torch/mpi_ops.cc +++ b/bluefog/torch/mpi_ops.cc @@ -338,11 +338,13 @@ int DoNeighborAllreduce(::torch::Tensor tensor, ::torch::Tensor output, auto bf_recv_neighbors = std::make_shared>(recv_neighbors); auto bf_send_neighbors = std::make_shared>(send_neighbors); auto ready_event = RecordReadyEvent(device); - auto enqueue_result = EnqueueTensorNeighborAllreduce( - bf_tensor, bf_output, bf_context, ready_event, bf_recv_neighbors, bf_send_neighbors, - enable_topo_check, op_name, CPU_DEVICE_ID, callback_wrapper([self_weight, neighbor_weights, - avg_computation, cpu_output, tensor, recv_neighbors, send_neighbors, output, device]() - mutable { + auto enqueue_result = EnqueueTensorNeighborAllreduce( + bf_tensor, bf_output, bf_context, ready_event, bf_recv_neighbors, + bf_send_neighbors, is_hierarchical, enable_topo_check, op_name, + CPU_DEVICE_ID, + callback_wrapper([self_weight, neighbor_weights, avg_computation, + cpu_output, tensor, recv_neighbors, send_neighbors, + output, device]() mutable { with_device device_guard(device); output.copy_(cpu_output); int recv_size = bluefog_neighbor_size(); @@ -381,8 +383,10 @@ int DoNeighborAllreduce(::torch::Tensor tensor, ::torch::Tensor output, for (int i = 0; i < indgree; i++) { double weight = 0.0; int recv_rank; - if(send_neighbors.empty()) recv_rank = *(sources_ptr+i); - else recv_rank = recv_neighbors[i]; + if(send_neighbors.empty()) + recv_rank = *(sources_ptr + i); + else + recv_rank = recv_neighbors[i]; auto it = neighbor_weights.find(recv_rank); if (it != neighbor_weights.end()) { weight = it->second; @@ -430,7 +434,7 @@ int DoNeighborAllreduce(::torch::Tensor tensor, ::torch::Tensor output, auto enqueue_result = EnqueueTensorNeighborAllreduce( bf_tensor, bf_output, bf_context, ready_event, bf_recv_neighbors, bf_send_neighbors, - enable_topo_check, op_name, device, callback_wrapper([self_weight, neighbor_weights, + is_hierarchical, enable_topo_check, op_name, device, callback_wrapper([self_weight, neighbor_weights, avg_computation, recv_neighbors, send_neighbors, tensor, output] () mutable { int recv_size = bluefog_neighbor_size(); if(!send_neighbors.empty()) recv_size = recv_neighbors.size(); @@ -450,10 +454,13 @@ int DoNeighborAllreduce(::torch::Tensor tensor, ::torch::Tensor output, int outdegree = 0; int* sources_ptr = nullptr; int* destinations_ptr = nullptr; - bluefog_load_topology(&indgree, sources_ptr, &outdegree, - destinations_ptr); auto output_reduced = output_buffer.slice(0, 0, first_dim); - if (!send_neighbors.empty()) indgree = recv_neighbors.size(); + if (send_neighbors.empty()) { + bluefog_load_topology(&indgree, sources_ptr, &outdegree, + destinations_ptr); + } else { + indgree = recv_neighbors.size(); + } for (int i = 0; i < indgree; i++) { double weight = 0.0; int recv_rank; diff --git a/bluefog/torch/mpi_ops.py b/bluefog/torch/mpi_ops.py index f6550962..a600f4c0 100644 --- a/bluefog/torch/mpi_ops.py +++ b/bluefog/torch/mpi_ops.py @@ -414,8 +414,7 @@ def _neighbor_allreduce_nonblocking(tensor, output, self_weight, neighbor_weight handle = getattr(mpi_lib, function)(tensor, output, self_weight, neighbor_weights, send_neighbors, enable_topo_check, avg_computation, - is_hierarchical=False, - name=name.encode() if name is not None else "") + False, name.encode() if name is not None else "") _handle_map[handle] = (tensor, output) return handle @@ -595,8 +594,7 @@ def _hierachical_neighbor_allreduce_nonblocking( avg_computation = False handle = getattr(mpi_lib, function)(tensor, output, self_weight, neighbor_weights, send_neighbors, enable_topo_check, avg_computation, - is_hierarchical=True, - name=name.encode() if name is not None else "") + True, name.encode() if name is not None else "") _handle_map[handle] = (tensor, output) return handle From cc4fb1d520bf33ae05c3c7bae02cb30b66aa42ea Mon Sep 17 00:00:00 2001 From: ybc Date: Thu, 22 Oct 2020 21:59:47 -0700 Subject: [PATCH 03/44] Build local nccl comm --- bluefog/common/nccl_controller.cc | 18 +++++++++++++++--- bluefog/common/nccl_controller.h | 7 ++++--- 2 files changed, 19 insertions(+), 6 deletions(-) diff --git a/bluefog/common/nccl_controller.cc b/bluefog/common/nccl_controller.cc index a2249d80..074c88ee 100644 --- a/bluefog/common/nccl_controller.cc +++ b/bluefog/common/nccl_controller.cc @@ -69,7 +69,8 @@ ncclDataType_t GetNCCLDataType(const DataType bf_data_type) { } void NCCLContext::Initialize(const int rank, const int size, - const int local_rank) { + const int local_rank, const int local_size, + const MPI_Comm& world_comm, const MPI_Comm& local_comm) { if (is_initialized) { BFLOG(DEBUG) << "NCCL context has been initialized but NCCLContext::Initialize " @@ -84,7 +85,7 @@ void NCCLContext::Initialize(const int rank, const int size, ncclUniqueId nccl_id; if (rank == 0) ncclGetUniqueId(&nccl_id); MPICHECK( - MPI_Bcast((void*)&nccl_id, sizeof(nccl_id), MPI_BYTE, 0, MPI_COMM_WORLD)); + MPI_Bcast((void*)&nccl_id, sizeof(nccl_id), MPI_BYTE, 0, world_comm)); // Assume one device per process int nDevices = 0; @@ -96,6 +97,15 @@ void NCCLContext::Initialize(const int rank, const int size, greatest_priority)); // TODO(ybc) Handle the error case then np > number of GPU properly. NCCLCHECK(ncclCommInitRank(&nccl_comm, size, nccl_id, rank)); + MPI_Barrier(world_comm); + + // Build local nccl + ncclUniqueId local_nccl_id; + if (local_rank == 0) ncclGetUniqueId(&local_nccl_id); + MPICHECK(MPI_Bcast((void*)&local_nccl_id, sizeof(local_nccl_id), MPI_BYTE, 0, + local_comm)); + NCCLCHECK(ncclCommInitRank(&nccl_local_comm, local_size, local_nccl_id, + local_rank)); cuda_device = local_rank % nDevices; is_initialized = true; @@ -182,7 +192,9 @@ cudaError_t NCCLContext::ReleaseCudaEvent(cudaEvent_t event) { } void NCCLController::Initialize() { - nccl_ctx_.Initialize(mpi_ctx_.rank_, mpi_ctx_.size_, mpi_ctx_.local_rank_); + nccl_ctx_.Initialize(mpi_ctx_.rank_, mpi_ctx_.size_, mpi_ctx_.local_rank_, + mpi_ctx_.local_size_, mpi_ctx_.mpi_comm, + mpi_ctx_.local_comm); #if NCCL_MINOR < 7 InitPeerCommunicators(); #endif diff --git a/bluefog/common/nccl_controller.h b/bluefog/common/nccl_controller.h index 82ec3f09..7060ebc4 100644 --- a/bluefog/common/nccl_controller.h +++ b/bluefog/common/nccl_controller.h @@ -56,8 +56,9 @@ class NCCLContext { NCCLContext(const NCCLContext&) = delete; NCCLContext& operator=(NCCLContext other) = delete; - void Initialize(const int rank, const int size, - const int local_rank); // only initial nccl_comm etc. + void Initialize(const int rank, const int size, const int local_rank, + const int local_size, const MPI_Comm& world_comm, + const MPI_Comm& local_comm); // only initial nccl_comm etc. void Finalize(); // nccl_comm, peer, window will all be cleaned. #if NCCL_MINOR < 7 void CleanPeerCommunicators(); @@ -67,8 +68,8 @@ class NCCLContext { cudaError_t GetCudaEvent(cudaEvent_t* event); cudaError_t ReleaseCudaEvent(cudaEvent_t event); - // TODO(ybc) Create intra-comm to allow the ops lik in-node allreduce. ncclComm_t nccl_comm; // Store a global nccl comm. + ncclComm_t nccl_local_comm; // Store a local nccl comm. cudaStream_t stream; // We reuse CUDA events as it appears that their creation carries non-zero cost. From 2df368a28b8741af9dc73cc7fafb55f6963fd9a8 Mon Sep 17 00:00:00 2001 From: ybc Date: Thu, 22 Oct 2020 23:06:11 -0700 Subject: [PATCH 04/44] add ihierarchical neighbor allreduce impl in NCCL --- bluefog/common/nccl_controller.cc | 178 ++++++++++++++++++++++-------- bluefog/torch/mpi_ops.cc | 35 ++++-- 2 files changed, 157 insertions(+), 56 deletions(-) diff --git a/bluefog/common/nccl_controller.cc b/bluefog/common/nccl_controller.cc index 074c88ee..8180847d 100644 --- a/bluefog/common/nccl_controller.cc +++ b/bluefog/common/nccl_controller.cc @@ -670,33 +670,70 @@ void NCCLController::NeighborAllreduce(TensorTableEntry& entry) { } #if NCCL_MINOR > 6 - ncclGroupStart(); - if (entry.send_neighbors->empty()) { - for (int i = 0; i < mpi_ctx_.neighbor_indgree_; i++) { - int recv_rank = mpi_ctx_.neighbor_in_ranks_[i]; - void* recvbuf = (void*)(static_cast(entry.output->data()) + - num_elements * i * element_size); - NCCLCHECK(ncclRecv(recvbuf, num_elements, GetNCCLDataType(entry.tensor), - recv_rank, nccl_ctx_.nccl_comm, nccl_ctx_.stream)); - } - for (int send_rank : mpi_ctx_.neighbor_out_ranks_) { - NCCLCHECK(ncclSend(sendbuf, num_elements, GetNCCLDataType(entry.tensor), - send_rank, nccl_ctx_.nccl_comm, nccl_ctx_.stream)); + if (!entry.is_hierarchical) { + ncclGroupStart(); + if (entry.send_neighbors->empty()) { + for (int i = 0; i < mpi_ctx_.neighbor_indgree_; i++) { + int recv_rank = mpi_ctx_.neighbor_in_ranks_[i]; + void* recvbuf = (void*)(static_cast(entry.output->data()) + + num_elements * i * element_size); + NCCLCHECK(ncclRecv(recvbuf, num_elements, GetNCCLDataType(entry.tensor), + recv_rank, nccl_ctx_.nccl_comm, nccl_ctx_.stream)); + } + for (int send_rank : mpi_ctx_.neighbor_out_ranks_) { + NCCLCHECK(ncclSend(sendbuf, num_elements, GetNCCLDataType(entry.tensor), + send_rank, nccl_ctx_.nccl_comm, nccl_ctx_.stream)); + } + } else { + for (size_t i = 0; i < entry.recv_neighbors->size(); ++i) { + int recv_rank = entry.recv_neighbors->at(i); + void* recvbuf = (void*)(static_cast(entry.output->data()) + + num_elements * i * element_size); + NCCLCHECK(ncclRecv(recvbuf, num_elements, GetNCCLDataType(entry.tensor), + recv_rank, nccl_ctx_.nccl_comm, nccl_ctx_.stream)); + } + for (int send_rank : *entry.send_neighbors) { + NCCLCHECK(ncclSend(sendbuf, num_elements, GetNCCLDataType(entry.tensor), + send_rank, nccl_ctx_.nccl_comm, nccl_ctx_.stream)); + } } + ncclGroupEnd(); } else { - for (size_t i = 0; i < entry.recv_neighbors->size(); ++i) { - int recv_rank = entry.recv_neighbors->at(i); - void* recvbuf = (void*)(static_cast(entry.output->data()) + - num_elements * i * element_size); - NCCLCHECK(ncclRecv(recvbuf, num_elements, GetNCCLDataType(entry.tensor), - recv_rank, nccl_ctx_.nccl_comm, nccl_ctx_.stream)); + if (entry.send_neighbors->empty()) { + throw std::runtime_error( + "Under hierachical neighbor_allreduce, argument " + "send_machine_neighbors should " + "not be empty."); } - for (int send_rank : *entry.send_neighbors) { - NCCLCHECK(ncclSend(sendbuf, num_elements, GetNCCLDataType(entry.tensor), - send_rank, nccl_ctx_.nccl_comm, nccl_ctx_.stream)); + // 1. In-place allreduce for all local ranks. Note it is sum, so we need to + // divided by local size at call back stage. + NCCLCHECK(ncclAllReduce(sendbuf, (void*)sendbuf, num_elements, + GetNCCLDataType(entry.tensor), ncclSum, + nccl_ctx_.nccl_local_comm, nccl_ctx_.stream)); + // 2. Local_rank = 0 do the neighbor all with other machines local_rank=0. + if (mpi_ctx_.local_rank_ == 0) { + ncclGroupStart(); + for (size_t i = 0; i < entry.recv_neighbors->size(); ++i) { + int recv_rank = entry.recv_neighbors->at(i); + void* recvbuf = (void*)(static_cast(entry.output->data()) + + num_elements * i * element_size); + NCCLCHECK(ncclRecv(recvbuf, num_elements, GetNCCLDataType(entry.tensor), + recv_rank, nccl_ctx_.nccl_comm, nccl_ctx_.stream)); + } + for (int send_rank : *entry.send_neighbors) { + NCCLCHECK(ncclSend(sendbuf, num_elements, GetNCCLDataType(entry.tensor), + send_rank, nccl_ctx_.nccl_comm, nccl_ctx_.stream)); + } + ncclGroupEnd(); + } else { + // No need to do anything } + // 3. Broadcast recv data from local rank = 0 to other local ranks. + int recv_num_elements = num_elements * entry.recv_neighbors->size(); + NCCLCHECK(ncclBroadcast(entry.output->data(), (void*)entry.output->data(), + recv_num_elements, GetNCCLDataType(entry.output), 0, + nccl_ctx_.nccl_local_comm, nccl_ctx_.stream)); } - ncclGroupEnd(); if (timeline_ptr_->Initialized()) { RecordEvent(event_queue, "COMM. (NCCL)"); @@ -721,7 +758,9 @@ void NCCLController::NeighborAllreduce(TensorTableEntry& entry) { "which doesn't support point-to-point communication. " "Hence, the performance may be largely degraded"; }); - + if (entry.is_hierarchical) { + throw std::runtime_error("Hierachical neighbor allreduce is not supported under NCCL < 2.7"); + } ncclGroupStart(); uint recv_rank_index = 0; uint send_rank_index = 0; @@ -890,37 +929,78 @@ void NCCLController::NeighborAllreduce(std::vector& entries) { // Hence, we need to offset the buffer data to location for neighbors. buffer_data = (uint8_t*)buffer_data + num_elements * element_size; - ncclGroupStart(); - if (first_entry.send_neighbors->empty()) { - for (int i = 0; i < mpi_ctx_.neighbor_indgree_; i++) { - int recv_rank = mpi_ctx_.neighbor_in_ranks_[i]; - void* recvbuf = - (void*)((uint8_t*)buffer_data + num_elements * i * element_size); - NCCLCHECK(ncclRecv(recvbuf, num_elements, - GetNCCLDataType(first_entry.tensor), recv_rank, - nccl_ctx_.nccl_comm, nccl_ctx_.stream)); - } - for (int send_rank : mpi_ctx_.neighbor_out_ranks_) { - NCCLCHECK(ncclSend(fused_input_data, num_elements, - GetNCCLDataType(first_entry.tensor), send_rank, - nccl_ctx_.nccl_comm, nccl_ctx_.stream)); + if (!first_entry.is_hierarchical) { + ncclGroupStart(); + if (first_entry.send_neighbors->empty()) { + for (int i = 0; i < mpi_ctx_.neighbor_indgree_; i++) { + int recv_rank = mpi_ctx_.neighbor_in_ranks_[i]; + void* recvbuf = + (void*)((uint8_t*)buffer_data + num_elements * i * element_size); + NCCLCHECK(ncclRecv(recvbuf, num_elements, + GetNCCLDataType(first_entry.tensor), recv_rank, + nccl_ctx_.nccl_comm, nccl_ctx_.stream)); + } + for (int send_rank : mpi_ctx_.neighbor_out_ranks_) { + NCCLCHECK(ncclSend(fused_input_data, num_elements, + GetNCCLDataType(first_entry.tensor), send_rank, + nccl_ctx_.nccl_comm, nccl_ctx_.stream)); + } + } else { + for (size_t i = 0; i < first_entry.recv_neighbors->size(); ++i) { + int recv_rank = first_entry.recv_neighbors->at(i); + void* recvbuf = + (void*)((uint8_t*)buffer_data + num_elements * i * element_size); + NCCLCHECK(ncclRecv(recvbuf, num_elements, + GetNCCLDataType(first_entry.tensor), recv_rank, + nccl_ctx_.nccl_comm, nccl_ctx_.stream)); + } + for (int send_rank : *first_entry.send_neighbors) { + NCCLCHECK(ncclSend(fused_input_data, num_elements, + GetNCCLDataType(first_entry.tensor), send_rank, + nccl_ctx_.nccl_comm, nccl_ctx_.stream)); + } } + ncclGroupEnd(); } else { - for (size_t i = 0; i < first_entry.recv_neighbors->size(); ++i) { - int recv_rank = first_entry.recv_neighbors->at(i); - void* recvbuf = - (void*)((uint8_t*)buffer_data + num_elements * i * element_size); - NCCLCHECK(ncclRecv(recvbuf, num_elements, - GetNCCLDataType(first_entry.tensor), recv_rank, - nccl_ctx_.nccl_comm, nccl_ctx_.stream)); + if (first_entry.send_neighbors->empty()) { + throw std::runtime_error( + "Under hierachical neighbor_allreduce, argument " + "send_machine_neighbors should not be empty."); } - for (int send_rank : *first_entry.send_neighbors) { - NCCLCHECK(ncclSend(fused_input_data, num_elements, - GetNCCLDataType(first_entry.tensor), send_rank, - nccl_ctx_.nccl_comm, nccl_ctx_.stream)); + + // 1. In-place allreduce for all local ranks. Note it is sum, so we need to + // divided by local size at call back stage. + NCCLCHECK(ncclAllReduce(fused_input_data, (void*)fused_input_data, + num_elements, GetNCCLDataType(first_entry.tensor), + ncclSum, nccl_ctx_.nccl_local_comm, + nccl_ctx_.stream)); + // 2. Local_rank = 0 do the neighbor all with other machines local_rank=0. + if (mpi_ctx_.local_rank_ == 0) { + ncclGroupStart(); + for (size_t i = 0; i < first_entry.recv_neighbors->size(); ++i) { + int recv_rank = first_entry.recv_neighbors->at(i); + void* recvbuf = + (void*)((uint8_t*)buffer_data + num_elements * i * element_size); + NCCLCHECK(ncclRecv(recvbuf, num_elements, + GetNCCLDataType(first_entry.tensor), recv_rank, + nccl_ctx_.nccl_comm, nccl_ctx_.stream)); + } + for (int send_rank : *first_entry.send_neighbors) { + NCCLCHECK(ncclSend(fused_input_data, num_elements, + GetNCCLDataType(first_entry.tensor), send_rank, + nccl_ctx_.nccl_comm, nccl_ctx_.stream)); + } + ncclGroupEnd(); + } else { + // No need to do anything } + // 3. Broadcast recv data from local rank = 0 to other local ranks. + int recv_num_elements = num_elements * first_entry.recv_neighbors->size(); + NCCLCHECK(ncclBroadcast(buffer_data, (void*)buffer_data, recv_num_elements, + GetNCCLDataType(first_entry.output), 0, + nccl_ctx_.nccl_local_comm, nccl_ctx_.stream)); } - ncclGroupEnd(); + if (timeline_ptr_->Initialized()) { RecordEvent(event_queue, "COMM. (NCCL)"); } diff --git a/bluefog/torch/mpi_ops.cc b/bluefog/torch/mpi_ops.cc index 07ea7266..8c1d283b 100644 --- a/bluefog/torch/mpi_ops.cc +++ b/bluefog/torch/mpi_ops.cc @@ -36,6 +36,7 @@ using ::bluefog::common::bluefog_load_topology_weights; using ::bluefog::common::bluefog_neighbor_size; using ::bluefog::common::bluefog_rank; using ::bluefog::common::bluefog_size; +using ::bluefog::common::bluefog_local_size; using ::bluefog::common::with_device; using ::bluefog::common::GetBluefogTimeline; using ::bluefog::common::Status; @@ -344,7 +345,7 @@ int DoNeighborAllreduce(::torch::Tensor tensor, ::torch::Tensor output, CPU_DEVICE_ID, callback_wrapper([self_weight, neighbor_weights, avg_computation, cpu_output, tensor, recv_neighbors, send_neighbors, - output, device]() mutable { + is_hierarchical, output, device]() mutable { with_device device_guard(device); output.copy_(cpu_output); int recv_size = bluefog_neighbor_size(); @@ -402,6 +403,10 @@ int DoNeighborAllreduce(::torch::Tensor tensor, ::torch::Tensor output, } output_buffer.resize_(shape_vector); output_buffer.add_(tensor_buffer.mul(self_weight)); + if (is_hierarchical){ + // Because there is ncclAllreduce just take sum. + output_buffer.div_(bluefog_local_size()); + } } else { int neighbor_size = send_neighbors.empty() ? bluefog_neighbor_size() @@ -416,7 +421,12 @@ int DoNeighborAllreduce(::torch::Tensor tensor, ::torch::Tensor output, } // Include self data as well. output_buffer.add_(tensor_buffer); - output_buffer.div_(neighbor_size + 1); + if (is_hierarchical){ + // Because there is ncclAllreduce just take sum. + output_buffer.div_(bluefog_local_size() * (neighbor_size + 1)); + } else { + output_buffer.div_(neighbor_size + 1); + } } output.resize_(shape_vector); MaybeCopyBufferBack(output, output_buffer); @@ -433,11 +443,13 @@ int DoNeighborAllreduce(::torch::Tensor tensor, ::torch::Tensor output, auto ready_event = RecordReadyEvent(device); auto enqueue_result = EnqueueTensorNeighborAllreduce( - bf_tensor, bf_output, bf_context, ready_event, bf_recv_neighbors, bf_send_neighbors, - is_hierarchical, enable_topo_check, op_name, device, callback_wrapper([self_weight, neighbor_weights, - avg_computation, recv_neighbors, send_neighbors, tensor, output] () mutable { + bf_tensor, bf_output, bf_context, ready_event, bf_recv_neighbors, + bf_send_neighbors, is_hierarchical, enable_topo_check, op_name, device, + callback_wrapper([self_weight, neighbor_weights, avg_computation, + recv_neighbors, send_neighbors, is_hierarchical, + tensor, output]() mutable { int recv_size = bluefog_neighbor_size(); - if(!send_neighbors.empty()) recv_size = recv_neighbors.size(); + if (!send_neighbors.empty()) recv_size = recv_neighbors.size(); if (recv_size > 0) { ::torch::Tensor output_buffer = MaybeCopyToTensorBuffer(output); ::torch::Tensor tensor_buffer = MaybeCopyToTensorBuffer(tensor); @@ -481,6 +493,10 @@ int DoNeighborAllreduce(::torch::Tensor tensor, ::torch::Tensor output, } output_buffer.resize_(shape_vector); output_buffer.add_(tensor_buffer.mul(self_weight)); + if (is_hierarchical){ + // Because there is ncclAllreduce just take sum. + output_buffer.div_(bluefog_local_size()); + } } else { int neighbor_size = send_neighbors.empty() ? bluefog_neighbor_size() @@ -495,7 +511,12 @@ int DoNeighborAllreduce(::torch::Tensor tensor, ::torch::Tensor output, output_buffer.resize_(shape_vector); // Include self data as well. output_buffer.add_(tensor_buffer); - output_buffer.div_(neighbor_size + 1); + if (is_hierarchical){ + // Because there is ncclAllreduce just take sum. + output_buffer.div_(bluefog_local_size() * (neighbor_size + 1)); + } else { + output_buffer.div_(neighbor_size + 1); + } } output.resize_(shape_vector); MaybeCopyBufferBack(output, output_buffer); From 1e61f3c924a3de20c7c50c8ebc37f3174e05d87e Mon Sep 17 00:00:00 2001 From: ybc Date: Thu, 22 Oct 2020 23:30:14 -0700 Subject: [PATCH 05/44] export hierachical_neighbor_allreduce --- bluefog/torch/__init__.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/bluefog/torch/__init__.py b/bluefog/torch/__init__.py index 2c813909..028396ab 100644 --- a/bluefog/torch/__init__.py +++ b/bluefog/torch/__init__.py @@ -47,6 +47,8 @@ from bluefog.torch.mpi_ops import broadcast_, broadcast_nonblocking_ from bluefog.torch.mpi_ops import neighbor_allgather, neighbor_allgather_nonblocking from bluefog.torch.mpi_ops import neighbor_allreduce, neighbor_allreduce_nonblocking +from bluefog.torch.mpi_ops import hierachical_neighbor_allreduce +from bluefog.torch.mpi_ops import hierachical_neighbor_allreduce_nonblocking from bluefog.torch.mpi_ops import pair_gossip, pair_gossip_nonblocking from bluefog.torch.mpi_ops import poll, synchronize, barrier From aafdf11521b74330d791ff80cad5147747c62697 Mon Sep 17 00:00:00 2001 From: ybc Date: Thu, 22 Oct 2020 23:41:25 -0700 Subject: [PATCH 06/44] Update mpi_ops.py --- bluefog/torch/mpi_ops.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/bluefog/torch/mpi_ops.py b/bluefog/torch/mpi_ops.py index a600f4c0..56ab667a 100644 --- a/bluefog/torch/mpi_ops.py +++ b/bluefog/torch/mpi_ops.py @@ -527,8 +527,10 @@ def hierachical_neighbor_allreduce(tensor: torch.Tensor, self_weight: float, neighbor_machine_weights: Dict[int, float], send_neighbor_machines: List[int], - enable_topo_check: bool = True, + enable_topo_check: bool = False, name: Optional[str] = None) -> torch.Tensor: + # TODO(hhb) Implement the logics for topo check for hierachical_neighbor_allreduce. + # TODO(ybc) add check for self_weight and neighbor_machine_weights. if (self_weight is None and neighbor_machine_weights is not None) or \ (self_weight is not None and send_neighbor_machines is None): @@ -544,7 +546,7 @@ def hierachical_neighbor_allreduce_nonblocking(tensor: torch.Tensor, self_weight: float, neighbor_machine_weights: Dict[int, float], send_neighbor_machines: List[int], - enable_topo_check: bool = True, + enable_topo_check: bool = False, name: Optional[str] = None) -> int: if (self_weight is None or neighbor_machine_weights is None): raise ValueError("Arguments self_weight and neighbor_weights cannot be empty or None.") @@ -565,7 +567,7 @@ def hierachical_neighbor_allreduce_nonblocking(tensor: torch.Tensor, def _hierachical_neighbor_allreduce_nonblocking( tensor, output, self_weight, neighbor_machine_weights, send_neighbor_machines, enable_topo_check, name): - assert not is_homogeneous, \ + assert is_homogeneous, \ "hierachical_neighbor_allreduce should be used under homogeneous environment only" function = _check_function(_neighbor_allreduce_function_factory, tensor) if self_weight is not None and neighbor_machine_weights is not None: From d7f1d014865409cc93a24353179c886d3dfdc005 Mon Sep 17 00:00:00 2001 From: ybc Date: Thu, 22 Oct 2020 23:48:52 -0700 Subject: [PATCH 07/44] remove extra avg_computation in hierachical nar --- bluefog/torch/mpi_ops.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/bluefog/torch/mpi_ops.py b/bluefog/torch/mpi_ops.py index 56ab667a..ba8294ff 100644 --- a/bluefog/torch/mpi_ops.py +++ b/bluefog/torch/mpi_ops.py @@ -411,10 +411,10 @@ def _neighbor_allreduce_nonblocking(tensor, output, self_weight, neighbor_weight else: raise ValueError("Arguments self_weight and neighbor_weights have to be presented at " "the same time") - + is_hierachical = False handle = getattr(mpi_lib, function)(tensor, output, self_weight, neighbor_weights, send_neighbors, enable_topo_check, avg_computation, - False, name.encode() if name is not None else "") + is_hierachical, name.encode() if name is not None else "") _handle_map[handle] = (tensor, output) return handle @@ -587,16 +587,15 @@ def _hierachical_neighbor_allreduce_nonblocking( break else: raise ValueError("Arguments self_weight and neighbor_weights cannot be empty or None.") - # Translate machine id into rank id. node_per_machine = local_size() neighbor_weights = { node_per_machine*m: weights for (m, weights) in neighbor_machine_weights.items()} send_neighbors = [node_per_machine*m for m in send_neighbor_machines] - avg_computation = False + is_hierachical = True handle = getattr(mpi_lib, function)(tensor, output, self_weight, neighbor_weights, send_neighbors, enable_topo_check, avg_computation, - True, name.encode() if name is not None else "") + is_hierachical, name.encode() if name is not None else "") _handle_map[handle] = (tensor, output) return handle From b4dd476d43ebc0103acc2ba39eb75160f75bbb45 Mon Sep 17 00:00:00 2001 From: ybc Date: Fri, 23 Oct 2020 00:02:03 -0700 Subject: [PATCH 08/44] fix the typo of hierarchical --- bluefog/common/mpi_controller.cc | 4 +-- bluefog/common/nccl_controller.cc | 6 ++--- bluefog/torch/__init__.py | 4 +-- bluefog/torch/mpi_ops.py | 42 +++++++++++++++---------------- 4 files changed, 28 insertions(+), 28 deletions(-) diff --git a/bluefog/common/mpi_controller.cc b/bluefog/common/mpi_controller.cc index efc2a215..3757854d 100644 --- a/bluefog/common/mpi_controller.cc +++ b/bluefog/common/mpi_controller.cc @@ -362,7 +362,7 @@ void MPIController::NeighborAllreduce(TensorTableEntry& entry) { // TODO(ybc) support this if (entry.is_hierarchical) { throw std::runtime_error( - "Hierachical neighbor_allreduce is only supported under NCCL case."); + "hierarchical neighbor_allreduce is only supported under NCCL case."); } Timeline* timeline_ptr; @@ -492,7 +492,7 @@ void MPIController::NeighborAllreduce(std::vector& entries) { // TODO(ybc) support this if (first_entry.is_hierarchical) { throw std::runtime_error( - "Hierachical neighbor_allreduce is only supported under NCCL case."); + "hierarchical neighbor_allreduce is only supported under NCCL case."); } void* buffer_data; diff --git a/bluefog/common/nccl_controller.cc b/bluefog/common/nccl_controller.cc index 8180847d..5b3b5860 100644 --- a/bluefog/common/nccl_controller.cc +++ b/bluefog/common/nccl_controller.cc @@ -701,7 +701,7 @@ void NCCLController::NeighborAllreduce(TensorTableEntry& entry) { } else { if (entry.send_neighbors->empty()) { throw std::runtime_error( - "Under hierachical neighbor_allreduce, argument " + "Under hierarchical neighbor_allreduce, argument " "send_machine_neighbors should " "not be empty."); } @@ -759,7 +759,7 @@ void NCCLController::NeighborAllreduce(TensorTableEntry& entry) { "Hence, the performance may be largely degraded"; }); if (entry.is_hierarchical) { - throw std::runtime_error("Hierachical neighbor allreduce is not supported under NCCL < 2.7"); + throw std::runtime_error("hierarchical neighbor allreduce is not supported under NCCL < 2.7"); } ncclGroupStart(); uint recv_rank_index = 0; @@ -964,7 +964,7 @@ void NCCLController::NeighborAllreduce(std::vector& entries) { } else { if (first_entry.send_neighbors->empty()) { throw std::runtime_error( - "Under hierachical neighbor_allreduce, argument " + "Under hierarchical neighbor_allreduce, argument " "send_machine_neighbors should not be empty."); } diff --git a/bluefog/torch/__init__.py b/bluefog/torch/__init__.py index 028396ab..b7596b51 100644 --- a/bluefog/torch/__init__.py +++ b/bluefog/torch/__init__.py @@ -47,8 +47,8 @@ from bluefog.torch.mpi_ops import broadcast_, broadcast_nonblocking_ from bluefog.torch.mpi_ops import neighbor_allgather, neighbor_allgather_nonblocking from bluefog.torch.mpi_ops import neighbor_allreduce, neighbor_allreduce_nonblocking -from bluefog.torch.mpi_ops import hierachical_neighbor_allreduce -from bluefog.torch.mpi_ops import hierachical_neighbor_allreduce_nonblocking +from bluefog.torch.mpi_ops import hierarchical_neighbor_allreduce +from bluefog.torch.mpi_ops import hierarchical_neighbor_allreduce_nonblocking from bluefog.torch.mpi_ops import pair_gossip, pair_gossip_nonblocking from bluefog.torch.mpi_ops import poll, synchronize, barrier diff --git a/bluefog/torch/mpi_ops.py b/bluefog/torch/mpi_ops.py index ba8294ff..757e433b 100644 --- a/bluefog/torch/mpi_ops.py +++ b/bluefog/torch/mpi_ops.py @@ -411,10 +411,10 @@ def _neighbor_allreduce_nonblocking(tensor, output, self_weight, neighbor_weight else: raise ValueError("Arguments self_weight and neighbor_weights have to be presented at " "the same time") - is_hierachical = False + is_hierarchical = False handle = getattr(mpi_lib, function)(tensor, output, self_weight, neighbor_weights, send_neighbors, enable_topo_check, avg_computation, - is_hierachical, name.encode() if name is not None else "") + is_hierarchical, name.encode() if name is not None else "") _handle_map[handle] = (tensor, output) return handle @@ -523,31 +523,31 @@ def neighbor_allreduce_nonblocking(tensor: torch.Tensor, send_neighbors, enable_topo_check, name=name) -def hierachical_neighbor_allreduce(tensor: torch.Tensor, - self_weight: float, - neighbor_machine_weights: Dict[int, float], - send_neighbor_machines: List[int], - enable_topo_check: bool = False, - name: Optional[str] = None) -> torch.Tensor: - # TODO(hhb) Implement the logics for topo check for hierachical_neighbor_allreduce. +def hierarchical_neighbor_allreduce(tensor: torch.Tensor, + self_weight: float, + neighbor_machine_weights: Dict[int, float], + send_neighbor_machines: List[int], + enable_topo_check: bool = False, + name: Optional[str] = None) -> torch.Tensor: + # TODO(hhb) Implement the logics for topo check for hierarchical_neighbor_allreduce. # TODO(ybc) add check for self_weight and neighbor_machine_weights. if (self_weight is None and neighbor_machine_weights is not None) or \ (self_weight is not None and send_neighbor_machines is None): raise ValueError("Arguments self_weight and neighbor_machine_weights have to " "be presented at the same time") - handle = hierachical_neighbor_allreduce_nonblocking( + handle = hierarchical_neighbor_allreduce_nonblocking( tensor, self_weight, neighbor_machine_weights, send_neighbor_machines, enable_topo_check, name) return synchronize(handle) -def hierachical_neighbor_allreduce_nonblocking(tensor: torch.Tensor, - self_weight: float, - neighbor_machine_weights: Dict[int, float], - send_neighbor_machines: List[int], - enable_topo_check: bool = False, - name: Optional[str] = None) -> int: +def hierarchical_neighbor_allreduce_nonblocking(tensor: torch.Tensor, + self_weight: float, + neighbor_machine_weights: Dict[int, float], + send_neighbor_machines: List[int], + enable_topo_check: bool = False, + name: Optional[str] = None) -> int: if (self_weight is None or neighbor_machine_weights is None): raise ValueError("Arguments self_weight and neighbor_weights cannot be empty or None.") if (self_weight is None and neighbor_machine_weights is not None) or \ @@ -559,16 +559,16 @@ def hierachical_neighbor_allreduce_nonblocking(tensor: torch.Tensor, new_shape = torch.Size([first_dim] + list(tensor.shape[1:])) output = tensor.new(new_shape) # Pre-allocate the memory for the output. - return _hierachical_neighbor_allreduce_nonblocking( + return _hierarchical_neighbor_allreduce_nonblocking( tensor, output, self_weight, neighbor_machine_weights, send_neighbor_machines, enable_topo_check, name=name) -def _hierachical_neighbor_allreduce_nonblocking( +def _hierarchical_neighbor_allreduce_nonblocking( tensor, output, self_weight, neighbor_machine_weights, send_neighbor_machines, enable_topo_check, name): assert is_homogeneous, \ - "hierachical_neighbor_allreduce should be used under homogeneous environment only" + "hierarchical_neighbor_allreduce should be used under homogeneous environment only" function = _check_function(_neighbor_allreduce_function_factory, tensor) if self_weight is not None and neighbor_machine_weights is not None: if not isinstance(neighbor_machine_weights, dict): @@ -592,10 +592,10 @@ def _hierachical_neighbor_allreduce_nonblocking( neighbor_weights = { node_per_machine*m: weights for (m, weights) in neighbor_machine_weights.items()} send_neighbors = [node_per_machine*m for m in send_neighbor_machines] - is_hierachical = True + is_hierarchical = True handle = getattr(mpi_lib, function)(tensor, output, self_weight, neighbor_weights, send_neighbors, enable_topo_check, avg_computation, - is_hierachical, name.encode() if name is not None else "") + is_hierarchical, name.encode() if name is not None else "") _handle_map[handle] = (tensor, output) return handle From 85d9e971e1440a82e978b1eceb123d0f85a768a8 Mon Sep 17 00:00:00 2001 From: ybc Date: Fri, 23 Oct 2020 00:05:32 -0700 Subject: [PATCH 09/44] build DistributedHierarchicalNeighborAllreduceOptimizer --- bluefog/torch/__init__.py | 1 + bluefog/torch/optimizers.py | 35 ++++++++++++++++++++++++++++++++++- 2 files changed, 35 insertions(+), 1 deletion(-) diff --git a/bluefog/torch/__init__.py b/bluefog/torch/__init__.py index b7596b51..f5f2e8e0 100644 --- a/bluefog/torch/__init__.py +++ b/bluefog/torch/__init__.py @@ -26,6 +26,7 @@ DistributedGradientAllreduceOptimizer, DistributedAllreduceOptimizer, DistributedNeighborAllreduceOptimizer, + DistributedHierarchicalNeighborAllreduceOptimizer, DistributedPushSumOptimizer, DistributedPullGetOptimizer, DistributedBluefogOptimizer) diff --git a/bluefog/torch/optimizers.py b/bluefog/torch/optimizers.py index 210262cc..66f12477 100644 --- a/bluefog/torch/optimizers.py +++ b/bluefog/torch/optimizers.py @@ -295,10 +295,14 @@ def __init__(self, params, model, reduce_type, num_steps_per_communication=1): super(self.__class__, self).__init__(params) named_parameters, models = _check_named_parameters(self, model) - self.neighbor_weights = None + # knobs for neighbor communication behavior self.self_weight = None + self.neighbor_weights = None self.send_neighbors = None + self.neighbor_machine_weights = None + self.send_neighbor_machines = None self.enable_topo_check = False + self._models = models self._parameter_names = {v: k for k, v in sorted(named_parameters)} self._handles = {} @@ -314,6 +318,8 @@ def __init__(self, params, model, reduce_type, num_steps_per_communication=1): self._reduce_method = 0 elif self._reduce_type_str == "neighbor.allreduce": self._reduce_method = 1 + elif self._reduce_type_str == "hierarchical.neighbor.allreduce": + self._reduce_method = 2 else: raise ValueError("Unknown reduce type for internal class _DistributedReduceOptimizer") @@ -352,6 +358,8 @@ def hook(module, *unused): handle = self._allreduce_data_async(p) elif self._reduce_method == 1: handle = self._neighbor_allreduce_data_async(p) + elif self._reduce_method == 2: + handle = self._hierarchical_neighbor_allreduce_data_async(p) else: raise ValueError( "Unknown reduce method. Do not change _reduce_method manually.") @@ -366,6 +374,15 @@ def _neighbor_allreduce_data_async(self, p): enable_topo_check=self.enable_topo_check) return handle + def _hierarchical_neighbor_allreduce_data_async(self, p): + name = self._parameter_names.get(p) + handle = bf.hierarchical_neighbor_allreduce_nonblocking( + p.data, name=name, self_weight=self.self_weight, + neighbor_machine_weights=self.neighbor_machine_weights, + send_neighbor_machines=self.send_neighbor_machines, + enable_topo_check=self.enable_topo_check) + return handle + def _allreduce_data_async(self, p): name = self._parameter_names.get(p) handle = bf.allreduce_nonblocking(p.data, average=True, name=name) @@ -389,6 +406,9 @@ def use_allreduce_in_communication(self): def use_neighbor_allreduce_in_communication(self): self._reduce_method = 1 + def use_hierarchical_neighbor_allreduce_in_communication(self): + self._reduce_method = 2 + def synchronize(self): with torch.no_grad(): for p, handle in self._handles.items(): @@ -791,6 +811,7 @@ def DistributedPushSumOptimizer(optimizer, model, ) return cls(optimizer.param_groups, model, num_steps_per_communication) + def DistributedPullGetOptimizer(optimizer, model, num_steps_per_communication=1): """ @@ -890,6 +911,7 @@ def DistributedBluefogOptimizer(optimizer, model, ) return cls(optimizer.param_groups, model, num_steps_per_communication, pull_style=False) + def DistributedAllreduceOptimizer(optimizer, model, num_steps_per_communication=1): """ @@ -984,6 +1006,17 @@ def DistributedNeighborAllreduceOptimizer(optimizer, model, return cls(optimizer.param_groups, model, "neighbor.allreduce", num_steps_per_communication) +def DistributedHierarchicalNeighborAllreduceOptimizer(optimizer, model, + num_steps_per_communication=1): + cls = type( + optimizer.__class__.__name__, + (optimizer.__class__,), + dict(_DistributedReduceOptimizer.__dict__), + ) + return cls(optimizer.param_groups, model, "hierarchical.neighbor.allreduce", + num_steps_per_communication) + + def DistributedGradientAllreduceOptimizer(optimizer, model, backward_passes_per_step=1): """ From f80cabf9bb0ae5d90690dcb63d9c2dd5e65801b5 Mon Sep 17 00:00:00 2001 From: ybc Date: Fri, 23 Oct 2020 17:40:13 -0700 Subject: [PATCH 10/44] Add GetExp2DynamicSendRecvMachineRanks --- bluefog/common/topology_util.py | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/bluefog/common/topology_util.py b/bluefog/common/topology_util.py index 08be77ba..4997afeb 100644 --- a/bluefog/common/topology_util.py +++ b/bluefog/common/topology_util.py @@ -430,6 +430,25 @@ def GetDynamicSendRecvRanks( index += 1 +def GetExp2DynamicSendRecvMachineRanks( + world_size: int, local_size: int, self_rank: int, local_rank: int + ) -> Iterator[Tuple[List[int], List[int]]]: + assert (self_rank % local_size) == local_rank, \ + "It should be used under homogeneous environment only." + assert (world_size % local_size) == 0, \ + "It should be used under homogeneous environment only." + machine_id = self_rank // local_size + machine_size = world_size // local_size + exp_2_size = int(np.log2(machine_size-1)) + index = 0 + while True: + machine_dist = 2**(index % (exp_2_size + 1)) + send_machine_rank = (machine_id + machine_dist) % machine_size + recv_machine_ranks = (machine_id - machine_dist) % machine_size + yield [send_machine_rank], [recv_machine_ranks] + index += 1 + + def GetInnerOuterRingDynamicSendRecvRanks( world_size: int, local_size: int, self_rank: int ) -> Iterator[Tuple[List[int], List[int]]]: From bb9fa0056946f1381bb06af831ec14de3d92c80b Mon Sep 17 00:00:00 2001 From: ybc Date: Fri, 23 Oct 2020 17:54:43 -0700 Subject: [PATCH 11/44] Add hierarchical_neighbor_allreduce --- examples/pytorch_cifar10_resnet.py | 21 +++++++++++++++++++-- 1 file changed, 19 insertions(+), 2 deletions(-) diff --git a/examples/pytorch_cifar10_resnet.py b/examples/pytorch_cifar10_resnet.py index 014b948b..adb18919 100644 --- a/examples/pytorch_cifar10_resnet.py +++ b/examples/pytorch_cifar10_resnet.py @@ -74,9 +74,9 @@ "--no-cuda", action="store_true", default=False, help="disables CUDA training" ) parser.add_argument("--seed", type=int, default=42, help="random seed") -parser.add_argument('--dist-optimizer', type=str, default='win_put', +parser.add_argument('--dist-optimizer', type=str, default='hierarchical_neighbor_allreduce', help='The type of distributed optimizer. Supporting options are ' + - '[win_put, neighbor_allreduce, allreduce, pull_get, push_sum, horovod]') + '[win_put, neighbor_allreduce, allreduce, pull_get, hierarchical_neighbor_allreduce, horovod]') parser.add_argument("--average-test-result", action="store_true", default=False, help=("Allreduce called to average test result. Warning this will " + @@ -226,6 +226,9 @@ elif args.dist_optimizer == 'gradient_allreduce': optimizer = optimizer = bf.DistributedGradientAllreduceOptimizer( optimizer, model=model) +elif args.dist_optimizer == 'hierarchical_neighbor_allreduce': + optimizer = optimizer = bf.DistributedHierarchicalNeighborAllreduceOptimizer( + optimizer, model=model) elif args.dist_optimizer == 'push_sum': optimizer = bf.DistributedPushSumOptimizer(optimizer, model=model) elif args.dist_optimizer == 'horovod': @@ -356,6 +359,14 @@ def adjust_learning_rate(epoch, batch_idx): bf.size(), local_size=bf.local_size() if args.local_size == -1 else args.local_size, self_rank=bf.rank()) + elif args.dist_optimizer == 'hierarchical_neighbor_allreduce': + # This optimizer can use only, + dynamic_machine_neighbor_allreduce_gen = topology_util.GetExp2DynamicSendRecvMachineRanks( + world_size=bf.size(), + local_size=bf.local_size(), + self_rank=bf.rank(), + local_rank=bf.local_rank() + ) else: dynamic_neighbor_allreduce_gen = topology_util.GetDynamicSendRecvRanks( bf.load_topology(), bf.rank()) @@ -386,6 +397,12 @@ def dynamic_topology_update(epoch, batch_idx): optimizer.neighbor_weights = {r: 1/(len(recv_neighbors) + 1) for r in recv_neighbors} optimizer.self_weight = 1 / (len(recv_neighbors) + 1) optimizer.enable_topo_check = False + elif args.dist_optimizer == 'hierarchical_neighbor_allreduce': + send_machines, recv_machines = next(dynamic_machine_neighbor_allreduce_gen) + optimizer.send_neighbor_machines = send_machines + optimizer.neighbor_machine_weights = {r: 1/(len(recv_neighbors) + 1) for r in recv_neighbors} + optimizer.self_weight = 1 / (len(recv_neighbors) + 1) + optimizer.enable_topo_check = False else: pass From aa53d06c9bae7289c5c30e13beb79cab4a8988ce Mon Sep 17 00:00:00 2001 From: ybc Date: Fri, 23 Oct 2020 18:02:43 -0700 Subject: [PATCH 12/44] Update pytorch_cifar10_resnet.py --- examples/pytorch_cifar10_resnet.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/pytorch_cifar10_resnet.py b/examples/pytorch_cifar10_resnet.py index adb18919..094bdae7 100644 --- a/examples/pytorch_cifar10_resnet.py +++ b/examples/pytorch_cifar10_resnet.py @@ -400,8 +400,8 @@ def dynamic_topology_update(epoch, batch_idx): elif args.dist_optimizer == 'hierarchical_neighbor_allreduce': send_machines, recv_machines = next(dynamic_machine_neighbor_allreduce_gen) optimizer.send_neighbor_machines = send_machines - optimizer.neighbor_machine_weights = {r: 1/(len(recv_neighbors) + 1) for r in recv_neighbors} - optimizer.self_weight = 1 / (len(recv_neighbors) + 1) + optimizer.neighbor_machine_weights = {r: 1/(len(recv_machines) + 1) for r in recv_machines} + optimizer.self_weight = 1 / (len(recv_machines) + 1) optimizer.enable_topo_check = False else: pass From 869f609273e37fb25406cb119310bda8914fb521 Mon Sep 17 00:00:00 2001 From: ybc Date: Fri, 23 Oct 2020 19:11:27 -0700 Subject: [PATCH 13/44] Copy input for hier nar due to in-place allreduce --- bluefog/torch/mpi_ops.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/bluefog/torch/mpi_ops.py b/bluefog/torch/mpi_ops.py index 757e433b..8e165766 100644 --- a/bluefog/torch/mpi_ops.py +++ b/bluefog/torch/mpi_ops.py @@ -592,11 +592,12 @@ def _hierarchical_neighbor_allreduce_nonblocking( neighbor_weights = { node_per_machine*m: weights for (m, weights) in neighbor_machine_weights.items()} send_neighbors = [node_per_machine*m for m in send_neighbor_machines] + tensor_buffer = tensor.detach().clone() is_hierarchical = True - handle = getattr(mpi_lib, function)(tensor, output, self_weight, neighbor_weights, + handle = getattr(mpi_lib, function)(tensor_buffer, output, self_weight, neighbor_weights, send_neighbors, enable_topo_check, avg_computation, is_hierarchical, name.encode() if name is not None else "") - _handle_map[handle] = (tensor, output) + _handle_map[handle] = (tensor_buffer, output) return handle From 2e04cbfad4da555d005e11667990d878e536659f Mon Sep 17 00:00:00 2001 From: ybc Date: Sat, 24 Oct 2020 15:34:55 -0700 Subject: [PATCH 14/44] Update pytorch_cifar10_resnet.py --- examples/pytorch_cifar10_resnet.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/examples/pytorch_cifar10_resnet.py b/examples/pytorch_cifar10_resnet.py index 094bdae7..1eb5a76b 100644 --- a/examples/pytorch_cifar10_resnet.py +++ b/examples/pytorch_cifar10_resnet.py @@ -112,11 +112,11 @@ elif args.virtual_topology == "InnerOuterRing": assert bf.is_homogeneous, "InnerOuterRing topo should be used only homogeneous environment" bf.set_topology(topology_util.InnerOuterRingGraph( - bf.size(), local_size=bf.local_size() if args.local_size == -1 else args.local_size)) + bf.size(), local_size=bf.local_size())) elif args.virtual_topology == "InnerOuterExp2": assert bf.is_homogeneous, "InnerOuterExp2 topo should be used under homogeneous environment" bf.set_topology(topology_util.InnerOuterExp2Graph( - bf.size(), local_size=bf.local_size() if args.local_size == -1 else args.local_size)) + bf.size(), local_size=bf.local_size())) else: raise ValueError("Unknown args.virtual_topology, supporting options are " + "[power2(Default), ring, mesh, star，InnerOuterRing， InnerOuterExp2].") @@ -352,12 +352,12 @@ def adjust_learning_rate(epoch, batch_idx): if args.virtual_topology == 'InnerOuterRing': dynamic_neighbor_allreduce_gen = topology_util.GetInnerOuterRingDynamicSendRecvRanks( bf.size(), - local_size=bf.local_size() if args.local_size == -1 else args.local_size, + local_size=bf.local_size(), self_rank=bf.rank()) elif args.virtual_topology == 'InnerOuterExp2': dynamic_neighbor_allreduce_gen = topology_util.GetInnerOuterExp2DynamicSendRecvRanks( bf.size(), - local_size=bf.local_size() if args.local_size == -1 else args.local_size, + local_size=bf.local_size(), self_rank=bf.rank()) elif args.dist_optimizer == 'hierarchical_neighbor_allreduce': # This optimizer can use only, From b88dde3e707d57a5ceb277169b49f0d3e01bb9a5 Mon Sep 17 00:00:00 2001 From: ybc Date: Sat, 24 Oct 2020 16:07:08 -0700 Subject: [PATCH 15/44] Copy fused input back to input for fused hier n_ar --- bluefog/common/nccl_controller.cc | 21 ++++++++++++++++++++- bluefog/common/nccl_controller.h | 3 +++ 2 files changed, 23 insertions(+), 1 deletion(-) diff --git a/bluefog/common/nccl_controller.cc b/bluefog/common/nccl_controller.cc index 5b3b5860..5561af50 100644 --- a/bluefog/common/nccl_controller.cc +++ b/bluefog/common/nccl_controller.cc @@ -991,8 +991,10 @@ void NCCLController::NeighborAllreduce(std::vector& entries) { nccl_ctx_.nccl_comm, nccl_ctx_.stream)); } ncclGroupEnd(); + MemcpyOutFusionBufferForInputs(fused_input_data, entries); } else { - // No need to do anything + // Because the in-place modification, we need to copy fused_input_data back to tensor as well + MemcpyOutFusionBufferForInputs(fused_input_data, entries); } // 3. Broadcast recv data from local rank = 0 to other local ranks. int recv_num_elements = num_elements * first_entry.recv_neighbors->size(); @@ -1004,6 +1006,7 @@ void NCCLController::NeighborAllreduce(std::vector& entries) { if (timeline_ptr_->Initialized()) { RecordEvent(event_queue, "COMM. (NCCL)"); } + // Remember buffer_data is already pointed at offset location (after self // tensor). int num_recv_neighbors = first_entry.send_neighbors->empty() @@ -1798,6 +1801,22 @@ void NCCLController::MemcpyOutFusionBufferForNeighbors( } } +void NCCLController::MemcpyOutFusionBufferForInputs( + const void* fused_input_data, std::vector& entries) { + // Copy the input data stored in the fusion buffer back to input, which is + // used in hierarchical neighbor allreduce since it has allreduce step to + // modified the input data. + int64_t offset = 0; + for (auto& e : entries) { + void* fused_input_data_at_offset = (uint8_t*)fused_input_data + offset; + void* dst_data = (void*)e.tensor->data(); + size_t count = (size_t)e.tensor->size(); + CUDACHECK(cudaMemcpyAsync(dst_data, fused_input_data_at_offset, count, + cudaMemcpyDeviceToDevice, nccl_ctx_.stream)); + offset += e.tensor->size(); + } +} + void NCCLController::MemcpyEntryInFusionBuffer(const TensorTableEntry& e, void* buffer_data_at_offset) { const void* src_data = e.tensor->data(); diff --git a/bluefog/common/nccl_controller.h b/bluefog/common/nccl_controller.h index 7060ebc4..3835a3d4 100644 --- a/bluefog/common/nccl_controller.h +++ b/bluefog/common/nccl_controller.h @@ -188,6 +188,9 @@ class NCCLController { const int num_recv_neighbors, const int64_t fused_data_size); + void MemcpyOutFusionBufferForInputs(const void* fused_input_data, + std::vector& entries); + void MemcpyEntryInFusionBuffer(const TensorTableEntry& e, void* buffer_data_at_offset); From 96214e5688c321be2c097b67bdee989780a66183 Mon Sep 17 00:00:00 2001 From: ybc Date: Sat, 24 Oct 2020 16:59:46 -0700 Subject: [PATCH 16/44] Use local rank 0 to recv and local rank 1 to send in h n_ar --- bluefog/common/nccl_controller.cc | 20 +++++++++++++++----- bluefog/torch/mpi_ops.py | 1 + 2 files changed, 16 insertions(+), 5 deletions(-) diff --git a/bluefog/common/nccl_controller.cc b/bluefog/common/nccl_controller.cc index 5561af50..72540998 100644 --- a/bluefog/common/nccl_controller.cc +++ b/bluefog/common/nccl_controller.cc @@ -967,6 +967,12 @@ void NCCLController::NeighborAllreduce(std::vector& entries) { "Under hierarchical neighbor_allreduce, argument " "send_machine_neighbors should not be empty."); } + if (mpi_ctx_.local_size_ < 2) { + throw std::runtime_error( + "Local size is smaller than 2, in this case, you should use " + "neighbor_allreduce instead of hierarchical_neighbor_allreduce." + ); + } // 1. In-place allreduce for all local ranks. Note it is sum, so we need to // divided by local size at call back stage. @@ -976,26 +982,30 @@ void NCCLController::NeighborAllreduce(std::vector& entries) { nccl_ctx_.stream)); // 2. Local_rank = 0 do the neighbor all with other machines local_rank=0. if (mpi_ctx_.local_rank_ == 0) { + // Use rank 0 for receiving ncclGroupStart(); - for (size_t i = 0; i < first_entry.recv_neighbors->size(); ++i) { + for (size_t i = 0; i < first_entry.recv_neighbors->size(); ++i) { int recv_rank = first_entry.recv_neighbors->at(i); void* recvbuf = (void*)((uint8_t*)buffer_data + num_elements * i * element_size); NCCLCHECK(ncclRecv(recvbuf, num_elements, - GetNCCLDataType(first_entry.tensor), recv_rank, + GetNCCLDataType(first_entry.tensor), recv_rank+1, nccl_ctx_.nccl_comm, nccl_ctx_.stream)); } + ncclGroupEnd(); + } else if (mpi_ctx_.local_rank_ == 1) { + ncclGroupStart(); for (int send_rank : *first_entry.send_neighbors) { NCCLCHECK(ncclSend(fused_input_data, num_elements, GetNCCLDataType(first_entry.tensor), send_rank, nccl_ctx_.nccl_comm, nccl_ctx_.stream)); } ncclGroupEnd(); - MemcpyOutFusionBufferForInputs(fused_input_data, entries); } else { - // Because the in-place modification, we need to copy fused_input_data back to tensor as well - MemcpyOutFusionBufferForInputs(fused_input_data, entries); + // Do nothing } + // Because the in-place modification, we need to copy fused_input_data back to tensor as well + MemcpyOutFusionBufferForInputs(fused_input_data, entries); // 3. Broadcast recv data from local rank = 0 to other local ranks. int recv_num_elements = num_elements * first_entry.recv_neighbors->size(); NCCLCHECK(ncclBroadcast(buffer_data, (void*)buffer_data, recv_num_elements, diff --git a/bluefog/torch/mpi_ops.py b/bluefog/torch/mpi_ops.py index 8e165766..81da35b3 100644 --- a/bluefog/torch/mpi_ops.py +++ b/bluefog/torch/mpi_ops.py @@ -569,6 +569,7 @@ def _hierarchical_neighbor_allreduce_nonblocking( send_neighbor_machines, enable_topo_check, name): assert is_homogeneous, \ "hierarchical_neighbor_allreduce should be used under homogeneous environment only" + assert local_size() > 1, "If local size is 1, you should use neighbor allreduce directorly." function = _check_function(_neighbor_allreduce_function_factory, tensor) if self_weight is not None and neighbor_machine_weights is not None: if not isinstance(neighbor_machine_weights, dict): From 95fd66cc2d69c9092f07a0f5cc85bec8e4406670 Mon Sep 17 00:00:00 2001 From: ybc Date: Sat, 24 Oct 2020 17:32:04 -0700 Subject: [PATCH 17/44] Add h n_ar to other examples and enable dynamic => disable args --- bluefog/common/nccl_controller.cc | 3 ++- examples/pytorch_benchmark.py | 28 +++++++++++++++++++++++----- examples/pytorch_cifar10_resnet.py | 12 ++++++------ examples/pytorch_mnist.py | 29 +++++++++++++++++++++++------ 4 files changed, 54 insertions(+), 18 deletions(-) diff --git a/bluefog/common/nccl_controller.cc b/bluefog/common/nccl_controller.cc index 72540998..f0e525fb 100644 --- a/bluefog/common/nccl_controller.cc +++ b/bluefog/common/nccl_controller.cc @@ -982,7 +982,7 @@ void NCCLController::NeighborAllreduce(std::vector& entries) { nccl_ctx_.stream)); // 2. Local_rank = 0 do the neighbor all with other machines local_rank=0. if (mpi_ctx_.local_rank_ == 0) { - // Use rank 0 for receiving + // Use local rank 0 for receiving ncclGroupStart(); for (size_t i = 0; i < first_entry.recv_neighbors->size(); ++i) { int recv_rank = first_entry.recv_neighbors->at(i); @@ -994,6 +994,7 @@ void NCCLController::NeighborAllreduce(std::vector& entries) { } ncclGroupEnd(); } else if (mpi_ctx_.local_rank_ == 1) { + // Use local rank 0 for sending ncclGroupStart(); for (int send_rank : *first_entry.send_neighbors) { NCCLCHECK(ncclSend(fused_input_data, num_elements, diff --git a/examples/pytorch_benchmark.py b/examples/pytorch_benchmark.py index 5a68c953..1681cf8b 100644 --- a/examples/pytorch_benchmark.py +++ b/examples/pytorch_benchmark.py @@ -55,9 +55,10 @@ help='partition size') parser.add_argument('--dist-optimizer', type=str, default='win_put', help='The type of distributed optimizer. Supporting options are ' + - '[win_put(default), neighbor_allreduce, allreduce, pull_get, push_sum, horovod]') -parser.add_argument('--enable-dynamic-topology', action='store_true', - default=False, help=('Enable each iteration to transmit one neighbor ' + + '[win_put(default), neighbor_allreduce, allreduce, ' + + 'hierarchical_neighbor_allreduce, pull_get, push_sum, horovod]') +parser.add_argument('--disable-dynamic-topology', action='store_true', + default=False, help=('Disable each iteration to transmit one neighbor ' + 'per iteration dynamically.')) parser.add_argument('--virtual-topology', type=str, default="power2", help='The underlying virtual topology. Supporting options are ' + @@ -142,6 +143,9 @@ def forward(self, x): optimizer, model=model) elif args.dist_optimizer == 'push_sum': optimizer = bf.DistributedPushSumOptimizer(optimizer, model=model) +elif args.dist_optimizer == 'hierarchical_neighbor_allreduce': + optimizer = optimizer = bf.DistributedHierarchicalNeighborAllreduceOptimizer( + optimizer, model=model) elif args.dist_optimizer == 'horovod': optimizer = optimizer = bf.DistributedOptimizer( optimizer, named_parameters=model.named_parameters() @@ -176,7 +180,7 @@ def forward(self, x): data_index = 0 -if args.enable_dynamic_topology and args.dist_optimizer != 'horovod': +if not args.disable_dynamic_topology and (args.dist_optimizer != 'horovod'): if args.virtual_topology == 'InnerOuterRing': dynamic_neighbor_allreduce_gen = topology_util.GetInnerOuterRingDynamicSendRecvRanks( bf.size(), @@ -187,6 +191,14 @@ def forward(self, x): bf.size(), local_size=bf.local_size() if args.local_size == -1 else args.local_size, self_rank=bf.rank()) + elif args.dist_optimizer == 'hierarchical_neighbor_allreduce': + # This optimizer can use following dynamic topo only so far. + dynamic_machine_neighbor_allreduce_gen = topology_util.GetExp2DynamicSendRecvMachineRanks( + world_size=bf.size(), + local_size=bf.local_size(), + self_rank=bf.rank(), + local_rank=bf.local_rank() + ) else: dynamic_neighbor_allreduce_gen = topology_util.GetDynamicSendRecvRanks( bf.load_topology(), bf.rank()) @@ -212,6 +224,12 @@ def dynamic_topology_update(batch_idx): optimizer.neighbor_weights = { r: 1/(len(recv_neighbors) + 1) for r in recv_neighbors} optimizer.self_weight = 1 / (len(recv_neighbors) + 1) + optimizer.enable_topo_check = False + elif args.dist_optimizer == 'hierarchical_neighbor_allreduce': + send_machines, recv_machines = next(dynamic_machine_neighbor_allreduce_gen) + optimizer.send_neighbor_machines = send_machines + optimizer.neighbor_machine_weights = {r: 1/(len(recv_machines) + 1) for r in recv_machines} + optimizer.self_weight = 1 / (len(recv_machines) + 1) optimizer.enable_topo_check = False else: pass @@ -220,7 +238,7 @@ def dynamic_topology_update(batch_idx): def benchmark_step(): global data_index - if args.enable_dynamic_topology: + if not args.disable_dynamic_topology: dynamic_topology_update(data_index) data = datasets[data_index % len(datasets)] data_index += 1 diff --git a/examples/pytorch_cifar10_resnet.py b/examples/pytorch_cifar10_resnet.py index 1eb5a76b..d2c7f62b 100644 --- a/examples/pytorch_cifar10_resnet.py +++ b/examples/pytorch_cifar10_resnet.py @@ -81,9 +81,9 @@ default=False, help=("Allreduce called to average test result. Warning this will " + "force the algorithm to sync every end of epoch.")) -parser.add_argument("--enable-dynamic-topology", action="store_true", - default=False, help=("Enable each iteration to transmit one neighbor " + - "per iteration dynamically.")) +parser.add_argument('--disable-dynamic-topology', action='store_true', + default=False, help=('Disable each iteration to transmit one neighbor ' + + 'per iteration dynamically.')) parser.add_argument('--virtual-topology', type=str, default="power2", help='The underlying virtual topology. Supporting options are ' + '[power2(Default), ring, mesh, star].') @@ -267,7 +267,7 @@ def train(epoch): disable=not verbose,) as t: for batch_idx, (data, target) in enumerate(train_loader): adjust_learning_rate(epoch, batch_idx) - if args.enable_dynamic_topology: + if not args.disable_dynamic_topology: dynamic_topology_update(epoch, batch_idx) if args.cuda: @@ -348,7 +348,7 @@ def adjust_learning_rate(epoch, batch_idx): args.base_lr * bf.size() * args.batches_per_allreduce * lr_adj ) -if args.enable_dynamic_topology and args.dist_optimizer != 'horovod': +if not args.disable_dynamic_topology and (args.dist_optimizer != 'horovod'): if args.virtual_topology == 'InnerOuterRing': dynamic_neighbor_allreduce_gen = topology_util.GetInnerOuterRingDynamicSendRecvRanks( bf.size(), @@ -360,7 +360,7 @@ def adjust_learning_rate(epoch, batch_idx): local_size=bf.local_size(), self_rank=bf.rank()) elif args.dist_optimizer == 'hierarchical_neighbor_allreduce': - # This optimizer can use only, + # This optimizer can use following dynamic topo only so far. dynamic_machine_neighbor_allreduce_gen = topology_util.GetExp2DynamicSendRecvMachineRanks( world_size=bf.size(), local_size=bf.local_size(), diff --git a/examples/pytorch_mnist.py b/examples/pytorch_mnist.py index 460f9fe7..84f7a2b1 100644 --- a/examples/pytorch_mnist.py +++ b/examples/pytorch_mnist.py @@ -62,9 +62,9 @@ default=False, help=("Allreduce called to average test result. Warning this will " + "force the algorithm to sync every end of epoch.")) -parser.add_argument("--enable-dynamic-topology", action="store_true", - default=False, help=("Enable each iteration to transmit one neighbor " + - "per iteration dynamically.")) +parser.add_argument('--disable-dynamic-topology', action='store_true', + default=False, help=('Disable each iteration to transmit one neighbor ' + + 'per iteration dynamically.')) parser.add_argument('--virtual-topology', type=str, default="power2", help='The underlying virtual topology. Supporting options are ' + '[power2(Default), ring, mesh, star, InnerOuterRing, InnerOuterExp2].') @@ -203,6 +203,9 @@ def forward(self, x): elif args.dist_optimizer == 'gradient_allreduce': optimizer = optimizer = bf.DistributedGradientAllreduceOptimizer( optimizer, model=model) +elif args.dist_optimizer == 'hierarchical_neighbor_allreduce': + optimizer = optimizer = bf.DistributedHierarchicalNeighborAllreduceOptimizer( + optimizer, model=model) elif args.dist_optimizer == 'push_sum': optimizer = bf.DistributedPushSumOptimizer(optimizer, model=model) elif args.dist_optimizer == 'horovod': @@ -217,7 +220,7 @@ def forward(self, x): '[neighbor_allreduce, gradient_allreduce, allreduce, ' + 'win_put, push_sum, horovod]') -if args.enable_dynamic_topology and args.dist_optimizer != 'horovod': +if not args.disable_dynamic_topology and (args.dist_optimizer != 'horovod'): if args.virtual_topology == 'InnerOuterRing': dynamic_neighbor_allreduce_gen = topology_util.GetInnerOuterRingDynamicSendRecvRanks( bf.size(), @@ -227,7 +230,15 @@ def forward(self, x): dynamic_neighbor_allreduce_gen = topology_util.GetInnerOuterExp2DynamicSendRecvRanks( bf.size(), local_size=bf.local_size() if args.local_size == -1 else args.local_size, - self_rank=bf.rank()) + self_rank=bf.rank()) + elif args.dist_optimizer == 'hierarchical_neighbor_allreduce': + # This optimizer can use following dynamic topo only so far. + dynamic_machine_neighbor_allreduce_gen = topology_util.GetExp2DynamicSendRecvMachineRanks( + world_size=bf.size(), + local_size=bf.local_size(), + self_rank=bf.rank(), + local_rank=bf.local_rank() + ) else: dynamic_neighbor_allreduce_gen = topology_util.GetDynamicSendRecvRanks( bf.load_topology(), bf.rank()) @@ -256,6 +267,12 @@ def dynamic_topology_update(epoch, batch_idx): optimizer.neighbor_weights = {r: 1/(len(recv_neighbors) + 1) for r in recv_neighbors} optimizer.self_weight = 1 / (len(recv_neighbors) + 1) optimizer.enable_topo_check = False + elif args.dist_optimizer == 'hierarchical_neighbor_allreduce': + send_machines, recv_machines = next(dynamic_machine_neighbor_allreduce_gen) + optimizer.send_neighbor_machines = send_machines + optimizer.neighbor_machine_weights = {r: 1/(len(recv_machines) + 1) for r in recv_machines} + optimizer.self_weight = 1 / (len(recv_machines) + 1) + optimizer.enable_topo_check = False else: pass @@ -265,7 +282,7 @@ def train(epoch): # Bluefog: set epoch to sampler for shuffling. train_sampler.set_epoch(epoch) for batch_idx, (data, target) in enumerate(train_loader): - if args.enable_dynamic_topology: + if not args.disable_dynamic_topology: dynamic_topology_update(epoch, batch_idx) if args.cuda: data, target = data.cuda(), target.cuda() From 2c65cefd34bea1bb74b7374125d67f6519a3bad6 Mon Sep 17 00:00:00 2001 From: ybc Date: Sat, 24 Oct 2020 21:52:46 -0700 Subject: [PATCH 18/44] fix the directly typo --- bluefog/torch/mpi_ops.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bluefog/torch/mpi_ops.py b/bluefog/torch/mpi_ops.py index 81da35b3..04ab7784 100644 --- a/bluefog/torch/mpi_ops.py +++ b/bluefog/torch/mpi_ops.py @@ -569,7 +569,7 @@ def _hierarchical_neighbor_allreduce_nonblocking( send_neighbor_machines, enable_topo_check, name): assert is_homogeneous, \ "hierarchical_neighbor_allreduce should be used under homogeneous environment only" - assert local_size() > 1, "If local size is 1, you should use neighbor allreduce directorly." + assert local_size() > 1, "If local size is 1, you should use neighbor allreduce directly." function = _check_function(_neighbor_allreduce_function_factory, tensor) if self_weight is not None and neighbor_machine_weights is not None: if not isinstance(neighbor_machine_weights, dict): From 5c73e70fee8c5643ff5e022852c5d26bffae808e Mon Sep 17 00:00:00 2001 From: ybc Date: Sun, 25 Oct 2020 13:56:01 -0700 Subject: [PATCH 19/44] Fix the mesh topo usage --- examples/pytorch_ImageNet_Resnet50.py | 6 +++--- examples/pytorch_benchmark.py | 6 +++--- examples/pytorch_cifar10_resnet.py | 6 +++--- examples/pytorch_mnist.py | 6 +++--- 4 files changed, 12 insertions(+), 12 deletions(-) diff --git a/examples/pytorch_ImageNet_Resnet50.py b/examples/pytorch_ImageNet_Resnet50.py index bf0618c1..3aabdb0c 100644 --- a/examples/pytorch_ImageNet_Resnet50.py +++ b/examples/pytorch_ImageNet_Resnet50.py @@ -91,10 +91,10 @@ if args.virtual_topology == "power2": pass elif args.virtual_topology == "ring": - bf.set_topology(topology_util.RingGraph(bf.size(), connect_style=0)) + bf.set_topology(topology_util.RingGraph(bf.size(), connect_style=1)) elif args.virtual_topology == "mesh": - bf.set_topology(topology_util.RingGraph( - bf.size(), connect_style=0), is_weighted=True) + bf.set_topology(topology_util.MeshGrid2DGraph( + bf.size()), is_weighted=True) elif args.virtual_topology == "star": bf.set_topology(topology_util.StarGraph(bf.size())) else: diff --git a/examples/pytorch_benchmark.py b/examples/pytorch_benchmark.py index 1681cf8b..15158271 100644 --- a/examples/pytorch_benchmark.py +++ b/examples/pytorch_benchmark.py @@ -77,10 +77,10 @@ if args.virtual_topology == "power2": pass elif args.virtual_topology == "ring": - bf.set_topology(topology_util.RingGraph(bf.size(), connect_style=0)) + bf.set_topology(topology_util.RingGraph(bf.size(), connect_style=1)) elif args.virtual_topology == "mesh": - bf.set_topology(topology_util.RingGraph( - bf.size(), connect_style=0), is_weighted=True) + bf.set_topology(topology_util.MeshGrid2DGraph( + bf.size()), is_weighted=True) elif args.virtual_topology == "star": bf.set_topology(topology_util.StarGraph(bf.size())) elif args.virtual_topology == "InnerOuterRing": diff --git a/examples/pytorch_cifar10_resnet.py b/examples/pytorch_cifar10_resnet.py index d2c7f62b..e801d30a 100644 --- a/examples/pytorch_cifar10_resnet.py +++ b/examples/pytorch_cifar10_resnet.py @@ -103,10 +103,10 @@ if args.virtual_topology == "power2": pass elif args.virtual_topology == "ring": - bf.set_topology(topology_util.RingGraph(bf.size(), connect_style=0)) + bf.set_topology(topology_util.RingGraph(bf.size(), connect_style=1)) elif args.virtual_topology == "mesh": - bf.set_topology(topology_util.RingGraph( - bf.size(), connect_style=0), is_weighted=True) + bf.set_topology(topology_util.MeshGrid2DGraph( + bf.size()), is_weighted=True) elif args.virtual_topology == "star": bf.set_topology(topology_util.StarGraph(bf.size())) elif args.virtual_topology == "InnerOuterRing": diff --git a/examples/pytorch_mnist.py b/examples/pytorch_mnist.py index 84f7a2b1..a80d97ed 100644 --- a/examples/pytorch_mnist.py +++ b/examples/pytorch_mnist.py @@ -93,10 +93,10 @@ if args.virtual_topology == "power2": pass elif args.virtual_topology == "ring": - bf.set_topology(topology_util.RingGraph(bf.size(), connect_style=0)) + bf.set_topology(topology_util.RingGraph(bf.size(), connect_style=1)) elif args.virtual_topology == "mesh": - bf.set_topology(topology_util.RingGraph( - bf.size(), connect_style=0), is_weighted=True) + bf.set_topology(topology_util.MeshGrid2DGraph( + bf.size()), is_weighted=True) elif args.virtual_topology == "star": bf.set_topology(topology_util.StarGraph(bf.size())) elif args.virtual_topology == "InnerOuterRing": From f6df8e14dc1a03e3148b2bb961fd93fbc1816485 Mon Sep 17 00:00:00 2001 From: ybc Date: Sun, 25 Oct 2020 17:59:24 -0700 Subject: [PATCH 20/44] Update the comments for hier n_ar --- bluefog/torch/mpi_ops.py | 80 ++++++++++++++++++++++++++++++++++++++-- 1 file changed, 76 insertions(+), 4 deletions(-) diff --git a/bluefog/torch/mpi_ops.py b/bluefog/torch/mpi_ops.py index 04ab7784..e0f599ee 100644 --- a/bluefog/torch/mpi_ops.py +++ b/bluefog/torch/mpi_ops.py @@ -437,7 +437,7 @@ def neighbor_allreduce(tensor: torch.Tensor, are ready to send and receive the tensor. Arguments: - tensor: A tensor to weighted average. + tensor: A tensor to execute weighted average with neighbors. self_weight: The weight for self node, used with neighbor_weights. neighbor_weights: The weights for in-neighbor nodes, used with self weight. If neighbor_weights is presented, the return tensor will return the weighted average @@ -486,7 +486,7 @@ def neighbor_allreduce_nonblocking(tensor: torch.Tensor, are ready to send and receive the tensor. Arguments: - tensor: A tensor to neighbor_allreduce. + tensor: A tensor to execute weighted average with neighbors. self_weight: The weight for self node, used with neighbor_weights. neighbor_weights: The weights for in-neighbor nodes, used with self weight. If neighbor_weights is presented, the return tensor will return the weighted average @@ -529,6 +529,38 @@ def hierarchical_neighbor_allreduce(tensor: torch.Tensor, send_neighbor_machines: List[int], enable_topo_check: bool = False, name: Optional[str] = None) -> torch.Tensor: + """ + A function that performs weighted averaging of the input tensor over the negihbor machines and + itself in the Bluefog processes. It is similar to neighbor_allreduce except each machine, + containing multiple processes, runs allreduce internal to form a super node then executes the + neighbor allreduce. The default behavior is (uniformly) average. + + The input tensor is not modified. + + The reduction operation is keyed by the name. If name is not provided, an incremented + auto-generated name is used. The tensor type and shape must be the same on all + Bluefog processes for a given name. The reduction will not start until all processes + are ready to send and receive the tensor. + + Warning: This function should be called only under homogenerous environment, all machines have + same number of Bluefog processes -- bf.local_size(). + + Arguments: + tensor: A tensor to execute weighted average with neighbor machines. + self_weight: The weight for self node, used with neighbor_weights. + neighbor_machine_weights: The weights for in-neighbor nodes, used with self weight. + The data structure of weights should be {machine id : weight}. All processes under + same machine should specifiy the same weights dictionary. + send_neighbor_machines: The list of neighbor machines to be sent to. All processes under + same machine should specifiy the same machine id. + enable_topo_check: When send_neighbors is present, enabling this option checks if the + sending and recieving neighbors match with each other. Disabling this check can boost + the performance. + name: A name of the reduction operation. + + Returns: + A tensor of the same shape and type as `tensor`, across all processes. + """ # TODO(hhb) Implement the logics for topo check for hierarchical_neighbor_allreduce. # TODO(ybc) add check for self_weight and neighbor_machine_weights. @@ -548,6 +580,39 @@ def hierarchical_neighbor_allreduce_nonblocking(tensor: torch.Tensor, send_neighbor_machines: List[int], enable_topo_check: bool = False, name: Optional[str] = None) -> int: + """ + A function that nonblockingly performs weighted averaging of the input tensor over the negihbor + machines and itself in the Bluefog processes. It is similar to neighbor_allreduce except each + machine containing multiple processes, runs allreduce internal to form a super node then executes + the neighbor allreduce. The default behavior is (uniformly) average. + + The input tensor is not modified. + + The reduction operation is keyed by the name. If name is not provided, an incremented + auto-generated name is used. The tensor type and shape must be the same on all + Bluefog processes for a given name. The reduction will not start until all processes + are ready to send and receive the tensor. + + Warning: This function should be called only under homogenerous environment, all machines have + same number of Bluefog processes -- bf.local_size(). + + Arguments: + tensor: A tensor to execute weighted average with neighbor machines. + self_weight: The weight for self node, used with neighbor_weights. + neighbor_machine_weights: The weights for in-neighbor nodes, used with self weight. + The data structure of weights should be {machine id : weight}. All processes under + same machine should specifiy the same weights dictionary. + send_neighbor_machines: The list of neighbor machines to be sent to. All processes under + same machine should specifiy the same machine id. + enable_topo_check: When send_neighbors is present, enabling this option checks if the + sending and recieving neighbors match with each other. Disabling this check can boost + the performance. + name: A name of the reduction operation. + + Returns: + A handle to the hierarchical_neighbor_allreduce operation that can be used with `poll()` or + `synchronize()`. + """ if (self_weight is None or neighbor_machine_weights is None): raise ValueError("Arguments self_weight and neighbor_weights cannot be empty or None.") if (self_weight is None and neighbor_machine_weights is not None) or \ @@ -588,10 +653,17 @@ def _hierarchical_neighbor_allreduce_nonblocking( break else: raise ValueError("Arguments self_weight and neighbor_weights cannot be empty or None.") + + machine_size = size() // local_size() # Translate machine id into rank id. node_per_machine = local_size() - neighbor_weights = { - node_per_machine*m: weights for (m, weights) in neighbor_machine_weights.items()} + neighbor_weights = {} + for (m, weights) in neighbor_machine_weights.items(): + if m >= machine_size: + raise ValueError( + f"machine id is larger than number of machine we detected ({node_per_machine})." + "Note it is 0-based index.") + neighbor_weights[node_per_machine*m] = weights send_neighbors = [node_per_machine*m for m in send_neighbor_machines] tensor_buffer = tensor.detach().clone() is_hierarchical = True From f5d5f65698148fb75d4292b07e73f45a08ac7c99 Mon Sep 17 00:00:00 2001 From: ybc Date: Sun, 25 Oct 2020 18:23:54 -0700 Subject: [PATCH 21/44] Add is_hierarchical to request message --- bluefog/common/message.cc | 4 ++++ bluefog/common/message.h | 4 ++++ bluefog/common/operations.cc | 25 +++++++++++++++++++++++++ bluefog/common/wire/message.fbs | 3 +++ bluefog/common/wire/message_generated.h | 20 ++++++++++++++++---- 5 files changed, 52 insertions(+), 4 deletions(-) diff --git a/bluefog/common/message.cc b/bluefog/common/message.cc index 99bb22cc..82cfa6e0 100644 --- a/bluefog/common/message.cc +++ b/bluefog/common/message.cc @@ -96,6 +96,10 @@ int32_t Request::device() const { return device_; } void Request::set_device(int32_t value) { device_ = value; } +bool Request::is_hierarchical() const { return is_hierarchical_; } + +void Request::set_is_hierarchical(bool value) { is_hierarchical_ = value; } + const std::vector& Request::tensor_shape() const { return tensor_shape_; } diff --git a/bluefog/common/message.h b/bluefog/common/message.h index d60a5524..b24063df 100644 --- a/bluefog/common/message.h +++ b/bluefog/common/message.h @@ -70,6 +70,9 @@ class Request { int32_t device() const; void set_device(int32_t value); + bool is_hierarchical() const; + void set_is_hierarchical(bool value); + const std::vector& tensor_shape() const; void set_tensor_shape(const std::vector& value); void add_tensor_shape(int64_t value); @@ -84,6 +87,7 @@ class Request { DataType tensor_type_ = DataType::BLUEFOG_UINT8; int32_t root_rank_ = 0; int32_t device_ = 0; + bool is_hierarchical_ = false; std::string tensor_name_; std::vector tensor_shape_; }; diff --git a/bluefog/common/operations.cc b/bluefog/common/operations.cc index ae4fb970..93d85ca3 100644 --- a/bluefog/common/operations.cc +++ b/bluefog/common/operations.cc @@ -163,6 +163,25 @@ bool CheckRequestRootRank(const std::vector& requests, return error; } +bool CheckRequestIsHierarchical(const std::vector& requests, + std::ostringstream& error_message_stream) { + auto message_type = requests[0].request_type(); + bool error = false; + int first_is_hierarchical = requests[0].is_hierarchical(); + for (unsigned int i = 1; i < requests.size(); i++) { + int this_is_hierarchical = requests[i].is_hierarchical(); + if (first_is_hierarchical != this_is_hierarchical) { + error = true; + error_message_stream + << "Mismatched " << Request::RequestType_Name(message_type) + << " is_hierarchical. Some ranks specified for hierarchical ops " + << " but some ranks are not."; + break; + } + } + return error; +} + bool CheckRequestTensorShape(const std::vector& requests, std::ostringstream& error_message_stream) { bool error = false; @@ -297,6 +316,11 @@ Response ConstructResponse(MessageTable* message_table, std::string name) { } // TODO(ybc) add check for is_hierarchical for neighbor_allreduce. + if(!error) { + if(message_type == Request::NEIGHBOR_ALLREDUCE) { + error = CheckRequestIsHierarchical(requests, error_message_stream); + } + } // If we are doing an (neighbor_)allreduce or broadcast, check that all tensor // shapes are identical. @@ -1497,6 +1521,7 @@ Status EnqueueTensorNeighborAllreduce(std::shared_ptr tensor, message.set_tensor_type(tensor->dtype()); message.set_device(device); message.set_request_type(Request::NEIGHBOR_ALLREDUCE); + message.set_is_hierarchical(is_hierarchical); for (int i = 0; i < tensor->shape().dims(); i++) { message.add_tensor_shape((int64_t)tensor->shape().dim_size(i)); } diff --git a/bluefog/common/wire/message.fbs b/bluefog/common/wire/message.fbs index 8c8a64b0..abe876ad 100644 --- a/bluefog/common/wire/message.fbs +++ b/bluefog/common/wire/message.fbs @@ -67,6 +67,9 @@ table Request { // We use a repeated integer to represent the tensor shape. tensor_shape:[long]; + + // Indicates it is hierarchical operation or not. + is_hierarchical: bool; } table RequestList { requests:[Request]; diff --git a/bluefog/common/wire/message_generated.h b/bluefog/common/wire/message_generated.h index 57ec519c..4c54de00 100644 --- a/bluefog/common/wire/message_generated.h +++ b/bluefog/common/wire/message_generated.h @@ -199,7 +199,8 @@ struct Request FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { VT_TENSOR_NAME = 10, VT_ROOT_RANK = 12, VT_DEVICE = 14, - VT_TENSOR_SHAPE = 16 + VT_TENSOR_SHAPE = 16, + VT_IS_HIERARCHICAL = 18 }; int32_t request_rank() const { return GetField(VT_REQUEST_RANK, 0); @@ -222,6 +223,9 @@ struct Request FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { const flatbuffers::Vector *tensor_shape() const { return GetPointer *>(VT_TENSOR_SHAPE); } + bool is_hierarchical() const { + return GetField(VT_IS_HIERARCHICAL, 0) != 0; + } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && VerifyField(verifier, VT_REQUEST_RANK) && @@ -233,6 +237,7 @@ struct Request FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { VerifyField(verifier, VT_DEVICE) && VerifyOffset(verifier, VT_TENSOR_SHAPE) && verifier.VerifyVector(tensor_shape()) && + VerifyField(verifier, VT_IS_HIERARCHICAL) && verifier.EndTable(); } }; @@ -262,6 +267,9 @@ struct RequestBuilder { void add_tensor_shape(flatbuffers::Offset> tensor_shape) { fbb_.AddOffset(Request::VT_TENSOR_SHAPE, tensor_shape); } + void add_is_hierarchical(bool is_hierarchical) { + fbb_.AddElement(Request::VT_IS_HIERARCHICAL, static_cast(is_hierarchical), 0); + } explicit RequestBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) { start_ = fbb_.StartTable(); @@ -282,13 +290,15 @@ inline flatbuffers::Offset CreateRequest( flatbuffers::Offset tensor_name = 0, int32_t root_rank = 0, int32_t device = 0, - flatbuffers::Offset> tensor_shape = 0) { + flatbuffers::Offset> tensor_shape = 0, + bool is_hierarchical = false) { RequestBuilder builder_(_fbb); builder_.add_tensor_shape(tensor_shape); builder_.add_device(device); builder_.add_root_rank(root_rank); builder_.add_tensor_name(tensor_name); builder_.add_request_rank(request_rank); + builder_.add_is_hierarchical(is_hierarchical); builder_.add_tensor_type(tensor_type); builder_.add_request_type(request_type); return builder_.Finish(); @@ -302,7 +312,8 @@ inline flatbuffers::Offset CreateRequestDirect( const char *tensor_name = nullptr, int32_t root_rank = 0, int32_t device = 0, - const std::vector *tensor_shape = nullptr) { + const std::vector *tensor_shape = nullptr, + bool is_hierarchical = false) { auto tensor_name__ = tensor_name ? _fbb.CreateString(tensor_name) : 0; auto tensor_shape__ = tensor_shape ? _fbb.CreateVector(*tensor_shape) : 0; return bluefog::common::wire::CreateRequest( @@ -313,7 +324,8 @@ inline flatbuffers::Offset CreateRequestDirect( tensor_name__, root_rank, device, - tensor_shape__); + tensor_shape__, + is_hierarchical); } struct RequestList FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { From 4489a9dd2f9be3b111130126c30390ab44c2df37 Mon Sep 17 00:00:00 2001 From: ybc Date: Sun, 25 Oct 2020 19:45:04 -0700 Subject: [PATCH 22/44] fix the is_hierarchical usage in request --- bluefog/common/message.cc | 2 ++ bluefog/common/operations.cc | 6 +++--- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/bluefog/common/message.cc b/bluefog/common/message.cc index 82cfa6e0..b36cff16 100644 --- a/bluefog/common/message.cc +++ b/bluefog/common/message.cc @@ -122,6 +122,7 @@ void Request_ParseFromWire(Request& request, request.set_tensor_name(obj->tensor_name()->str()); request.set_root_rank(obj->root_rank()); request.set_device(obj->device()); + request.set_is_hierarchical(obj->is_hierarchical()); request.set_tensor_shape(std::vector(obj->tensor_shape()->begin(), obj->tensor_shape()->end())); } @@ -141,6 +142,7 @@ void Request_SerializeToWire(const Request& request, request_builder.add_tensor_name(tensor_name_wire); request_builder.add_root_rank(request.root_rank()); request_builder.add_device(request.device()); + request_builder.add_is_hierarchical(request.is_hierarchical()); request_builder.add_tensor_shape(tensor_shape_wire); obj = request_builder.Finish(); } diff --git a/bluefog/common/operations.cc b/bluefog/common/operations.cc index 93d85ca3..5273e2e5 100644 --- a/bluefog/common/operations.cc +++ b/bluefog/common/operations.cc @@ -167,14 +167,14 @@ bool CheckRequestIsHierarchical(const std::vector& requests, std::ostringstream& error_message_stream) { auto message_type = requests[0].request_type(); bool error = false; - int first_is_hierarchical = requests[0].is_hierarchical(); + bool first_is_hierarchical = requests[0].is_hierarchical(); for (unsigned int i = 1; i < requests.size(); i++) { - int this_is_hierarchical = requests[i].is_hierarchical(); + bool this_is_hierarchical = requests[i].is_hierarchical(); if (first_is_hierarchical != this_is_hierarchical) { error = true; error_message_stream << "Mismatched " << Request::RequestType_Name(message_type) - << " is_hierarchical. Some ranks specified for hierarchical ops " + << " is_hierarchical ops. Some ranks specified for hierarchical ops " << " but some ranks are not."; break; } From 4344a103fd1027ada0ba7fd00320da17216c0538 Mon Sep 17 00:00:00 2001 From: ybc Date: Sun, 25 Oct 2020 22:20:03 -0700 Subject: [PATCH 23/44] Update GetExp2DynamicSendRecvMachineRanks in 2 machines case --- bluefog/common/topology_util.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/bluefog/common/topology_util.py b/bluefog/common/topology_util.py index 4997afeb..73cc30f9 100644 --- a/bluefog/common/topology_util.py +++ b/bluefog/common/topology_util.py @@ -437,9 +437,12 @@ def GetExp2DynamicSendRecvMachineRanks( "It should be used under homogeneous environment only." assert (world_size % local_size) == 0, \ "It should be used under homogeneous environment only." + assert world_size > local_size, \ + "It should be used under at least two machines case." + machine_id = self_rank // local_size machine_size = world_size // local_size - exp_2_size = int(np.log2(machine_size-1)) + exp_2_size = int(np.log2(machine_size-1)) if machine_size > 1 else 0 index = 0 while True: machine_dist = 2**(index % (exp_2_size + 1)) From 510210a361a66dd48366bcfc4c13306618e44a1c Mon Sep 17 00:00:00 2001 From: ybc Date: Sun, 25 Oct 2020 22:27:49 -0700 Subject: [PATCH 24/44] Update hier n_ar comments --- bluefog/torch/mpi_ops.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/bluefog/torch/mpi_ops.py b/bluefog/torch/mpi_ops.py index e0f599ee..6c3b7609 100644 --- a/bluefog/torch/mpi_ops.py +++ b/bluefog/torch/mpi_ops.py @@ -531,9 +531,9 @@ def hierarchical_neighbor_allreduce(tensor: torch.Tensor, name: Optional[str] = None) -> torch.Tensor: """ A function that performs weighted averaging of the input tensor over the negihbor machines and - itself in the Bluefog processes. It is similar to neighbor_allreduce except each machine, - containing multiple processes, runs allreduce internal to form a super node then executes the - neighbor allreduce. The default behavior is (uniformly) average. + itself in the Bluefog processes. It is similar to neighbor_allreduce. But each machine runs + allreduce internal first to form a super node then executes the neighbor allreduce at machine + level. The default behavior is (uniformly) average. The input tensor is not modified. @@ -582,9 +582,9 @@ def hierarchical_neighbor_allreduce_nonblocking(tensor: torch.Tensor, name: Optional[str] = None) -> int: """ A function that nonblockingly performs weighted averaging of the input tensor over the negihbor - machines and itself in the Bluefog processes. It is similar to neighbor_allreduce except each - machine containing multiple processes, runs allreduce internal to form a super node then executes - the neighbor allreduce. The default behavior is (uniformly) average. + machines and itself in the Bluefog processes. It is similar to neighbor_allreduce. But + each machine runs allreduce internal first to form a super node then executes + the neighbor allreduce at machine level. The default behavior is (uniformly) average. The input tensor is not modified. From 726b06e547285f8930f4e529da10867c86b91612 Mon Sep 17 00:00:00 2001 From: ybc Date: Sun, 25 Oct 2020 23:28:26 -0700 Subject: [PATCH 25/44] Use local rank 0 to send/recv for h n_ar --- bluefog/common/nccl_controller.cc | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/bluefog/common/nccl_controller.cc b/bluefog/common/nccl_controller.cc index f0e525fb..9e9926f2 100644 --- a/bluefog/common/nccl_controller.cc +++ b/bluefog/common/nccl_controller.cc @@ -989,13 +989,9 @@ void NCCLController::NeighborAllreduce(std::vector& entries) { void* recvbuf = (void*)((uint8_t*)buffer_data + num_elements * i * element_size); NCCLCHECK(ncclRecv(recvbuf, num_elements, - GetNCCLDataType(first_entry.tensor), recv_rank+1, + GetNCCLDataType(first_entry.tensor), recv_rank, nccl_ctx_.nccl_comm, nccl_ctx_.stream)); } - ncclGroupEnd(); - } else if (mpi_ctx_.local_rank_ == 1) { - // Use local rank 0 for sending - ncclGroupStart(); for (int send_rank : *first_entry.send_neighbors) { NCCLCHECK(ncclSend(fused_input_data, num_elements, GetNCCLDataType(first_entry.tensor), send_rank, From 8650da5e9407860511e4554284ab976cdee52ccc Mon Sep 17 00:00:00 2001 From: ybc Date: Tue, 27 Oct 2020 17:00:03 -0700 Subject: [PATCH 26/44] Add allreduce inplace API --- bluefog/torch/__init__.py | 1 + bluefog/torch/mpi_ops.py | 49 +++++++++++++++++++++++++++ bluefog/torch/utility.py | 2 +- test/torch_ops_test.py | 69 +++++++++++++++++++++++++++++++++++++-- 4 files changed, 118 insertions(+), 3 deletions(-) diff --git a/bluefog/torch/__init__.py b/bluefog/torch/__init__.py index f5f2e8e0..e656b054 100644 --- a/bluefog/torch/__init__.py +++ b/bluefog/torch/__init__.py @@ -43,6 +43,7 @@ from bluefog.torch.mpi_ops import set_skip_negotiate_stage, get_skip_negotiate_stage from bluefog.torch.mpi_ops import allreduce, allreduce_nonblocking +from bluefog.torch.mpi_ops import allreduce_, allreduce_nonblocking_ from bluefog.torch.mpi_ops import allgather, allgather_nonblocking from bluefog.torch.mpi_ops import broadcast, broadcast_nonblocking from bluefog.torch.mpi_ops import broadcast_, broadcast_nonblocking_ diff --git a/bluefog/torch/mpi_ops.py b/bluefog/torch/mpi_ops.py index 6c3b7609..df285994 100644 --- a/bluefog/torch/mpi_ops.py +++ b/bluefog/torch/mpi_ops.py @@ -143,6 +143,55 @@ def allreduce_nonblocking(tensor: torch.Tensor, average: bool = True, return _allreduce_nonblocking(tensor, output, average, name) +def allreduce_(tensor: torch.Tensor, average: bool = True, + name: Optional[str] = None) -> torch.Tensor: + """ + A function that performs averaging or summation of the input tensor over all the + Bluefog processes. The operation is performed in-place. + + The reduction operation is keyed by the name. If name is not provided, an incremented + auto-generated name is used. The tensor type and shape must be the same on all + Bluefog processes for a given name. The reduction will not start until all processes + are ready to send and receive the tensor. + + Arguments: + tensor: A tensor to average and sum. + average: A flag indicating whether to compute average or summation, + defaults to average. + name: A name of the reduction operation. + + Returns: + A tensor of the same shape and type as `tensor`, averaged or summed across all + processes. + """ + handle = allreduce_nonblocking_(tensor, average, name) + return synchronize(handle) + + +def allreduce_nonblocking_(tensor: torch.Tensor, average: bool = True, + name: Optional[str] = None) -> int: + """ + A function that performs nonblocking averaging or summation of the input tensor + over all the Bluefog processes. The operation is performed in-place. + + The reduction operation is keyed by the name. If name is not provided, an incremented + auto-generated name is used. The tensor type and shape must be the same on all + Bluefog processes for a given name. The reduction will not start until all processes + are ready to send and receive the tensor. + + Arguments: + tensor: A tensor to average and sum. + average: A flag indicating whether to compute average or summation, + defaults to average. + name: A name of the reduction operation. + + Returns: + A handle to the allreduce operation that can be used with `poll()` or + `synchronize()`. + """ + return _allreduce_nonblocking(tensor, tensor, average, name) + + def _broadcast_function_factory(tensor): return 'bluefog_torch_broadcast_nonblocking_' + tensor.type().replace('.', '_') diff --git a/bluefog/torch/utility.py b/bluefog/torch/utility.py index 5de28b7a..ea94538d 100644 --- a/bluefog/torch/utility.py +++ b/bluefog/torch/utility.py @@ -74,7 +74,7 @@ def allreduce_parameters(params): # Run asynchronous broadcasts. handles = [] for name, p in params: - handle = bf.allreduce_nonblocking(p, average=True, name=name) + handle = bf.allreduce_nonblocking_(p, average=True, name=name) handles.append(handle) # Wait for completion. diff --git a/test/torch_ops_test.py b/test/torch_ops_test.py index 8a4e8fc6..7be827dd 100644 --- a/test/torch_ops_test.py +++ b/test/torch_ops_test.py @@ -131,7 +131,7 @@ def test_broadcast_inplace(self): ), "bf.broadcast_ produces incorrect broadcasted tensor" def test_allreduce_avg(self): - """Test that the allreduce correctly sums 1D, 2D, 3D tensors.""" + """Test that the allreduce correctly average 1D, 2D, 3D tensors.""" size = bf.size() if size <= 1: fname = inspect.currentframe().f_code.co_name @@ -168,7 +168,6 @@ def test_allreduce_sum(self): dims = [1, 2, 3] for dtype, dim in itertools.product(dtypes, dims): - torch.manual_seed(123456) tensor = torch.FloatTensor(*([23] * dim)).random_(-100, 100) tensor = self.cast_and_place(tensor, dtype) name = "allreduce_tensor_{}_{}".format(dim, dtype) @@ -179,6 +178,32 @@ def test_allreduce_sum(self): torch.allclose(output, tensor.mul(size)) ), "bf.allreduce(sum) produces incorrect tensor" + def test_allreduce_avg_inplace(self): + """Test that the allreduce correctly averages 1D, 2D, 3D tensors inplace.""" + size = bf.size() + rank = bf.rank() + if size <= 1: + fname = inspect.currentframe().f_code.co_name + warnings.warn("Skip {} due to size 1".format(fname)) + return + dtypes = [torch.FloatTensor, torch.DoubleTensor, torch.HalfTensor] + if TEST_ON_GPU: + dtypes += [torch.cuda.FloatTensor, torch.cuda.DoubleTensor] + + dims = [1, 2, 3] + for dtype, dim in itertools.product(dtypes, dims): + torch.manual_seed(123456) + tensor = torch.FloatTensor(*([23] * dim)).fill_(1).mul_(rank) + name = "allreduce_tensor_{}_{}".format(dim, dtype) + tensor = self.cast_and_place(tensor, dtype) + + bf.allreduce_(tensor, average=True, name=name) + tensor = self.convert_cpu_fp16_to_fp32(tensor)[0] + exp_tenosr = torch.ones_like(tensor).mul_((size-1)/2) + assert ( + torch.allclose(tensor, exp_tenosr) + ), "bf.allreduce_(avg) produces incorrect tensor" + def test_allreduce_fusion(self): """Test that the allreduce works under tensor fusion.""" size = bf.size() @@ -214,6 +239,46 @@ def test_allreduce_fusion(self): torch.allclose(tensor_2, output_2) ), "bf.allreduce(fusion) produces incorrect tensor 2" + def test_allreduce_fusion_inplace(self): + """Test that the allreduce works under tensor fusion.""" + size = bf.size() + rank = bf.rank() + if size <= 1: + fname = inspect.currentframe().f_code.co_name + warnings.warn("Skip {} due to size 1".format(fname)) + return + dtypes = [torch.FloatTensor, torch.DoubleTensor, torch.HalfTensor] + if TEST_ON_GPU: + dtypes += [torch.cuda.FloatTensor, torch.cuda.DoubleTensor] + + dims = [1, 2, 3] + for dtype, dim in itertools.product(dtypes, dims): + torch.manual_seed(123456) + tensor_1 = torch.FloatTensor(*([23] * dim)).fill_(1).mul_(rank) + tensor_2 = torch.FloatTensor(*([23] * dim)).fill_(1).mul_(rank+0.5) + name_1 = "allreduce_fusion_tensor_{}_{}_1".format(dim, dtype) + name_2 = "allreduce_fusion_tensor_{}_{}_2".format(dim, dtype) + tensor_1 = self.cast_and_place(tensor_1, dtype) + tensor_2 = self.cast_and_place(tensor_2, dtype) + + handle_1 = bf.allreduce_nonblocking_( + tensor_1, average=True, name=name_1) + handle_2 = bf.allreduce_nonblocking_( + tensor_2, average=True, name=name_2) + + bf.synchronize(handle_1) + bf.synchronize(handle_2) + tensor_1 = self.convert_cpu_fp16_to_fp32(tensor_1)[0] + tensor_2 = self.convert_cpu_fp16_to_fp32(tensor_2)[0] + exp_tenosr_1 = torch.ones_like(tensor_1).mul_((size-1)/2) + exp_tenosr_2 = torch.ones_like(tensor_2).mul_((size-1)/2 + 0.5) + assert ( + torch.allclose(tensor_1, exp_tenosr_1) + ), "bf.allreduce(fusion) produces incorrect tensor 1" + assert ( + torch.allclose(tensor_2, exp_tenosr_2) + ), "bf.allreduce(fusion) produces incorrect tensor 2" + def test_allgather(self): """Test that the allgather correctly gathers 1D, 2D, 3D tensors.""" size = bf.size() From cc05047907661923c1d803791f2de78ed6091396 Mon Sep 17 00:00:00 2001 From: ybc Date: Tue, 27 Oct 2020 20:29:50 -0700 Subject: [PATCH 27/44] Update pytorch_benchmark.py --- examples/pytorch_benchmark.py | 1 - 1 file changed, 1 deletion(-) diff --git a/examples/pytorch_benchmark.py b/examples/pytorch_benchmark.py index 15158271..a242bb3f 100644 --- a/examples/pytorch_benchmark.py +++ b/examples/pytorch_benchmark.py @@ -69,7 +69,6 @@ args.cuda = not args.no_cuda and torch.cuda.is_available() if args.dist_optimizer == 'horovod': - print("importing horovod") import horovod.torch as bf bf.init() From 1a67e28a1ef73c468976f138e3a3bb51db0fd30e Mon Sep 17 00:00:00 2001 From: ybc Date: Wed, 28 Oct 2020 18:36:18 -0700 Subject: [PATCH 28/44] Add hierarchical local allreduce --- bluefog/common/mpi_controller.cc | 17 ++++++++++---- bluefog/common/nccl_controller.cc | 13 +++++++---- bluefog/common/operations.cc | 11 ++++++--- bluefog/common/operations.h | 1 + bluefog/torch/mpi_ops.cc | 39 ++++++++++++++++++------------- bluefog/torch/mpi_ops.h | 3 ++- bluefog/torch/mpi_ops.py | 28 ++++++++++++++-------- 7 files changed, 73 insertions(+), 39 deletions(-) diff --git a/bluefog/common/mpi_controller.cc b/bluefog/common/mpi_controller.cc index 3757854d..aa62efef 100644 --- a/bluefog/common/mpi_controller.cc +++ b/bluefog/common/mpi_controller.cc @@ -142,12 +142,16 @@ void MPIController::Allreduce(TensorTableEntry& entry) { void* buffer_data = (void*)entry.output->data(); int num_elements = entry.tensor->shape().num_elements(); + // Here is_hierarchical == true means local allreduce. + auto communicator_type = + entry.is_hierarchical ? Communicator::LOCAL : Communicator::GLOBAL; + // We need to explicitly set the device here. with_device device_guard(entry.device); - int ret_code = MPI_Allreduce( - sendbuf, buffer_data, num_elements, mpi_ctx_.GetMPIDataType(entry.tensor), - mpi_ctx_.GetMPISumOp(entry.tensor->dtype()), - mpi_ctx_.GetMPICommunicator(Communicator::GLOBAL)); + int ret_code = MPI_Allreduce(sendbuf, buffer_data, num_elements, + mpi_ctx_.GetMPIDataType(entry.tensor), + mpi_ctx_.GetMPISumOp(entry.tensor->dtype()), + mpi_ctx_.GetMPICommunicator(communicator_type)); if (ret_code != MPI_SUCCESS) { throw std::runtime_error( "MPI_AllReduce failed, see MPI output for details."); @@ -465,11 +469,14 @@ void MPIController::Allreduce(std::vector& entries) { timeline_ptr->ActivityEndAll(entries); timeline_ptr->ActivityStartAll(entries, "COMMUNICATE"); + // Here is_hierarchical == true means local allreduce. + auto communicator_type = + first_entry.is_hierarchical ? Communicator::LOCAL : Communicator::GLOBAL; int ret_code = MPI_Allreduce(MPI_IN_PLACE, buffer_data, num_elements, mpi_ctx_.GetMPIDataType(first_entry.tensor), mpi_ctx_.GetMPISumOp(first_entry.tensor->dtype()), - mpi_ctx_.GetMPICommunicator(Communicator::GLOBAL)); + mpi_ctx_.GetMPICommunicator(communicator_type)); if (ret_code != MPI_SUCCESS) { throw std::runtime_error( "MPI_AllReduce failed, see MPI output for details."); diff --git a/bluefog/common/nccl_controller.cc b/bluefog/common/nccl_controller.cc index 9e9926f2..73b68d75 100644 --- a/bluefog/common/nccl_controller.cc +++ b/bluefog/common/nccl_controller.cc @@ -443,13 +443,15 @@ void NCCLController::Allreduce(TensorTableEntry& entry) { int num_elements = entry.tensor->shape().num_elements(); std::queue> event_queue; + auto& nccl_comm = + entry.is_hierarchical ? nccl_ctx_.nccl_local_comm : nccl_ctx_.nccl_comm; with_device device_guard(entry.device); timeline_ptr_->ActivityStart(entry.tensor_name, "COMM. (NCCL)"); NCCLCHECK(ncclAllReduce(sendbuf, buffer_data, num_elements, - GetNCCLDataType(entry.tensor), ncclSum, - nccl_ctx_.nccl_comm, nccl_ctx_.stream)); + GetNCCLDataType(entry.tensor), ncclSum, nccl_comm, + nccl_ctx_.stream)); if (timeline_ptr_->Initialized()) { RecordEvent(event_queue, "COMM. (NCCL)"); @@ -846,10 +848,13 @@ void NCCLController::Allreduce(std::vector& entries) { } const void* fused_input_data = buffer_data; + auto& nccl_comm = first_entry.is_hierarchical ? nccl_ctx_.nccl_local_comm + : nccl_ctx_.nccl_comm; + ncclResult_t ret_code = ncclAllReduce(fused_input_data, buffer_data, num_elements, - GetNCCLDataType(first_entry.tensor), ncclSum, - nccl_ctx_.nccl_comm, nccl_ctx_.stream); + GetNCCLDataType(first_entry.tensor), ncclSum, nccl_comm, + nccl_ctx_.stream); if (ret_code != ncclSuccess) { std::string error_msg = "ncclAllReduce failed, see NCCL output (NCCL_DEBUG=INFO) " diff --git a/bluefog/common/operations.cc b/bluefog/common/operations.cc index 5273e2e5..e214cdf3 100644 --- a/bluefog/common/operations.cc +++ b/bluefog/common/operations.cc @@ -315,9 +315,10 @@ Response ConstructResponse(MessageTable* message_table, std::string name) { } } - // TODO(ybc) add check for is_hierarchical for neighbor_allreduce. - if(!error) { - if(message_type == Request::NEIGHBOR_ALLREDUCE) { + // If we are doing allreduce, make sure all are Hierarchical or are all not. + if (!error) { + if (message_type == Request::ALLREDUCE || + message_type == Request::NEIGHBOR_ALLREDUCE) { error = CheckRequestIsHierarchical(requests, error_message_stream); } } @@ -927,6 +928,7 @@ void NegotiateOfRequestOfMaster(BluefogGlobalState& state, if (response.response_type() == new_response.response_type() && response.devices() == new_response.devices() && entry.tensor->dtype() == new_entry.tensor->dtype() && + entry.is_hierarchical == new_entry.is_hierarchical && tensor_size + new_tensor_size <= state.tensor_fusion_threshold) { // These tensors will fuse together well. tensor_size += new_tensor_size; @@ -1382,6 +1384,7 @@ Status EnqueueTensorAllreduce(std::shared_ptr tensor, std::shared_ptr output, std::shared_ptr context, std::shared_ptr ready_event, + bool is_hierarchical_local, const std::string& name, const int device, StatusCallback callback) { Request message; @@ -1389,6 +1392,7 @@ Status EnqueueTensorAllreduce(std::shared_ptr tensor, message.set_tensor_name(name); message.set_tensor_type(tensor->dtype()); message.set_device(device); + message.set_is_hierarchical(is_hierarchical_local); message.set_request_type(Request::ALLREDUCE); for (int i = 0; i < tensor->shape().dims(); i++) { message.add_tensor_shape((int64_t)tensor->shape().dim_size(i)); @@ -1400,6 +1404,7 @@ Status EnqueueTensorAllreduce(std::shared_ptr tensor, e.output = output; e.device = device; e.ready_event = ready_event; + e.is_hierarchical = is_hierarchical_local; e.context = context; e.callback = callback; e.mpi_ops_type = MPIOpsType::ALLREDUCE; diff --git a/bluefog/common/operations.h b/bluefog/common/operations.h index 66293cde..7f922a52 100644 --- a/bluefog/common/operations.h +++ b/bluefog/common/operations.h @@ -112,6 +112,7 @@ Status EnqueueTensorAllreduce(std::shared_ptr tensor, std::shared_ptr output, std::shared_ptr context, std::shared_ptr ready_event, + bool is_hierarchical_local, const std::string& name, const int device, StatusCallback callback); diff --git a/bluefog/torch/mpi_ops.cc b/bluefog/torch/mpi_ops.cc index 8c1d283b..58661833 100644 --- a/bluefog/torch/mpi_ops.cc +++ b/bluefog/torch/mpi_ops.cc @@ -99,7 +99,7 @@ std::function(std::function)> } int DoAllreduce(::torch::Tensor tensor, ::torch::Tensor output, int average, - const std::string& name) { + bool is_hierarchical_local, const std::string& name) { ThrowIfError(common::CheckInitialized()); auto handle = handle_manager.AllocateHandle(); @@ -127,17 +127,21 @@ int DoAllreduce(::torch::Tensor tensor, ::torch::Tensor output, int average, auto ready_event = RecordReadyEvent(device); auto enqueue_result = EnqueueTensorAllreduce( - bf_tensor, bf_tensor, bf_context, ready_event, op_name, CPU_DEVICE_ID, - callback_wrapper([average, output, cpu_buffer, device]() mutable { - with_device device_guard(device); - output.copy_(cpu_buffer); + bf_tensor, bf_tensor, bf_context, ready_event, is_hierarchical_local, + op_name, CPU_DEVICE_ID, + callback_wrapper([average, output, is_hierarchical_local, cpu_buffer, + device]() mutable { + with_device device_guard(device); + output.copy_(cpu_buffer); - // Will execute in the `device` context. - ::torch::Tensor output_buffer = MaybeCopyToTensorBuffer(output); - if (average && bluefog_size() > 1) { - output_buffer.div_(bluefog_size()); - } - MaybeCopyBufferBack(output, output_buffer); + // Will execute in the `device` context. + ::torch::Tensor output_buffer = MaybeCopyToTensorBuffer(output); + int size = + is_hierarchical_local ? bluefog_local_size() : bluefog_size(); + if (average && size > 1) { + output_buffer.div_(bluefog_size()); + } + MaybeCopyBufferBack(output, output_buffer); })); ThrowIfError(enqueue_result); } else { @@ -147,15 +151,18 @@ int DoAllreduce(::torch::Tensor tensor, ::torch::Tensor output, int average, auto ready_event = RecordReadyEvent(device); auto enqueue_result = EnqueueTensorAllreduce( - bf_tensor, bf_output, bf_context, ready_event, op_name, device, - callback_wrapper([average, output]() mutable { + bf_tensor, bf_output, bf_context, ready_event, is_hierarchical_local, + op_name, device, + callback_wrapper([average, output, is_hierarchical_local]() mutable { // Will execute in the `device` context. ::torch::Tensor output_buffer = MaybeCopyToTensorBuffer(output); - if (average && bluefog_size() > 1) { - output_buffer.div_(bluefog_size()); + int size = + is_hierarchical_local ? bluefog_local_size() : bluefog_size(); + if (average && size > 1) { + output_buffer.div_(size); } MaybeCopyBufferBack(output, output_buffer); - })); + })); ThrowIfError(enqueue_result); } return handle; diff --git a/bluefog/torch/mpi_ops.h b/bluefog/torch/mpi_ops.h index ba495641..2808f780 100644 --- a/bluefog/torch/mpi_ops.h +++ b/bluefog/torch/mpi_ops.h @@ -31,7 +31,8 @@ namespace torch { #define ALLREDUCE_H(torch_Tensor, THTensor) \ extern "C" int bluefog_torch_allreduce_nonblocking_##torch_Tensor( \ - THTensor* tensor, THTensor* output, int average, char* name); + THTensor* tensor, THTensor* output, int average, \ + bool is_hierarchical_local, char* name); ALLREDUCE_H(torch_IntTensor, THIntTensor) ALLREDUCE_H(torch_LongTensor, THLongTensor) diff --git a/bluefog/torch/mpi_ops.py b/bluefog/torch/mpi_ops.py index df285994..dd8e61bc 100644 --- a/bluefog/torch/mpi_ops.py +++ b/bluefog/torch/mpi_ops.py @@ -79,7 +79,7 @@ def _allreduce_function_factory(tensor): return 'bluefog_torch_allreduce_nonblocking_' + tensor.type().replace('.', '_') -def _allreduce_nonblocking(tensor, output, average, name): +def _allreduce_nonblocking(tensor, output, average, is_hierarchical_local, name): function = _check_function(_allreduce_function_factory, tensor) if average: assert isinstance(tensor, (torch.HalfTensor, torch.FloatTensor, torch.DoubleTensor, @@ -87,14 +87,14 @@ def _allreduce_nonblocking(tensor, output, average, name): torch.cuda.HalfTensor)), \ "If average is set in allreduce, only float or double tensor is allowed." - handle = getattr(mpi_lib, function)(tensor, output, average, + handle = getattr(mpi_lib, function)(tensor, output, average, is_hierarchical_local, name.encode() if name is not None else "") _handle_map[handle] = (tensor, output) return handle def allreduce(tensor: torch.Tensor, average: bool = True, - name: Optional[str] = None) -> torch.Tensor: + is_hierarchical_local=False, name: Optional[str] = None) -> torch.Tensor: """ A function that performs averaging or summation of the input tensor over all the Bluefog processes. The input tensor is not modified. @@ -108,18 +108,20 @@ def allreduce(tensor: torch.Tensor, average: bool = True, tensor: A tensor to average and sum. average: A flag indicating whether to compute average or summation, defaults to average. + is_hierarchical_local: If set, allreduce is executed within one machine instead of + global allreduce. name: A name of the reduction operation. Returns: A tensor of the same shape and type as `tensor`, averaged or summed across all processes. """ - handle = allreduce_nonblocking(tensor, average, name) + handle = allreduce_nonblocking(tensor, average, is_hierarchical_local, name) return synchronize(handle) def allreduce_nonblocking(tensor: torch.Tensor, average: bool = True, - name: Optional[str] = None) -> int: + is_hierarchical_local=False, name: Optional[str] = None) -> int: """ A function that performs nonblocking averaging or summation of the input tensor over all the Bluefog processes. The input tensor is not modified. @@ -133,6 +135,8 @@ def allreduce_nonblocking(tensor: torch.Tensor, average: bool = True, tensor: A tensor to average and sum. average: A flag indicating whether to compute average or summation, defaults to average. + is_hierarchical_local: If set, allreduce is executed within one machine instead of + global allreduce. name: A name of the reduction operation. Returns: @@ -140,11 +144,11 @@ def allreduce_nonblocking(tensor: torch.Tensor, average: bool = True, `synchronize()`. """ output = tensor.new(tensor.shape) - return _allreduce_nonblocking(tensor, output, average, name) + return _allreduce_nonblocking(tensor, output, average, is_hierarchical_local, name) def allreduce_(tensor: torch.Tensor, average: bool = True, - name: Optional[str] = None) -> torch.Tensor: + is_hierarchical_local=False, name: Optional[str] = None) -> torch.Tensor: """ A function that performs averaging or summation of the input tensor over all the Bluefog processes. The operation is performed in-place. @@ -158,18 +162,20 @@ def allreduce_(tensor: torch.Tensor, average: bool = True, tensor: A tensor to average and sum. average: A flag indicating whether to compute average or summation, defaults to average. + is_hierarchical_local: If set, allreduce is executed within one machine instead of + global allreduce. name: A name of the reduction operation. Returns: A tensor of the same shape and type as `tensor`, averaged or summed across all processes. """ - handle = allreduce_nonblocking_(tensor, average, name) + handle = allreduce_nonblocking_(tensor, average, is_hierarchical_local, name) return synchronize(handle) def allreduce_nonblocking_(tensor: torch.Tensor, average: bool = True, - name: Optional[str] = None) -> int: + is_hierarchical_local=False, name: Optional[str] = None) -> int: """ A function that performs nonblocking averaging or summation of the input tensor over all the Bluefog processes. The operation is performed in-place. @@ -183,13 +189,15 @@ def allreduce_nonblocking_(tensor: torch.Tensor, average: bool = True, tensor: A tensor to average and sum. average: A flag indicating whether to compute average or summation, defaults to average. + is_hierarchical_local: If set, allreduce is executed within one machine instead of + global allreduce. name: A name of the reduction operation. Returns: A handle to the allreduce operation that can be used with `poll()` or `synchronize()`. """ - return _allreduce_nonblocking(tensor, tensor, average, name) + return _allreduce_nonblocking(tensor, tensor, average, is_hierarchical_local, name) def _broadcast_function_factory(tensor): From 5b9571971215844448a047468809891c0b57e25f Mon Sep 17 00:00:00 2001 From: ybc Date: Fri, 30 Oct 2020 22:19:23 -0700 Subject: [PATCH 29/44] Add use_empty_function_in_communication in dist optimizer --- bluefog/torch/optimizers.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/bluefog/torch/optimizers.py b/bluefog/torch/optimizers.py index 66f12477..5e63fd3b 100644 --- a/bluefog/torch/optimizers.py +++ b/bluefog/torch/optimizers.py @@ -360,6 +360,8 @@ def hook(module, *unused): handle = self._neighbor_allreduce_data_async(p) elif self._reduce_method == 2: handle = self._hierarchical_neighbor_allreduce_data_async(p) + elif self._reduce_method == -1: + handle = None else: raise ValueError( "Unknown reduce method. Do not change _reduce_method manually.") @@ -409,12 +411,16 @@ def use_neighbor_allreduce_in_communication(self): def use_hierarchical_neighbor_allreduce_in_communication(self): self._reduce_method = 2 + def use_empty_function_in_communication(self): + self._reduce_method = -1 + def synchronize(self): with torch.no_grad(): for p, handle in self._handles.items(): - output = bf.synchronize(handle) + if handle is not None: + output = bf.synchronize(handle) + p.set_(output) self._reduce_delay[p] = self._num_steps_per_communication - p.set_(output) self._handles.clear() self._synchronized = True From e869fc0d0bd52417f3fe2264aa54f81fe76db352 Mon Sep 17 00:00:00 2001 From: ybc Date: Sat, 31 Oct 2020 13:47:22 -0700 Subject: [PATCH 30/44] Add Hierarchical ops explanation in docs --- docs/_static/bf_hier_local_allreduce.png | Bin 0 -> 170899 bytes docs/_static/bf_hier_neighbor_allreduce.png | Bin 0 -> 168263 bytes docs/bluefog_ops.rst | 34 ++++++++++++++++++-- 3 files changed, 31 insertions(+), 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z=QH<#$$}0YlRVr3p0Sa(YME$$axMFg_wTVd;jAgthf-`aP&m<*RK-dM9`s^xbuI<+dmtGpRRiWypk8>5*SYUFONzk0PcRn z+F|a+f4SP<7jXv>b!xK+7kK@UXCFZY z{*HQP-qn1FTD$#})b;aK3pQY-^So0Gcdx?avTmYY};_s`F zyaylmH#(-t2$Lp#SQN(IIF@L>pA_X-&vkezkxWD5_dCj)72q}I}j0WHpW?JcZY9k`7(x2Q={P>YPXV_ANDDw9a%{(`?eEXOH@%X?tUiP}e@4OfnQ85lOlu4e$N z`ZYBC+lnx`5dOK5d^v(LAxV^IRZV`iu%eNa*}U;x_}DAHUNxi>EA$ex!k;^D>(o=< zn(Q)i#9Y|5oek3ur6Wo?12h#tKT1vv1ZQDBl!x>(q4+Z;0P6UpG_3s zc}bB3`}iwVeEo7q=Id$${XrK$uVdJem#Yld=Kx_EPa)#7COHqEZP12 zEC5R}QEB3Km959zE692LX{9Pn!@Fzl;28EPH=oDDWzT&T(S<)mD%~S(z#L1nG5eGF zKA!6ly0VG1A5WS7y6zwpe=2Em+vt0;!W0jYc_H5^X^LwarQ+%MJj4{@v*o5+`K4?* zKgk1{GhXp7`$hH Date: Sat, 31 Oct 2020 14:29:09 -0700 Subject: [PATCH 31/44] Update the document and remove som API --- bluefog/common/topology_util.py | 27 ++++++- bluefog/torch/__init__.py | 4 +- bluefog/torch/mpi_ops.py | 1 + bluefog/torch/optimizers.py | 106 +++++++++++++------------- bluefog/torch/utility.py | 2 +- docs/index.rst | 4 +- examples/pytorch_ImageNet_Resnet50.py | 2 +- examples/pytorch_benchmark.py | 23 ++---- examples/pytorch_cifar10_resnet.py | 21 +---- examples/pytorch_mnist.py | 21 +---- 10 files changed, 96 insertions(+), 115 deletions(-) diff --git a/bluefog/common/topology_util.py b/bluefog/common/topology_util.py index 73cc30f9..308e02c1 100644 --- a/bluefog/common/topology_util.py +++ b/bluefog/common/topology_util.py @@ -433,6 +433,23 @@ def GetDynamicSendRecvRanks( def GetExp2DynamicSendRecvMachineRanks( world_size: int, local_size: int, self_rank: int, local_rank: int ) -> Iterator[Tuple[List[int], List[int]]]: + """ + A utility function to generate 1-outgoing send machine id and corresponding recieving + machine id(s) for Exponentia-2 topology. + + Args: + world_size (int): the size of all nodes; world_size = num_machines * nodes_per_machine + local_size (int): number of nodes in each machine + self_rank (int): The self rank. + local_rank (int): The self local rank. + + Yields: + Iterator[Tuple[List[int], List[int]]]: send_machine_ids, recv_machine_ids. + + Warning: + This function should be used under homogeneous enviroment only, i.e. all machines have + the same number of local processes. + """ assert (self_rank % local_size) == local_rank, \ "It should be used under homogeneous environment only." assert (world_size % local_size) == 0, \ @@ -455,8 +472,9 @@ def GetExp2DynamicSendRecvMachineRanks( def GetInnerOuterRingDynamicSendRecvRanks( world_size: int, local_size: int, self_rank: int ) -> Iterator[Tuple[List[int], List[int]]]: - """A utility function to generate 1-outgoing send rank and corresponding recieving rank(s) - for Inner-Ring-Outer-Ring topology + """ + A utility function to generate 1-outgoing send rank and corresponding recieving rank(s) + for Inner-Ring-Outer-Ring topology. Args: world_size (int): the size of all nodes; world_size = num_machines * nodes_per_machine @@ -526,8 +544,9 @@ def GetInnerOuterRingDynamicSendRecvRanks( def GetInnerOuterExp2DynamicSendRecvRanks( world_size: int, local_size: int, self_rank: int ) -> Iterator[Tuple[List[int], List[int]]]: - """A utility function to generate 1-outgoing send rank and corresponding recieving rank(s) - for Inner-Exp2-Outer-Exp2 ring topology + """ + A utility function to generate 1-outgoing send rank and corresponding recieving rank(s) + for Inner-Exp2-Outer-Exp2 ring topology. Args: world_size (int): the size of all nodes; world_size = num_machines * nodes_per_machine diff --git a/bluefog/torch/__init__.py b/bluefog/torch/__init__.py index e656b054..706c592e 100644 --- a/bluefog/torch/__init__.py +++ b/bluefog/torch/__init__.py @@ -27,9 +27,7 @@ DistributedAllreduceOptimizer, DistributedNeighborAllreduceOptimizer, DistributedHierarchicalNeighborAllreduceOptimizer, - DistributedPushSumOptimizer, - DistributedPullGetOptimizer, - DistributedBluefogOptimizer) + DistributedWinPutOptimizer) check_extension('bluefog.torch', __file__, 'mpi_lib') diff --git a/bluefog/torch/mpi_ops.py b/bluefog/torch/mpi_ops.py index dd8e61bc..7d7765f2 100644 --- a/bluefog/torch/mpi_ops.py +++ b/bluefog/torch/mpi_ops.py @@ -917,6 +917,7 @@ def win_update_then_collect(name: str, require_mutex: bool = True) -> torch.Tens """ A utility function to sync the neighbor buffers then accumulate all neighbor buffers' tensors into self tensor and clear the buffer. It is equivalent to + >>> win_update(name, self_weight=1.0, neighbor_weights={neighbor: 1.0}, reset=True, require_mutex=require_mutex) diff --git a/bluefog/torch/optimizers.py b/bluefog/torch/optimizers.py index 5e63fd3b..3eae55b7 100644 --- a/bluefog/torch/optimizers.py +++ b/bluefog/torch/optimizers.py @@ -462,7 +462,7 @@ def step(self, closure=None): return super(self.__class__, self).step(closure) -class _DistributedBluefogOptimizer(torch.optim.Optimizer): +class _DistributedWinOptimizer(torch.optim.Optimizer): def __init__(self, params, model, num_steps_per_communication, pull_style): super(self.__class__, self).__init__(params) @@ -859,13 +859,13 @@ def DistributedPullGetOptimizer(optimizer, model, cls = type( optimizer.__class__.__name__, (optimizer.__class__,), - dict(_DistributedBluefogOptimizer.__dict__), + dict(_DistributedWinOptimizer.__dict__), ) return cls(optimizer.param_groups, model, num_steps_per_communication, pull_style=True) -def DistributedBluefogOptimizer(optimizer, model, - num_steps_per_communication=1): +def DistributedWinPutOptimizer(optimizer, model, + num_steps_per_communication=1): """An distributed optimizer that wraps another torch.optim.Optimizer with pull model average through bf.win_put ops. @@ -877,43 +877,17 @@ def DistributedBluefogOptimizer(optimizer, model, per num_steps_per_communication before reducing them over distributed computation resources. - Example for two scenarios to use num_steps_per_communication: - Scenario 1) Local accumulation of gradient without update model. - (Used in large batch size or large model cases) - >>> opt = bf.DistributedBluefogOptimizer(optimizer, model, num_steps_per_communication=J) - >>> opt.zero_grad() - >>> for j in range(J): - >>> output = model(data_batch_i) - >>> loss = ... - >>> loss.backward() - >>> opt.step() # Window operation happens here - Scenario 2) Local updating the model. (Used in case that decreasing the communication). - >>> opt = bf.DistributedBluefogOptimizer(optimizer, model, num_steps_per_communication=J) - >>> for j in range(J): - >>> output = model(data_batch_i) - >>> loss = ... - >>> opt.zero_grad() - >>> loss.backward() - >>> opt.step() # Window operation happens at the last iteration - Returned optimizer has two extra parameters `dst_weights` and `force_barrier`. Set dst_weights dictionary as {rank: scaling} differently per iteration to achieve win_put over dynamic graph behavior. If force_barrier is True, a barrier function will put at `step()` to synchronous processes. - - Example: - >>> import bluefog.torch as bf - >>> ... - >>> bf.init() - >>> optimizer = optim.SGD(model.parameters(), lr=lr * bf.size()) - >>> optimizer = bf.DistributedBluefogOptimizer(optimizer, model) """ # We dynamically create a new class that inherits from the optimizer that was passed in. # The goal is to override the `step()` method. cls = type( optimizer.__class__.__name__, (optimizer.__class__,), - dict(_DistributedBluefogOptimizer.__dict__), + dict(_DistributedWinOptimizer.__dict__), ) return cls(optimizer.param_groups, model, num_steps_per_communication, pull_style=False) @@ -932,9 +906,10 @@ def DistributedAllreduceOptimizer(optimizer, model, per num_steps_per_communication before reducing them over distributed computation resources. - Example for two scenarios to use num_steps_per_communication: + Example for two scenarios to use num_steps_per_communication. Scenario 1) Local accumulation of gradient without update model. (Used in large batch size or large model cases) + >>> opt = bf.DistributedAllreduceOptimizer(optimizer, model, num_steps_per_communication=J) >>> opt.zero_grad() @@ -943,7 +918,9 @@ def DistributedAllreduceOptimizer(optimizer, model, >>> loss = ... >>> loss.backward() >>> opt.step() # Allreducing happens here + Scenario 2) Local updating the model. (Used in case that decreasing the communication). + >>> opt = bf.DistributedAllreduceOptimizer(optimizer, model, num_steps_per_communication=J) >>> for j in range(J): @@ -969,9 +946,37 @@ def DistributedNeighborAllreduceOptimizer(optimizer, model, An distributed optimizer that wraps another torch.optim.Optimizer through neighbor_allreduce ops over parameters. - Returned optimizer has two extra parameters `self_weight` and `neighbor_weights`. - Set self_weight as some scalar and dst_weights dictionary as {rank: scaling} differently - per iteration to achieve win_put over dynamic graph behavior. + Returned optimizer has three extra parameters `self_weight`, `neighbor_weights` and + `send_neighbors` to control the behavior of neighbor allreduce. Changing the values + of these knobs to achieve dynamic topologies. + + Arguments: + optimizer: Optimizer to use for computing gradients and applying updates. + model: The model or a list of models you want to train with. + num_steps_per_communication: Number of expected model forward function calls before each + communication. This allows local model parameter updates + per num_steps_per_communication before reducing them over + distributed computation resources. + """ + # We dynamically create a new class that inherits from the optimizer that was passed in. + # The goal is to override the `step()` method with neighbor_allreduce implementation. + cls = type( + optimizer.__class__.__name__, + (optimizer.__class__,), + dict(_DistributedReduceOptimizer.__dict__), + ) + return cls(optimizer.param_groups, model, "neighbor.allreduce", num_steps_per_communication) + + +def DistributedHierarchicalNeighborAllreduceOptimizer(optimizer, model, + num_steps_per_communication=1): + """ + An distributed optimizer that wraps another torch.optim.Optimizer through + hierarchical_neighbor_allreduce ops over parameters. + + Returned optimizer has three extra parameters `self_weight`, `neighbor_machine_weights` and + `send_neighbor_machines` to control the behavior of hierarchical neighbor allreduce. Changing + the values of these knobs to achieve dynamic topologies. Arguments: optimizer: Optimizer to use for computing gradients and applying updates. @@ -981,20 +986,27 @@ def DistributedNeighborAllreduceOptimizer(optimizer, model, per num_steps_per_communication before reducing them over distributed computation resources. + Warning: + The processes within the same machine should provide the same `neighbor_machine_weights` and + `send_neighbor_machines` to avoid unexpected behavior. + Example for two scenarios to use num_steps_per_communication: Scenario 1) Local accumulation of gradient without update model. (Used in large batch size or large model cases) - >>> opt = bf.DistributedNeighborAllreduceOptimizer(optimizer, model, - num_steps_per_communication=J) + + >>> opt = bf.DistributedHierarchicalNeighborAllreduceOptimizer( + optimizer, model, num_steps_per_communication=J) >>> opt.zero_grad() >>> for j in range(J): >>> output = model(data_batch_i) >>> loss = ... >>> loss.backward() >>> opt.step() # Neighbor allreducing happens here + Scenario 2) Local updating the model. (Used in case that decreasing the communication). - >>> opt = bf.DistributedNeighborAllreduceOptimizer(optimizer, model, - num_steps_per_communication=J) + + >>> opt = bf.DistributedHierarchicalNeighborAllreduceOptimizer( + optimizer, model, num_steps_per_communication=J) >>> for j in range(J): >>> output = model(data_batch_i) >>> loss = ... @@ -1002,18 +1014,6 @@ def DistributedNeighborAllreduceOptimizer(optimizer, model, >>> loss.backward() >>> opt.step() # Neighbor allreducing happens at the last iteration """ - # We dynamically create a new class that inherits from the optimizer that was passed in. - # The goal is to override the `step()` method with neighbor_allreduce implementation. - cls = type( - optimizer.__class__.__name__, - (optimizer.__class__,), - dict(_DistributedReduceOptimizer.__dict__), - ) - return cls(optimizer.param_groups, model, "neighbor.allreduce", num_steps_per_communication) - - -def DistributedHierarchicalNeighborAllreduceOptimizer(optimizer, model, - num_steps_per_communication=1): cls = type( optimizer.__class__.__name__, (optimizer.__class__,), @@ -1039,8 +1039,10 @@ def DistributedGradientAllreduceOptimizer(optimizer, model, distributed computation resources. Example for two scenarios to use num_steps_per_communication: + Scenario 1) Local accumulation of gradient without update model. (Used in large batch size or large model cases) + >>> opt = bf.DistributedGradientAllreduceOptimizer(optimizer, model, >>> num_steps_per_communication=J) >>> opt.zero_grad() @@ -1049,7 +1051,9 @@ def DistributedGradientAllreduceOptimizer(optimizer, model, >>> loss = ... >>> loss.backward() >>> opt.step() # Allreducing happens here + Scenario 2) Local updating the model. (Used in case that decreasing the communication). + >>> opt = bf.DistributedGradientAllreduceOptimizer(optimizer, model, >>> num_steps_per_communication=J) >>> for j in range(J): diff --git a/bluefog/torch/utility.py b/bluefog/torch/utility.py index ea94538d..15a5f343 100644 --- a/bluefog/torch/utility.py +++ b/bluefog/torch/utility.py @@ -115,7 +115,7 @@ def broadcast_optimizer_state(optimizer, root_rank): super(optimizer.__class__, optimizer).step() elif optimizer.__module__ == bf.DistributedPullGetOptimizer.__module__: super(optimizer.__class__, optimizer).step() - elif optimizer.__module__ == bf.DistributedBluefogOptimizer.__module__: + elif optimizer.__module__ == bf.DistributedWinPutOptimizer.__module__: super(optimizer.__class__, optimizer).step() else: optimizer.step() diff --git a/docs/index.rst b/docs/index.rst index 38628fc3..a377419f 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -44,7 +44,7 @@ the :ref:`install_bluefog` page if you need more information or other install op We provide high-level wrapper for torch optimizer. Probably, the only thing you need to modify the existing script to distributed implementation is wrapping the optimizer -with our ``DistributedBluefogOptimizer``, +with our ``DistributedNeighborAllreduceOptimizer``, then run it through ``bfrun``. That is it! .. code-block:: python @@ -57,7 +57,7 @@ then run it through ``bfrun``. That is it! ... bf.init() optimizer = optim.SGD(model.parameters(), lr=lr * bf.size()) - optimizer = bf.DistributedBluefogOptimizer( + optimizer = bf.DistributedNeighborAllreduceOptimizer( optimizer, model ) ... diff --git a/examples/pytorch_ImageNet_Resnet50.py b/examples/pytorch_ImageNet_Resnet50.py index 3aabdb0c..9bc2ed2d 100644 --- a/examples/pytorch_ImageNet_Resnet50.py +++ b/examples/pytorch_ImageNet_Resnet50.py @@ -182,7 +182,7 @@ # Bluefog: wrap optimizer with DistributedOptimizer. # TODO(hhb) Pass batches_per_allreduce to optimizer. if args.dist_optimizer == 'win_put': - optimizer = bf.DistributedBluefogOptimizer(optimizer, model=model) + optimizer = bf.DistributedWinPutOptimizer(optimizer, model=model) elif args.dist_optimizer == 'neighbor_allreduce': optimizer = optimizer = bf.DistributedNeighborAllreduceOptimizer( optimizer, model=model) diff --git a/examples/pytorch_benchmark.py b/examples/pytorch_benchmark.py index a242bb3f..03c22e1c 100644 --- a/examples/pytorch_benchmark.py +++ b/examples/pytorch_benchmark.py @@ -53,10 +53,10 @@ help='disables profiler') parser.add_argument('--partition', type=int, default=None, help='partition size') -parser.add_argument('--dist-optimizer', type=str, default='win_put', +parser.add_argument('--dist-optimizer', type=str, default='neighbor_allreduce', help='The type of distributed optimizer. Supporting options are ' + - '[win_put(default), neighbor_allreduce, allreduce, ' + - 'hierarchical_neighbor_allreduce, pull_get, push_sum, horovod]') + '[win_put, neighbor_allreduce, allreduce, ' + + 'hierarchical_neighbor_allreduce, horovod]') parser.add_argument('--disable-dynamic-topology', action='store_true', default=False, help=('Disable each iteration to transmit one neighbor ' + 'per iteration dynamically.')) @@ -130,7 +130,7 @@ def forward(self, x): # Bluefog: wrap optimizer with DistributedOptimizer. if args.dist_optimizer == 'win_put': - optimizer = bf.DistributedBluefogOptimizer(optimizer, model=model) + optimizer = bf.DistributedWinPutOptimizer(optimizer, model=model) elif args.dist_optimizer == 'neighbor_allreduce': optimizer = optimizer = bf.DistributedNeighborAllreduceOptimizer( optimizer, model=model) @@ -140,8 +140,6 @@ def forward(self, x): elif args.dist_optimizer == 'gradient_allreduce': optimizer = optimizer = bf.DistributedGradientAllreduceOptimizer( optimizer, model=model) -elif args.dist_optimizer == 'push_sum': - optimizer = bf.DistributedPushSumOptimizer(optimizer, model=model) elif args.dist_optimizer == 'hierarchical_neighbor_allreduce': optimizer = optimizer = bf.DistributedHierarchicalNeighborAllreduceOptimizer( optimizer, model=model) @@ -149,13 +147,11 @@ def forward(self, x): optimizer = optimizer = bf.DistributedOptimizer( optimizer, named_parameters=model.named_parameters() ) -elif args.dist_optimizer == 'pull_get': - optimizer = bf.DistributedPullGetOptimizer(optimizer, model=model) else: raise ValueError('Unknown args.dist-optimizer type -- ' + args.dist_optimizer + '\n' + 'Please set the argument to be one of ' + '[neighbor_allreduce, gradient_allreduce, allreduce, ' + - 'win_put, push_sum, horovod]') + 'win_put, horovod]') bf.broadcast_parameters(model.state_dict(), root_rank=0) bf.broadcast_optimizer_state(optimizer, root_rank=0) @@ -208,15 +204,6 @@ def dynamic_topology_update(batch_idx): num_out_neighbors = len(bf.out_neighbor_ranks()) sent_neighbor = bf.out_neighbor_ranks()[batch_idx % num_out_neighbors] optimizer.dst_weights = {sent_neighbor: 1.0} - elif args.dist_optimizer == 'pull_get': - num_in_neighbors = len(bf.in_neighbor_ranks()) - recv_neighbor = bf.in_neighbor_ranks()[batch_idx % num_in_neighbors] - optimizer.src_weights = {recv_neighbor: 1.0} - elif args.dist_optimizer == 'push_sum': - num_out_neighbors = len(bf.out_neighbor_ranks()) - sent_neighbor = bf.out_neighbor_ranks()[batch_idx % num_out_neighbors] - optimizer.dst_weights = {sent_neighbor: 0.5} - optimizer.self_weight = 0.5 elif args.dist_optimizer == 'neighbor_allreduce': send_neighbors, recv_neighbors = next(dynamic_neighbor_allreduce_gen) optimizer.send_neighbors = send_neighbors diff --git a/examples/pytorch_cifar10_resnet.py b/examples/pytorch_cifar10_resnet.py index e801d30a..32bdd1cc 100644 --- a/examples/pytorch_cifar10_resnet.py +++ b/examples/pytorch_cifar10_resnet.py @@ -74,7 +74,7 @@ "--no-cuda", action="store_true", default=False, help="disables CUDA training" ) parser.add_argument("--seed", type=int, default=42, help="random seed") -parser.add_argument('--dist-optimizer', type=str, default='hierarchical_neighbor_allreduce', +parser.add_argument('--dist-optimizer', type=str, default='neighbor_allreduce', help='The type of distributed optimizer. Supporting options are ' + '[win_put, neighbor_allreduce, allreduce, pull_get, hierarchical_neighbor_allreduce, horovod]') parser.add_argument("--average-test-result", action="store_true", @@ -216,7 +216,7 @@ # Bluefog: wrap optimizer with DistributedOptimizer. if args.dist_optimizer == 'win_put': - optimizer = bf.DistributedBluefogOptimizer(optimizer, model=model) + optimizer = bf.DistributedWinPutOptimizer(optimizer, model=model) elif args.dist_optimizer == 'neighbor_allreduce': optimizer = optimizer = bf.DistributedNeighborAllreduceOptimizer( optimizer, model=model) @@ -229,8 +229,6 @@ elif args.dist_optimizer == 'hierarchical_neighbor_allreduce': optimizer = optimizer = bf.DistributedHierarchicalNeighborAllreduceOptimizer( optimizer, model=model) -elif args.dist_optimizer == 'push_sum': - optimizer = bf.DistributedPushSumOptimizer(optimizer, model=model) elif args.dist_optimizer == 'horovod': optimizer = optimizer = bf.DistributedOptimizer( optimizer, named_parameters=model.named_parameters() @@ -241,7 +239,7 @@ raise ValueError('Unknown args.dist-optimizer type -- ' + args.dist_optimizer + '\n' + 'Please set the argument to be one of ' + '[neighbor_allreduce, gradient_allreduce, allreduce, ' + - 'win_put, push_sum, horovod]') + 'win_put, horovod]') print("resume_from_epoch: ", resume_from_epoch) # Restore from a previous checkpoint, if initial_epoch is specified. @@ -378,19 +376,6 @@ def dynamic_topology_update(epoch, batch_idx): num_out_neighbors = len(bf.out_neighbor_ranks()) sent_neighbor = bf.out_neighbor_ranks()[batch_idx % num_out_neighbors] optimizer.dst_weights = {sent_neighbor: 1.0} - elif args.dist_optimizer == 'pull_get': - if epoch < 3: - return - num_in_neighbors = len(bf.in_neighbor_ranks()) - recv_neighbor = bf.in_neighbor_ranks()[batch_idx % num_in_neighbors] - optimizer.src_weights = {recv_neighbor: 1.0} - elif args.dist_optimizer == 'push_sum': - if epoch < 3: - return - num_out_neighbors = len(bf.out_neighbor_ranks()) - sent_neighbor = bf.out_neighbor_ranks()[batch_idx % num_out_neighbors] - optimizer.dst_weights = {sent_neighbor: 0.5} - optimizer.self_weight = 0.5 elif args.dist_optimizer == 'neighbor_allreduce': send_neighbors, recv_neighbors = next(dynamic_neighbor_allreduce_gen) optimizer.send_neighbors = send_neighbors diff --git a/examples/pytorch_mnist.py b/examples/pytorch_mnist.py index a80d97ed..358b2111 100644 --- a/examples/pytorch_mnist.py +++ b/examples/pytorch_mnist.py @@ -55,9 +55,9 @@ parser.add_argument( "--no-cuda", action="store_true", default=False, help="disables CUDA training" ) -parser.add_argument('--dist-optimizer', type=str, default='win_put', +parser.add_argument('--dist-optimizer', type=str, default='neighbor_allreduce', help='The type of distributed optimizer. Supporting options are ' + - '[win_put, neighbor_allreduce, allreduce, pull_get, push_sum, horovod]') + '[win_put, neighbor_allreduce, allreduce, horovod]') parser.add_argument("--average-test-result", action="store_true", default=False, help=("Allreduce called to average test result. Warning this will " + @@ -193,7 +193,7 @@ def forward(self, x): # Bluefog: wrap optimizer with DistributedOptimizer. if args.dist_optimizer == 'win_put': - optimizer = bf.DistributedBluefogOptimizer(optimizer, model=model) + optimizer = bf.DistributedWinPutOptimizer(optimizer, model=model) elif args.dist_optimizer == 'neighbor_allreduce': optimizer = optimizer = bf.DistributedNeighborAllreduceOptimizer( optimizer, model=model) @@ -206,8 +206,6 @@ def forward(self, x): elif args.dist_optimizer == 'hierarchical_neighbor_allreduce': optimizer = optimizer = bf.DistributedHierarchicalNeighborAllreduceOptimizer( optimizer, model=model) -elif args.dist_optimizer == 'push_sum': - optimizer = bf.DistributedPushSumOptimizer(optimizer, model=model) elif args.dist_optimizer == 'horovod': optimizer = optimizer = bf.DistributedOptimizer( optimizer, named_parameters=model.named_parameters() @@ -218,7 +216,7 @@ def forward(self, x): raise ValueError('Unknown args.dist-optimizer type -- ' + args.dist_optimizer + '\n' + 'Please set the argument to be one of ' + '[neighbor_allreduce, gradient_allreduce, allreduce, ' + - 'win_put, push_sum, horovod]') + 'win_put, horovod]') if not args.disable_dynamic_topology and (args.dist_optimizer != 'horovod'): if args.virtual_topology == 'InnerOuterRing': @@ -250,17 +248,6 @@ def dynamic_topology_update(epoch, batch_idx): num_out_neighbors = len(bf.out_neighbor_ranks()) sent_neighbor = bf.out_neighbor_ranks()[batch_idx % num_out_neighbors] optimizer.dst_weights = {sent_neighbor: 1.0} - elif args.dist_optimizer == 'pull_get': - if epoch < 3: - return - num_in_neighbors = len(bf.in_neighbor_ranks()) - recv_neighbor = bf.in_neighbor_ranks()[batch_idx % num_in_neighbors] - optimizer.src_weights = {recv_neighbor: 1.0} - elif args.dist_optimizer == 'push_sum': - num_out_neighbors = len(bf.out_neighbor_ranks()) - sent_neighbor = bf.out_neighbor_ranks()[batch_idx % num_out_neighbors] - optimizer.dst_weights = {sent_neighbor: 0.5} - optimizer.self_weight = 0.5 elif args.dist_optimizer == 'neighbor_allreduce': send_neighbors, recv_neighbors = next(dynamic_neighbor_allreduce_gen) optimizer.send_neighbors = send_neighbors From 2fbab44179879f31d3420c6c35c6047ca6bf8aa9 Mon Sep 17 00:00:00 2001 From: ybc Date: Sat, 31 Oct 2020 14:54:59 -0700 Subject: [PATCH 32/44] Simplify the examples folder --- examples/pytorch_nonconvex_logitReg.py | 315 ------------------ examples/tensorflow2_mnist.py | 92 ----- .../pytorch_opt_linear_speedup_test.py | 0 3 files changed, 407 deletions(-) delete mode 100644 examples/pytorch_nonconvex_logitReg.py delete mode 100644 examples/tensorflow2_mnist.py rename {examples => scripts}/pytorch_opt_linear_speedup_test.py (100%) diff --git a/examples/pytorch_nonconvex_logitReg.py b/examples/pytorch_nonconvex_logitReg.py deleted file mode 100644 index 4b8f2c21..00000000 --- a/examples/pytorch_nonconvex_logitReg.py +++ /dev/null @@ -1,315 +0,0 @@ -# Copyright 2020 Bluefog Team. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== - -import torch -import matplotlib.pyplot as plt -import argparse - -import bluefog.torch as bf -from bluefog.common import topology_util - -# Parser -parser = argparse.ArgumentParser( - description="PyTorch ImageNet Example", - formatter_class=argparse.ArgumentDefaultsHelpFormatter, -) -parser.add_argument( - "--save-plot-file", default='plot.png', help="Saving the plot in the file." -) -parser.add_argument( - "--plot-interactive", action='store_true', help="Use plt.show() to present the plot." -) -parser.add_argument( - "--max-iter", action='store', type=int, default=1000, help="Use plt.show() to present the plot." -) -parser.add_argument( - "--method", help="this example supports exact_diffusion, gradient_tracking, and push_diging", - default='exact_diffusion' -) -args = parser.parse_args() - - -def finalize_plot(): - plt.savefig(args.save_plot_file) - if args.plot_interactive: - plt.show() - plt.close() - - -def logistic_loss_step(x_, rho, beta, tensor_name): - """Calculate gradient of logistic loss via pytorch autograd.""" - with bf.timeline_context(tensor_name=tensor_name, - activity_name="gradient computation"): - loss_ = torch.mean(torch.log(1 + torch.exp(-y*X.mm(x_)))) \ - + torch.sum(rho*beta*x_*x_/(1+beta*x_*x_)) - loss_.backward() - return loss_ - -# # ================== Distributed gradient descent ================================ -# # Calculate the solution with distributed gradient descent: -# # x^{k+1} = x^k - alpha * allreduce(local_grad) -# # it will be used to verify the solution of various decentralized algorithms. -# # ================================================================================ -def distributed_grad_descent(rho, beta, maxite=5000, alpha=1e-1): - w_opt = torch.zeros(n, 1, dtype=torch.double, requires_grad=True) - - for i in range(maxite): - # calculate gradient via pytorch autograd - logistic_loss_step(w_opt, rho, beta, tensor_name='allreduce.gradient') - # global gradient - grad = bf.allreduce(w_opt.grad.data, name='gradient') - - # distributed gradient descent - w_opt.data = w_opt.data - alpha*grad - w_opt.grad.data.zero_() - - logistic_loss_step(w_opt, rho, beta, tensor_name='allreduce.gradient') - grad = bf.allreduce(w_opt.grad.data, name='gradient') # global gradient - - # evaluate the convergence of distributed logistic regression - # the norm of global gradient is expected to 0 (optimality condition) - global_grad_norm = torch.norm(grad, p=2) - print("[DG] Rank {}: global gradient norm: {}".format( - bf.rank(), global_grad_norm)) - - # the norm of local gradient is expected not be be close to 0 - # this is because each rank converges to global solution, not local solution - local_grad_norm = torch.norm(w_opt.grad.data, p=2) - print("[DG] Rank {}: local gradient norm: {}".format( - bf.rank(), local_grad_norm)) - - return w_opt - - -# ==================== Exact Diffusion =========================================== -# Calculate the true solution with exact diffusion recursion as follows: -# -# psi^{k+1} = w^k - alpha * grad(w^k) -# phi^{k+1} = psi^{k+1} + w^k - psi^{k} -# w^{k+1} = neighbor_allreduce(phi^{k+1}) -# -# Reference: -# -# [R1] K. Yuan, B. Ying, X. Zhao, and A. H. Sayed, ``Exact diffusion for distributed -# optimization and learning -- Part I: Algorithm development'', 2018. (Alg. 1) -# link: https://arxiv.org/abs/1702.05122 -# -# [R2] Z. Li, W. Shi and M. Yan, ``A Decentralized Proximal-gradient Method with -# Network Independent Step-sizes and Separated Convergence Rates'', 2019 -# ================================================================================ -def exact_diffusion(w_opt, rho, beta, maxite=2000, alpha_ed=1e-1, use_Abar=False): - w = torch.zeros(n, 1, dtype=torch.double, requires_grad=True) - phi, psi, psi_prev = w.clone(), w.clone(), w.clone() - mse = [] - - topology = bf.load_topology() - self_weight, neighbor_weights = topology_util.GetRecvWeights( - topology, bf.rank()) - - # construct A_bar - if use_Abar: - self_weight = (self_weight+1)/2 - for k, v in neighbor_weights.items(): - neighbor_weights[k] = v/2 - - # if bf.rank() == 0: - # for k, v in neighbor_weights.items(): - # print(neighbor_weights[k]) - - for i in range(maxite): - # calculate loccal gradient via pytorch autograd - logistic_loss_step( - w, rho, beta, tensor_name='neighbor.allreduce.local_variable') - - # exact diffusion - psi = w - alpha_ed * w.grad.data - phi = psi + w.data - psi_prev - w.data = bf.neighbor_allreduce( - phi, self_weight, neighbor_weights, name='local variable') - psi_prev = psi.clone() - w.grad.data.zero_() - - # record convergence - if bf.rank() == 0: - mse.append(torch.norm(w.data - w_opt.data, p=2)) - - return w, mse - -# ======================= gradient tracking ===================================== -# Calculate the true solution with gradient tracking (GT for short): -# -# w^{k+1} = neighbor_allreduce(w^k) - alpha*q^k -# q^{k+1} = neighbor_allreduce(q^k) + grad(w^{k+1}) - grad(w^k) -# where q^0 = grad(w^0) -# -# Reference: -# [R1] A. Nedic, A. Olshevsky, and W. Shi, ``Achieving geometric convergence -# for distributed optimization over time-varying graphs'', 2017. (Alg. 1) -# -# [R2] G. Qu and N. Li, ``Harnessing smoothness to accelerate distributed -# optimization'', 2018 -# -# [R3] J. Xu et.al., ``Augmented distributed gradient methods for multi-agent -# optimization under uncoordinated constant stepsizes'', 2015 -# -# [R4] P. Di Lorenzo and G. Scutari, ``Next: In-network nonconvex optimization'', -# 2016 -# ================================================================================ -def gradient_tracking(w_opt, rho, beta, maxite=2000, alpha_gt=1e-1): - w = torch.zeros(n, 1, dtype=torch.double, requires_grad=True) - logistic_loss_step( - w, rho, beta, tensor_name='neighbor.allreduce.Grad.Tracking.w') - q = w.grad.data # q^0 = grad(w^0) - w.grad.data.zero_() - - grad_prev = q.clone() - mse_gt = [] - for i in range(maxite): - - # Algorithm: - # w^{k+1} = neighbor_allreduce(w^k) - alpha*q^k - # q^{k+1} = neighbor_allreduce(q^k) + grad(w^{k+1}) - grad(w^k) - - # Notice the communication of neighbor_allreduce can overlap with gradient computation. - w_handle = bf.neighbor_allreduce_nonblocking(w.data, name='Grad.Tracking.w') - q_handle = bf.neighbor_allreduce_nonblocking(q, name='Grad.Tracking.q') - w.data = bf.synchronize(w_handle) - alpha_gt * q - # calculate local gradient - logistic_loss_step( - w, rho, beta, tensor_name='neighbor.allreduce.Grad.Tracking.w') - grad = w.grad.data.clone() - q = bf.synchronize(q_handle) + grad - grad_prev - grad_prev = grad - w.grad.data.zero_() - - # record convergence - if bf.rank() == 0: - mse_gt.append(torch.norm(w.data - w_opt.data, p=2)) - - return w, mse_gt - -# ======================= Push-DIGing for directed graph ======================= -# Calculate the true solution with Push-DIGing: -# -# Reference: -# -# [R1] A. Nedic, A. Olshevsky, and W. Shi, ``Achieving geometric convergence -# for distributed optimization over time-varying graphs'', 2017. (Alg. 2) -# ============================================================================ -def push_diging(w_opt, rho, beta, maxite=2000, alpha_pd=1e-1): - bf.set_topology(topology_util.PowerGraph(bf.size())) - outdegree = len(bf.out_neighbor_ranks()) - indegree = len(bf.in_neighbor_ranks()) - - # u, y, v = w[:n], w[n:2*n], w[2n] - w = torch.zeros(2*n+1, 1).to(torch.double) - x = torch.zeros(n, 1, dtype=torch.double, requires_grad=True) - - logistic_loss_step(x, rho, beta, tensor_name='w_buff') - grad = x.grad.data.clone() - w[n:2*n] = grad - x.grad.data.zero_() - - w[-1] = 1.0 - grad_prev = w[n:2*n].clone() - - bf.win_create(w, name="w_buff", zero_init=True) - - mse_pd = [] - for i in range(maxite): - w[:n] = w[:n] - alpha_pd*w[n:2*n] - bf.win_accumulate( - w, name="w_buff", - dst_weights={rank: 1.0 / (outdegree*2) - for rank in bf.out_neighbor_ranks()}, - require_mutex=True) - w.div_(2) - w = bf.win_update_then_collect(name="w_buff") - - x.data = w[:n]/w[-1] - logistic_loss_step(x, rho, beta, tensor_name='w_buff') - grad = x.grad.data.clone() - x.grad.data.zero_() - - w[n:2*n] += grad - grad_prev - grad_prev = grad - if i % 10 == 0: - bf.barrier() - if bf.rank() == 0: - mse_pd.append(torch.norm(x.data - w_opt, p=2)) - - bf.barrier() - w = bf.win_update_then_collect(name="w_buff") - x.data = w[:n]/w[-1] - - return x, mse_pd - - -# ======================= Code starts here ======================= -bf.init() - -# The logistic regression problem is -# min_w (1/n)*\sum_i ln(1 + exp(-y_i*X_i'*w)) + 0.5*rho*|w|^2 -# where each rank i holds a local dataset (X_i, y_i). -# In (X_i, y_i), X_i is data and y_i is lable. -# We expect each rank will converge to the global solution after the algorithm - -# Generate data for logistic regression (synthesized data) -torch.random.manual_seed(123417 * bf.rank()) -m, n = 20, 5 -X = torch.randn(m, n).to(torch.double) -w_0 = torch.randn(n, 1).to(torch.double) -y = torch.rand(m, 1).to(torch.double) < 1 / (1+torch.exp(X.mm(w_0))) -y = y.double() -y = 2*y - 1 -rho = 1e-3 -beta = 1 - -# calculate the global solution w_opt via distributed gradient descent -w_opt = distributed_grad_descent(rho, beta) - -# solve the logistic regression with indicated decentralized algorithms -if args.method == 'exact_diffusion': - w, mse = exact_diffusion(w_opt, rho, beta, maxite=args.max_iter) -elif args.method == 'gradient_tracking': - w, mse = gradient_tracking(w_opt, rho, beta, maxite=args.max_iter) -elif args.method == 'push_diging': - w, mse = push_diging(w_opt, rho, beta, maxite=args.max_iter) -else: - raise NotImplementedError( - 'Algorithm not support. This example only supports' + - ' exact_diffusion, gradient_tracking, and push_diging' - ) - -# plot and print result -if bf.rank() == 0: - plt.semilogy(mse) - finalize_plot() - -# calculate local and global gradient -logistic_loss_step(w, rho, beta, tensor_name="w_buff") -grad = bf.allreduce(w.grad.data, name='gradient') # global gradient - -# evaluate the convergence of gradient tracking for logistic regression -# the norm of global gradient is expected to be 0 (optimality condition) -global_grad_norm = torch.norm(grad, p=2) -print("[{}] Rank {}: global gradient norm: {}".format( - args.method, bf.rank(), global_grad_norm)) - -# the norm of local gradient is expected not to be close to 0 -# this is because each rank converges to global solution, not local solution -local_grad_norm = torch.norm(w.grad.data, p=2) -print("[{}] Rank {}: local gradient norm: {}".format( - args.method, bf.rank(), local_grad_norm)) diff --git a/examples/tensorflow2_mnist.py b/examples/tensorflow2_mnist.py deleted file mode 100644 index 258d8522..00000000 --- a/examples/tensorflow2_mnist.py +++ /dev/null @@ -1,92 +0,0 @@ -# Modifications copyright (C) 2020 Bluefog Team. All Rights Reserved. -# Copyright 2019 Uber Technologies, Inc. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== - -import tensorflow as tf -import bluefog.tensorflow as bf - -# Bluefog: initialize Bluefog. -bf.init() - -# Bluefog: pin GPU to be used to process local rank (one GPU per process) -gpus = tf.config.experimental.list_physical_devices('GPU') -for gpu in gpus: - tf.config.experimental.set_memory_growth(gpu, True) -if gpus: - tf.config.experimental.set_visible_devices(gpus[bf.local_rank()], 'GPU') - -(mnist_images, mnist_labels), _ = \ - tf.keras.datasets.mnist.load_data(path='mnist-%d.npz' % bf.rank()) - -dataset = tf.data.Dataset.from_tensor_slices( - (tf.cast(mnist_images[..., tf.newaxis] / 255.0, tf.float32), - tf.cast(mnist_labels, tf.int64)) -) -dataset = dataset.repeat().shuffle(10000).batch(128) - -mnist_model = tf.keras.Sequential([ - tf.keras.layers.Conv2D(32, [3, 3], activation='relu'), - tf.keras.layers.Conv2D(64, [3, 3], activation='relu'), - tf.keras.layers.MaxPooling2D(pool_size=(2, 2)), - tf.keras.layers.Dropout(0.25), - tf.keras.layers.Flatten(), - tf.keras.layers.Dense(128, activation='relu'), - tf.keras.layers.Dropout(0.5), - tf.keras.layers.Dense(10, activation='softmax') -]) -loss = tf.losses.SparseCategoricalCrossentropy() - -# Bluefog: adjust learning rate based on number of GPUs. -opt = tf.optimizers.SGD(0.01 * bf.size()) - - -@tf.function -def training_step(imgs, labels_, is_fisrt_batch): - with tf.GradientTape() as tape: - probs = mnist_model(imgs, training=True) - _loss_value = loss(labels_, probs) - tf.print("_loss_value: ", _loss_value) - - # Bluefog: add Bluefog Distributed GradientTape. - tape = bf.DistributedGradientTape(tape) - - grads = tape.gradient(_loss_value, mnist_model.trainable_variables) - opt.apply_gradients(zip(grads, mnist_model.trainable_variables)) - # Bluefog: broadcast initial variable states from rank 0 to all other processes. - # This is necessary to ensure consistent initialization of all workers when - # training is started with random weights or restored from a checkpoint. - # - # Note: broadcast should be done after the first gradient step to ensure optimizer - # initialization. - if is_fisrt_batch: - bf.broadcast_variables( - mnist_model.variables + opt.variables(), root_rank=0) - tf.print("broadcast is Done!") - - return _loss_value - - -tf.config.experimental_run_functions_eagerly(False) - -#TODO(ybc) Unfortunately, the tensorflow code won't work because the tensorflow is triggered -# through the graph flow engine so that the parallel ops can occur with any order. The mismatch -# of allreduce/broadcast order will cause the procedure crash or hang. -# One-sided ops can avoid this problem in theory. - -# Bluefog: adjust number of steps based on number of GPUs. -for batch, (images, labels) in enumerate(dataset.take(10000 // bf.size())): - loss_value = training_step(images, labels, batch == 0) - if batch % 2 == 0 and bf.local_rank() == 0: - print('Step #%d\tLoss: %.6f' % (batch, loss_value)) diff --git a/examples/pytorch_opt_linear_speedup_test.py b/scripts/pytorch_opt_linear_speedup_test.py similarity index 100% rename from examples/pytorch_opt_linear_speedup_test.py rename to scripts/pytorch_opt_linear_speedup_test.py From a6228c2c90268eaf59619627fcfbd68385e920be Mon Sep 17 00:00:00 2001 From: ybc Date: Sat, 31 Oct 2020 16:50:38 -0700 Subject: [PATCH 33/44] Add hier n_ar for mpi case --- bluefog/common/mpi_controller.cc | 111 ++++++++++++++++++++----------- 1 file changed, 73 insertions(+), 38 deletions(-) diff --git a/bluefog/common/mpi_controller.cc b/bluefog/common/mpi_controller.cc index aa62efef..c3b6eac5 100644 --- a/bluefog/common/mpi_controller.cc +++ b/bluefog/common/mpi_controller.cc @@ -299,6 +299,12 @@ bool CheckNeighborSendRecvPattern(int size, const TensorTableEntry& entry, // enabled the check if enable_topo_check is true and partial // neighbor_allreduce is activated. if (entry.enable_topo_check && !entry.send_neighbors->empty()) { + if (entry.is_hierarchical) { + // TODO: support check. + BFLOG(INFO) << "Request to check topology for hierarchical neighbor " + << "allreduce ops but it is not supported yet."; + return res; + } timeline_ptr->ActivityStart(entry.tensor_name, "NEGOTIATION"); // Put all the send and recv neighbors in a single vector, and obtain a send // matrix and a recv matrix through MPI_Allgather. @@ -363,11 +369,6 @@ std::string GenerateNeighborAllreduceErrorMessage(const std::vector& void MPIController::NeighborAllreduce(TensorTableEntry& entry) { const void* sendbuf = entry.tensor->data(); int num_elements = entry.tensor->shape().num_elements(); - // TODO(ybc) support this - if (entry.is_hierarchical) { - throw std::runtime_error( - "hierarchical neighbor_allreduce is only supported under NCCL case."); - } Timeline* timeline_ptr; Status timeline_status = GetBluefogTimeline(timeline_ptr); @@ -401,44 +402,78 @@ void MPIController::NeighborAllreduce(TensorTableEntry& entry) { // is no need to transfer the local info again. However, for computation view, // including itself is more intuitive. std::string error_message = ""; - if (entry.send_neighbors->empty()) { - int ret_code = MPI_Neighbor_allgather( - sendbuf, num_elements, mpi_ctx_.GetMPIDataType(entry.tensor), buffer_data, - num_elements, mpi_ctx_.GetMPIDataType(entry.output), - mpi_ctx_.GetMPICommunicator(Communicator::GRAPH)); - if (ret_code != MPI_SUCCESS) { - throw std::runtime_error( - "MPI_Neighbor_allreduce (through neighbor_allgather) failed, see MPI " - "output for details."); - } - } else { - int nsend = entry.send_neighbors->size(); - int nrecv = entry.recv_neighbors->size(); - std::vector requests(nsend+nrecv); - std::vector statuses(nsend+nrecv); - int element_size = mpi_ctx_.GetMPITypeSize(entry.output->dtype()); - for (int i = 0; i < nrecv; ++i) { - void* recvbuf = (void*)(static_cast(entry.output->data()) - +num_elements*i*element_size); - int ret_code = MPI_Irecv(recvbuf, num_elements, mpi_ctx_.GetMPIDataType(entry.output), - entry.recv_neighbors->at(i), mpi_ctx_.rank_+entry.recv_neighbors->at(i), - mpi_ctx_.GetMPICommunicator(Communicator::GRAPH), &requests[i+nsend]); + + if (!entry.is_hierarchical) { + if (entry.send_neighbors->empty()) { + int ret_code = MPI_Neighbor_allgather( + sendbuf, num_elements, mpi_ctx_.GetMPIDataType(entry.tensor), + buffer_data, num_elements, mpi_ctx_.GetMPIDataType(entry.output), + mpi_ctx_.GetMPICommunicator(Communicator::GRAPH)); if (ret_code != MPI_SUCCESS) { throw std::runtime_error( - "MPI_Irecv (for dynamic neighbor_allreduce) failed, see MPI output for details."); + "MPI_Neighbor_allreduce (through neighbor_allgather) failed, see " + "MPI " + "output for details."); } - } - for (int i = 0; i < nsend; ++i) { - int ret_code = MPI_Isend(sendbuf, num_elements, mpi_ctx_.GetMPIDataType(entry.tensor), - entry.send_neighbors->at(i), mpi_ctx_.rank_+entry.send_neighbors->at(i), - mpi_ctx_.GetMPICommunicator(Communicator::GRAPH), &requests[i]); - if (ret_code != MPI_SUCCESS) { - throw std::runtime_error( - "MPI_Isend (for dynamic neighbor_allreduce) failed, see MPI output for details."); + } else { + int nsend = entry.send_neighbors->size(); + int nrecv = entry.recv_neighbors->size(); + std::vector requests(nsend + nrecv); + std::vector statuses(nsend + nrecv); + int element_size = mpi_ctx_.GetMPITypeSize(entry.output->dtype()); + for (int i = 0; i < nrecv; ++i) { + void* recvbuf = (void*)(static_cast(entry.output->data()) + + num_elements * i * element_size); + int ret_code = MPI_Irecv( + recvbuf, num_elements, mpi_ctx_.GetMPIDataType(entry.output), + entry.recv_neighbors->at(i), + mpi_ctx_.rank_ + entry.recv_neighbors->at(i), + mpi_ctx_.GetMPICommunicator(Communicator::GRAPH), + &requests[i + nsend]); + if (ret_code != MPI_SUCCESS) { + throw std::runtime_error( + "MPI_Irecv (for dynamic neighbor_allreduce) failed, see MPI " + "output for details."); + } } + for (int i = 0; i < nsend; ++i) { + int ret_code = MPI_Isend( + sendbuf, num_elements, mpi_ctx_.GetMPIDataType(entry.tensor), + entry.send_neighbors->at(i), + mpi_ctx_.rank_ + entry.send_neighbors->at(i), + mpi_ctx_.GetMPICommunicator(Communicator::GRAPH), &requests[i]); + if (ret_code != MPI_SUCCESS) { + throw std::runtime_error( + "MPI_Isend (for dynamic neighbor_allreduce) failed, see MPI " + "output for details."); + } + } + MPI_Waitall(nsend + nrecv, requests.data(), statuses.data()); + error_message = + GenerateNeighborAllreduceErrorMessage(statuses, nsend, nrecv); + } + } else { + if (entry.send_neighbors->empty()) { + throw std::runtime_error( + "Under hierarchical neighbor_allreduce, argument " + "send_machine_neighbors should not be empty."); + } + if (mpi_ctx_.local_size_ < 2) { + throw std::runtime_error( + "Local size is smaller than 2, in this case, you should use " + "neighbor_allreduce instead of hierarchical_neighbor_allreduce."); } - MPI_Waitall(nsend+nrecv, requests.data(), statuses.data()); - error_message = GenerateNeighborAllreduceErrorMessage(statuses, nsend, nrecv); + // 1. In-place allreduce + MPI_Allreduce(MPI_IN_PLACE, (void*)sendbuf, num_elements, + mpi_ctx_.GetMPIDataType(entry.tensor), MPI_SUM, + mpi_ctx_.GetMPICommunicator(Communicator::LOCAL)); + // 2. Local_rank = 0 do the neighbor all with other machines local_rank=0. + + // 3. Broadcast recv data from local rank = 0 to other local ranks. + int recv_num_elements = num_elements * entry.recv_neighbors->size(); + MPI_Bcast(buffer_data, recv_num_elements, + mpi_ctx_.GetMPIDataType(entry.output), 0, + mpi_ctx_.GetMPICommunicator(Communicator::LOCAL)); } timeline_ptr->ActivityEnd(entry.tensor_name); From c77cb0090f616f40700e1db835726ed115c84e6f Mon Sep 17 00:00:00 2001 From: ybc Date: Sat, 31 Oct 2020 16:58:38 -0700 Subject: [PATCH 34/44] Update mpi h_nar --- bluefog/common/mpi_controller.cc | 40 +++++++++++++++++++++++++++++++- 1 file changed, 39 insertions(+), 1 deletion(-) diff --git a/bluefog/common/mpi_controller.cc b/bluefog/common/mpi_controller.cc index c3b6eac5..9be5890a 100644 --- a/bluefog/common/mpi_controller.cc +++ b/bluefog/common/mpi_controller.cc @@ -468,7 +468,45 @@ void MPIController::NeighborAllreduce(TensorTableEntry& entry) { mpi_ctx_.GetMPIDataType(entry.tensor), MPI_SUM, mpi_ctx_.GetMPICommunicator(Communicator::LOCAL)); // 2. Local_rank = 0 do the neighbor all with other machines local_rank=0. - + if (mpi_ctx_.local_rank_ == 0) { + int nsend = entry.send_neighbors->size(); + int nrecv = entry.recv_neighbors->size(); + std::vector requests(nsend + nrecv); + std::vector statuses(nsend + nrecv); + int element_size = mpi_ctx_.GetMPITypeSize(entry.output->dtype()); + for (int i = 0; i < nrecv; ++i) { + void* recvbuf = (void*)(static_cast(entry.output->data()) + + num_elements * i * element_size); + int ret_code = MPI_Irecv( + recvbuf, num_elements, mpi_ctx_.GetMPIDataType(entry.output), + entry.recv_neighbors->at(i), + mpi_ctx_.rank_ + entry.recv_neighbors->at(i), + mpi_ctx_.GetMPICommunicator(Communicator::GRAPH), + &requests[i + nsend]); + if (ret_code != MPI_SUCCESS) { + throw std::runtime_error( + "MPI_Irecv (for dynamic neighbor_allreduce) failed, see MPI " + "output for details."); + } + } + for (int i = 0; i < nsend; ++i) { + int ret_code = MPI_Isend( + sendbuf, num_elements, mpi_ctx_.GetMPIDataType(entry.tensor), + entry.send_neighbors->at(i), + mpi_ctx_.rank_ + entry.send_neighbors->at(i), + mpi_ctx_.GetMPICommunicator(Communicator::GRAPH), &requests[i]); + if (ret_code != MPI_SUCCESS) { + throw std::runtime_error( + "MPI_Isend (for dynamic neighbor_allreduce) failed, see MPI " + "output for details."); + } + } + MPI_Waitall(nsend + nrecv, requests.data(), statuses.data()); + error_message = + GenerateNeighborAllreduceErrorMessage(statuses, nsend, nrecv); + } else { + // Do nothing here. + } // 3. Broadcast recv data from local rank = 0 to other local ranks. int recv_num_elements = num_elements * entry.recv_neighbors->size(); MPI_Bcast(buffer_data, recv_num_elements, From d85766c23307102e8dea8c3e25a3475068c4315a Mon Sep 17 00:00:00 2001 From: ybc Date: Sat, 31 Oct 2020 17:02:23 -0700 Subject: [PATCH 35/44] Remove the modula device count since we support only at most one device with process on in NCCL --- examples/pytorch_ImageNet_Resnet50.py | 2 +- examples/pytorch_benchmark.py | 2 +- examples/pytorch_cifar10_resnet.py | 2 +- examples/pytorch_mnist.py | 2 +- scripts/pytorch_opt_linear_speedup_test.py | 2 +- 5 files changed, 5 insertions(+), 5 deletions(-) diff --git a/examples/pytorch_ImageNet_Resnet50.py b/examples/pytorch_ImageNet_Resnet50.py index 9bc2ed2d..2e50301e 100644 --- a/examples/pytorch_ImageNet_Resnet50.py +++ b/examples/pytorch_ImageNet_Resnet50.py @@ -103,7 +103,7 @@ if args.cuda: # Bluefog: pin GPU to local rank. - torch.cuda.set_device(bf.local_rank() % torch.cuda.device_count()) + torch.cuda.set_device(bf.local_rank()) torch.cuda.manual_seed(args.seed) cudnn.benchmark = True diff --git a/examples/pytorch_benchmark.py b/examples/pytorch_benchmark.py index 03c22e1c..6b4494b5 100644 --- a/examples/pytorch_benchmark.py +++ b/examples/pytorch_benchmark.py @@ -95,7 +95,7 @@ "[power2(Default), ring, mesh, star，InnerOuterRing， InnerOuterExp2].") if args.cuda: - torch.cuda.set_device(bf.local_rank() % torch.cuda.device_count()) + torch.cuda.set_device(bf.local_rank()) cudnn.benchmark = True # Set up standard model. diff --git a/examples/pytorch_cifar10_resnet.py b/examples/pytorch_cifar10_resnet.py index 32bdd1cc..35a36666 100644 --- a/examples/pytorch_cifar10_resnet.py +++ b/examples/pytorch_cifar10_resnet.py @@ -124,7 +124,7 @@ if args.cuda: print("using cuda.") # Bluefog: pin GPU to local rank. - torch.cuda.set_device(bf.local_rank() % torch.cuda.device_count()) + torch.cuda.set_device(bf.local_rank()) torch.cuda.manual_seed(args.seed) else: print("using cpu") diff --git a/examples/pytorch_mnist.py b/examples/pytorch_mnist.py index 358b2111..14d99017 100644 --- a/examples/pytorch_mnist.py +++ b/examples/pytorch_mnist.py @@ -113,7 +113,7 @@ if args.cuda: # Bluefog: pin GPU to local rank. - torch.cuda.set_device(bf.local_rank() % torch.cuda.device_count()) + torch.cuda.set_device(bf.local_rank()) torch.cuda.manual_seed(args.seed) diff --git a/scripts/pytorch_opt_linear_speedup_test.py b/scripts/pytorch_opt_linear_speedup_test.py index 0408b8e7..d53e4c95 100644 --- a/scripts/pytorch_opt_linear_speedup_test.py +++ b/scripts/pytorch_opt_linear_speedup_test.py @@ -75,7 +75,7 @@ bf.init() if args.cuda: - torch.cuda.set_device(bf.local_rank() % torch.cuda.device_count()) + torch.cuda.set_device(bf.local_rank()) cudnn.benchmark = True def logistic_loss_step(x_, rho, X, y, tensor_name, calculate_by_hand=True): From e266ca6196afa25874596d172c68a743b06ffa69 Mon Sep 17 00:00:00 2001 From: ybc Date: Sat, 31 Oct 2020 17:09:08 -0700 Subject: [PATCH 36/44] Add h_nar for fused case --- bluefog/common/mpi_controller.cc | 145 +++++++++++++++++++++---------- 1 file changed, 101 insertions(+), 44 deletions(-) diff --git a/bluefog/common/mpi_controller.cc b/bluefog/common/mpi_controller.cc index 9be5890a..7276a71a 100644 --- a/bluefog/common/mpi_controller.cc +++ b/bluefog/common/mpi_controller.cc @@ -569,11 +569,6 @@ void MPIController::Allreduce(std::vector& entries) { void MPIController::NeighborAllreduce(std::vector& entries) { auto& first_entry = entries[0]; with_device device_guard(first_entry.device); - // TODO(ybc) support this - if (first_entry.is_hierarchical) { - throw std::runtime_error( - "hierarchical neighbor_allreduce is only supported under NCCL case."); - } void* buffer_data; size_t buffer_len = 0; @@ -619,51 +614,113 @@ void MPIController::NeighborAllreduce(std::vector& entries) { // is no need to transfer the local info again. However, for computation view, // including itself is more intuitive. std::string error_message = ""; - if (first_entry.send_neighbors->empty()) { - int ret_code = MPI_Neighbor_allgather( - fused_input_data, num_elements, mpi_ctx_.GetMPIDataType(first_entry.tensor), - buffer_data, num_elements, mpi_ctx_.GetMPIDataType(first_entry.output), - mpi_ctx_.GetMPICommunicator(Communicator::GRAPH)); - if (ret_code != MPI_SUCCESS) { - throw std::runtime_error( - "MPI_Neighbor_allreduce (through neighbor_allgather) failed, see MPI " - "output for details."); - } - } else { - int nsend = first_entry.send_neighbors->size(); - int nrecv = first_entry.recv_neighbors->size(); - std::vector requests(nsend + nrecv); - std::vector statuses(nsend + nrecv); - for (int i = 0; i < nrecv; ++i) { - void* recvbuf = - (void*)((uint8_t*)buffer_data + num_elements * i * element_size); - int ret_code = MPI_Irecv(recvbuf, num_elements, - mpi_ctx_.GetMPIDataType(first_entry.output), - first_entry.recv_neighbors->at(i), - /*tag=*/mpi_ctx_.rank_ + first_entry.recv_neighbors->at(i), - mpi_ctx_.GetMPICommunicator(Communicator::GRAPH), - &requests[i + nsend]); + + if (!first_entry.is_hierarchical) { + if (first_entry.send_neighbors->empty()) { + int ret_code = MPI_Neighbor_allgather( + fused_input_data, num_elements, mpi_ctx_.GetMPIDataType(first_entry.tensor), + buffer_data, num_elements, mpi_ctx_.GetMPIDataType(first_entry.output), + mpi_ctx_.GetMPICommunicator(Communicator::GRAPH)); if (ret_code != MPI_SUCCESS) { throw std::runtime_error( - "MPI_Irecv (for dynamic neighbor_allreduce) failed, see MPI output " - "for details."); + "MPI_Neighbor_allreduce (through neighbor_allgather) failed, see MPI " + "output for details."); + } + } else { + int nsend = first_entry.send_neighbors->size(); + int nrecv = first_entry.recv_neighbors->size(); + std::vector requests(nsend + nrecv); + std::vector statuses(nsend + nrecv); + for (int i = 0; i < nrecv; ++i) { + void* recvbuf = + (void*)((uint8_t*)buffer_data + num_elements * i * element_size); + int ret_code = MPI_Irecv(recvbuf, num_elements, + mpi_ctx_.GetMPIDataType(first_entry.output), + first_entry.recv_neighbors->at(i), + /*tag=*/mpi_ctx_.rank_ + first_entry.recv_neighbors->at(i), + mpi_ctx_.GetMPICommunicator(Communicator::GRAPH), + &requests[i + nsend]); + if (ret_code != MPI_SUCCESS) { + throw std::runtime_error( + "MPI_Irecv (for dynamic neighbor_allreduce) failed, see MPI output " + "for details."); + } + } + for (int i = 0; i < nsend; ++i) { + int ret_code = MPI_Isend( + fused_input_data, num_elements, mpi_ctx_.GetMPIDataType(first_entry.tensor), + first_entry.send_neighbors->at(i), + /*tag=*/mpi_ctx_.rank_ + first_entry.send_neighbors->at(i), + mpi_ctx_.GetMPICommunicator(Communicator::GRAPH), &requests[i]); + if (ret_code != MPI_SUCCESS) { + throw std::runtime_error( + "MPI_Isend (for dynamic neighbor_allreduce) failed, see MPI output " + "for details."); + } } + MPI_Waitall(nsend + nrecv, requests.data(), statuses.data()); + error_message = + GenerateNeighborAllreduceErrorMessage(statuses, nsend, nrecv); } - for (int i = 0; i < nsend; ++i) { - int ret_code = MPI_Isend( - fused_input_data, num_elements, mpi_ctx_.GetMPIDataType(first_entry.tensor), - first_entry.send_neighbors->at(i), - /*tag=*/mpi_ctx_.rank_ + first_entry.send_neighbors->at(i), - mpi_ctx_.GetMPICommunicator(Communicator::GRAPH), &requests[i]); - if (ret_code != MPI_SUCCESS) { - throw std::runtime_error( - "MPI_Isend (for dynamic neighbor_allreduce) failed, see MPI output " - "for details."); + } else { + if (first_entry.send_neighbors->empty()) { + throw std::runtime_error( + "Under hierarchical neighbor_allreduce, argument " + "send_machine_neighbors should not be empty."); + } + if (mpi_ctx_.local_size_ < 2) { + throw std::runtime_error( + "Local size is smaller than 2, in this case, you should use " + "neighbor_allreduce instead of hierarchical_neighbor_allreduce."); + } + // 1. In-place allreduce + MPI_Allreduce(MPI_IN_PLACE, (void*)fused_input_data, num_elements, + mpi_ctx_.GetMPIDataType(first_entry.tensor), MPI_SUM, + mpi_ctx_.GetMPICommunicator(Communicator::LOCAL)); + // 2. Local_rank = 0 do the neighbor all with other machines local_rank=0. + if (mpi_ctx_.local_rank_ == 0) { + int nsend = first_entry.send_neighbors->size(); + int nrecv = first_entry.recv_neighbors->size(); + std::vector requests(nsend + nrecv); + std::vector statuses(nsend + nrecv); + for (int i = 0; i < nrecv; ++i) { + void* recvbuf = + (void*)((uint8_t*)buffer_data + num_elements * i * element_size); + int ret_code = MPI_Irecv(recvbuf, num_elements, + mpi_ctx_.GetMPIDataType(first_entry.output), + first_entry.recv_neighbors->at(i), + /*tag=*/mpi_ctx_.rank_ + first_entry.recv_neighbors->at(i), + mpi_ctx_.GetMPICommunicator(Communicator::GRAPH), + &requests[i + nsend]); + if (ret_code != MPI_SUCCESS) { + throw std::runtime_error( + "MPI_Irecv (for dynamic neighbor_allreduce) failed, see MPI output " + "for details."); + } + } + for (int i = 0; i < nsend; ++i) { + int ret_code = MPI_Isend( + fused_input_data, num_elements, mpi_ctx_.GetMPIDataType(first_entry.tensor), + first_entry.send_neighbors->at(i), + /*tag=*/mpi_ctx_.rank_ + first_entry.send_neighbors->at(i), + mpi_ctx_.GetMPICommunicator(Communicator::GRAPH), &requests[i]); + if (ret_code != MPI_SUCCESS) { + throw std::runtime_error( + "MPI_Isend (for dynamic neighbor_allreduce) failed, see MPI output " + "for details."); + } } + MPI_Waitall(nsend + nrecv, requests.data(), statuses.data()); + error_message = + GenerateNeighborAllreduceErrorMessage(statuses, nsend, nrecv); + } else { + // Do nothing here. } - MPI_Waitall(nsend + nrecv, requests.data(), statuses.data()); - error_message = - GenerateNeighborAllreduceErrorMessage(statuses, nsend, nrecv); + // 3. Broadcast recv data from local rank = 0 to other local ranks. + int recv_num_elements = num_elements * first_entry.recv_neighbors->size(); + MPI_Bcast(buffer_data, recv_num_elements, + mpi_ctx_.GetMPIDataType(first_entry.output), 0, + mpi_ctx_.GetMPICommunicator(Communicator::LOCAL)); } timeline_ptr->ActivityEndAll(entries); From 38db5b505653b8a87ee792090931e812c4355356 Mon Sep 17 00:00:00 2001 From: ybc Date: Sat, 31 Oct 2020 17:25:05 -0700 Subject: [PATCH 37/44] Add the missing MemcpyOutFusionBufferForInputs for h_nar --- bluefog/common/mpi_controller.cc | 26 ++++++++++++++++++++++++++ bluefog/common/mpi_controller.h | 3 +++ 2 files changed, 29 insertions(+) diff --git a/bluefog/common/mpi_controller.cc b/bluefog/common/mpi_controller.cc index 7276a71a..22e8baae 100644 --- a/bluefog/common/mpi_controller.cc +++ b/bluefog/common/mpi_controller.cc @@ -716,6 +716,8 @@ void MPIController::NeighborAllreduce(std::vector& entries) { } else { // Do nothing here. } + // Because the in-place modification, we need to copy fused_input_data back to tensor as well + MemcpyOutFusionBufferForInputs(fused_input_data, entries); // 3. Broadcast recv data from local rank = 0 to other local ranks. int recv_num_elements = num_elements * first_entry.recv_neighbors->size(); MPI_Bcast(buffer_data, recv_num_elements, @@ -1321,6 +1323,30 @@ void MPIController::MemcpyOutFusionBufferForNeighbors( } } +void MPIController::MemcpyOutFusionBufferForInputs( + const void* fused_input_data, std::vector& entries) { + // Copy the input data stored in the fusion buffer back to input, which is + // used in hierarchical neighbor allreduce since it has allreduce step to + // modified the input data. + int64_t offset = 0; + for (auto& e : entries) { + void* fused_input_data_at_offset = (uint8_t*)fused_input_data + offset; + void* dst_data = (void*)e.tensor->data(); + size_t count = (size_t)e.tensor->size(); +#if HAVE_CUDA + if (e.device != CPU_DEVICE_ID) { + CUDACHECK(cudaMemcpy(dst_data, fused_input_data_at_offset, count, + cudaMemcpyDeviceToDevice)); + } else { +#endif + std::memcpy(dst_data, fused_input_data, count); +#if HAVE_CUDA + } +#endif + offset += e.tensor->size(); + } +} + void MPIController::MemcpyEntryInFusionBuffer(const TensorTableEntry& e, void* buffer_data_at_offset) { const void* src_data = e.tensor->data(); diff --git a/bluefog/common/mpi_controller.h b/bluefog/common/mpi_controller.h index 64946159..75f4e245 100644 --- a/bluefog/common/mpi_controller.h +++ b/bluefog/common/mpi_controller.h @@ -116,6 +116,9 @@ class MPIController { const int num_recv_neighbors, const int64_t fused_data_size); + void MemcpyOutFusionBufferForInputs(const void* fused_input_data, + std::vector& entries); + void MemcpyEntryInFusionBuffer(const TensorTableEntry& e, void* buffer_data_at_offset); From cf481e127deccb9ee3e3665a22d2d135ff99d8ce Mon Sep 17 00:00:00 2001 From: ybc Date: Sat, 31 Oct 2020 22:29:29 -0700 Subject: [PATCH 38/44] Remove mesh/star topo in examples --- examples/pytorch_ImageNet_Resnet50.py | 6 ------ examples/pytorch_benchmark.py | 5 ----- examples/pytorch_cifar10_resnet.py | 7 ------- examples/pytorch_mnist.py | 4 +--- 4 files changed, 1 insertion(+), 21 deletions(-) diff --git a/examples/pytorch_ImageNet_Resnet50.py b/examples/pytorch_ImageNet_Resnet50.py index 2e50301e..fedb7b03 100644 --- a/examples/pytorch_ImageNet_Resnet50.py +++ b/examples/pytorch_ImageNet_Resnet50.py @@ -92,11 +92,6 @@ pass elif args.virtual_topology == "ring": bf.set_topology(topology_util.RingGraph(bf.size(), connect_style=1)) - elif args.virtual_topology == "mesh": - bf.set_topology(topology_util.MeshGrid2DGraph( - bf.size()), is_weighted=True) - elif args.virtual_topology == "star": - bf.set_topology(topology_util.StarGraph(bf.size())) else: raise ValueError("Unknown args.virtual_topology, supporting options are " + "[power2(Default), ring, mesh, star].") @@ -180,7 +175,6 @@ momentum=args.momentum, weight_decay=args.wd) # Bluefog: wrap optimizer with DistributedOptimizer. -# TODO(hhb) Pass batches_per_allreduce to optimizer. if args.dist_optimizer == 'win_put': optimizer = bf.DistributedWinPutOptimizer(optimizer, model=model) elif args.dist_optimizer == 'neighbor_allreduce': diff --git a/examples/pytorch_benchmark.py b/examples/pytorch_benchmark.py index 6b4494b5..32678cf4 100644 --- a/examples/pytorch_benchmark.py +++ b/examples/pytorch_benchmark.py @@ -77,11 +77,6 @@ pass elif args.virtual_topology == "ring": bf.set_topology(topology_util.RingGraph(bf.size(), connect_style=1)) - elif args.virtual_topology == "mesh": - bf.set_topology(topology_util.MeshGrid2DGraph( - bf.size()), is_weighted=True) - elif args.virtual_topology == "star": - bf.set_topology(topology_util.StarGraph(bf.size())) elif args.virtual_topology == "InnerOuterRing": assert bf.is_homogeneous, "InnerOuterRing topo should be used only homogeneous environment" bf.set_topology(topology_util.InnerOuterRingGraph( diff --git a/examples/pytorch_cifar10_resnet.py b/examples/pytorch_cifar10_resnet.py index 35a36666..ee72f7fc 100644 --- a/examples/pytorch_cifar10_resnet.py +++ b/examples/pytorch_cifar10_resnet.py @@ -104,11 +104,6 @@ pass elif args.virtual_topology == "ring": bf.set_topology(topology_util.RingGraph(bf.size(), connect_style=1)) - elif args.virtual_topology == "mesh": - bf.set_topology(topology_util.MeshGrid2DGraph( - bf.size()), is_weighted=True) - elif args.virtual_topology == "star": - bf.set_topology(topology_util.StarGraph(bf.size())) elif args.virtual_topology == "InnerOuterRing": assert bf.is_homogeneous, "InnerOuterRing topo should be used only homogeneous environment" bf.set_topology(topology_util.InnerOuterRingGraph( @@ -233,8 +228,6 @@ optimizer = optimizer = bf.DistributedOptimizer( optimizer, named_parameters=model.named_parameters() ) -elif args.dist_optimizer == 'pull_get': - optimizer = bf.DistributedPullGetOptimizer(optimizer, model=model) else: raise ValueError('Unknown args.dist-optimizer type -- ' + args.dist_optimizer + '\n' + 'Please set the argument to be one of ' + diff --git a/examples/pytorch_mnist.py b/examples/pytorch_mnist.py index 14d99017..0faadfe5 100644 --- a/examples/pytorch_mnist.py +++ b/examples/pytorch_mnist.py @@ -210,8 +210,6 @@ def forward(self, x): optimizer = optimizer = bf.DistributedOptimizer( optimizer, named_parameters=model.named_parameters() ) -elif args.dist_optimizer == 'pull_get': - optimizer = bf.DistributedPullGetOptimizer(optimizer, model=model) else: raise ValueError('Unknown args.dist-optimizer type -- ' + args.dist_optimizer + '\n' + 'Please set the argument to be one of ' + @@ -228,7 +226,7 @@ def forward(self, x): dynamic_neighbor_allreduce_gen = topology_util.GetInnerOuterExp2DynamicSendRecvRanks( bf.size(), local_size=bf.local_size() if args.local_size == -1 else args.local_size, - self_rank=bf.rank()) + self_rank=bf.rank()) elif args.dist_optimizer == 'hierarchical_neighbor_allreduce': # This optimizer can use following dynamic topo only so far. dynamic_machine_neighbor_allreduce_gen = topology_util.GetExp2DynamicSendRecvMachineRanks( From 854e99b3c5080175c53a5e56641d0d194a97c726 Mon Sep 17 00:00:00 2001 From: ybc Date: Sun, 1 Nov 2020 13:07:45 -0800 Subject: [PATCH 39/44] Minor style update --- examples/pytorch_benchmark.py | 2 +- examples/pytorch_cifar10_resnet.py | 12 ++++++------ 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/examples/pytorch_benchmark.py b/examples/pytorch_benchmark.py index 32678cf4..8332623c 100644 --- a/examples/pytorch_benchmark.py +++ b/examples/pytorch_benchmark.py @@ -205,7 +205,7 @@ def dynamic_topology_update(batch_idx): optimizer.neighbor_weights = { r: 1/(len(recv_neighbors) + 1) for r in recv_neighbors} optimizer.self_weight = 1 / (len(recv_neighbors) + 1) - optimizer.enable_topo_check = False + optimizer.enable_topo_check = False elif args.dist_optimizer == 'hierarchical_neighbor_allreduce': send_machines, recv_machines = next(dynamic_machine_neighbor_allreduce_gen) optimizer.send_neighbor_machines = send_machines diff --git a/examples/pytorch_cifar10_resnet.py b/examples/pytorch_cifar10_resnet.py index ee72f7fc..f9ccd533 100644 --- a/examples/pytorch_cifar10_resnet.py +++ b/examples/pytorch_cifar10_resnet.py @@ -75,8 +75,8 @@ ) parser.add_argument("--seed", type=int, default=42, help="random seed") parser.add_argument('--dist-optimizer', type=str, default='neighbor_allreduce', - help='The type of distributed optimizer. Supporting options are ' + - '[win_put, neighbor_allreduce, allreduce, pull_get, hierarchical_neighbor_allreduce, horovod]') + help='The type of distributed optimizer. Supporting options are [win_put, ' + + 'neighbor_allreduce, allreduce, hierarchical_neighbor_allreduce, horovod]') parser.add_argument("--average-test-result", action="store_true", default=False, help=("Allreduce called to average test result. Warning this will " + @@ -157,8 +157,8 @@ transform=transforms.Compose( [ transforms.ToTensor(), - transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[ - 0.229, 0.224, 0.225]), + transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]), ] ), ) @@ -178,8 +178,8 @@ transform=transforms.Compose( [ transforms.ToTensor(), - transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[ - 0.229, 0.224, 0.225]), + transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]), ] ), ) From aa4094a7629b45f9a883a4ec8e1736e39bb6eee1 Mon Sep 17 00:00:00 2001 From: ybc Date: Sun, 1 Nov 2020 17:40:15 -0800 Subject: [PATCH 40/44] PowerGraph => ExponentialGraph --- bluefog/common/basics.py | 10 ++--- bluefog/common/mpi_controller.cc | 2 +- bluefog/common/topology_util.py | 28 +++++++------- docs/timeline.rst | 2 +- examples/pytorch_ImageNet_Resnet50.py | 8 ++-- examples/pytorch_average_consensus.py | 16 ++++---- examples/pytorch_benchmark.py | 18 ++++----- examples/pytorch_cifar10_resnet.py | 44 +++++----------------- examples/pytorch_mnist.py | 18 ++++----- examples/pytorch_optimization.py | 8 ++-- scripts/pytorch_opt_linear_speedup_test.py | 10 ++--- scripts/single_ops_test.py | 16 ++++---- test/tensorflow_basics_test.py | 8 ++-- test/torch_basics_test.py | 10 ++--- test/torch_ops_test.py | 22 +++++------ test/torch_win_ops_test.py | 20 +++++----- 16 files changed, 108 insertions(+), 132 deletions(-) diff --git a/bluefog/common/basics.py b/bluefog/common/basics.py index 2e10f50a..e26b0bd2 100644 --- a/bluefog/common/basics.py +++ b/bluefog/common/basics.py @@ -51,7 +51,7 @@ def init(self, topology_fn: Optional[Callable[[int], networkx.DiGraph]] = None, Args: topology_fn: A callable function that takes size as input and return networkx.DiGraph object to decide the topology. If not provided - a default power graph (base 2) structure is called. + a default exponential graph (base 2) structure is called. is_weighted: If set to true, the neighbor ops like (win_update, neighbor_allreduce) will execute the weighted average instead, where the weight is the value used in topology matrix (including self). @@ -60,7 +60,7 @@ def init(self, topology_fn: Optional[Callable[[int], networkx.DiGraph]] = None, if topology_fn: topo = topology_fn(self.size()) else: - topo = topology_util.PowerGraph(self.size()) + topo = topology_util.ExponentialGraph(self.size()) self.set_topology(topo, is_weighted) atexit.register(self.shutdown) @@ -191,7 +191,7 @@ def set_topology(self, topology: Optional[networkx.DiGraph] = None, Args: Topo: A networkx.DiGraph object to decide the topology. If not provided - a default power graph (base 2) structure is used. + a default exponential graph (base 2) structure is used. is_weighted: If set to true, the win_update and neighbor_allreduce will execute the weighted average instead, where the weights are the value used in topology matrix (including self weight). Note win_get/win_put/win_accumulate do not use this weight @@ -207,10 +207,10 @@ def set_topology(self, topology: Optional[networkx.DiGraph] = None, >>> bf.set_topology(topology_util.RingGraph(bf.size())) """ if topology is None: - topology = topology_util.PowerGraph(size=self.size()) + topology = topology_util.ExponentialGraph(size=self.size()) if self.local_rank() == 0: logger.info( - "Topology is not specified. Default Power Two topology is used.") + "Topology is not specified. Default Exponential Two topology is used.") if not isinstance(topology, networkx.DiGraph): raise TypeError("topology must be a networkx.DiGraph obejct.") diff --git a/bluefog/common/mpi_controller.cc b/bluefog/common/mpi_controller.cc index 820c5e10..cba85dde 100644 --- a/bluefog/common/mpi_controller.cc +++ b/bluefog/common/mpi_controller.cc @@ -825,7 +825,7 @@ void MPIController::WinCreate(TensorTableEntry& entry) { win_manager->SetGlobalWin(global_mpi_win_ptr); // Build extra buffers for win_put. - // For example: size=4 power two ring topology + // For example: size=4 exponential two ring topology // r\s 0 1 2 3 // 0 g x x // 1 x g x diff --git a/bluefog/common/topology_util.py b/bluefog/common/topology_util.py index 308e02c1..6be78a38 100644 --- a/bluefog/common/topology_util.py +++ b/bluefog/common/topology_util.py @@ -63,18 +63,18 @@ def GetSendWeights(topo: nx.DiGraph, rank: int) -> Tuple[float, Dict[int, float] return self_weight, neighbor_weights -def PowerTwoRingGraph(size: int) -> nx.DiGraph: +def ExponentialTwoGraph(size: int) -> nx.DiGraph: """Generate graph topology such that each points only - connected to a point such that the index difference is power of 2. + connected to a point such that the index difference is the power of 2. - Example: A PowerTwoRingGraph with 12 nodes: + Example: A ExponentialTwoGraph with 12 nodes: .. plot:: :context: close-figs >>> import networkx as nx >>> from bluefog.common import topology_util - >>> G = topology_util.PowerTwoRingGraph(12) + >>> G = topology_util.ExponentialTwoGraph(12) >>> nx.draw_circular(G) """ assert size > 0 @@ -96,18 +96,18 @@ def isPowerOf(x, base): return False -def PowerGraph(size: int, base: int = 2) -> nx.DiGraph: +def ExponentialGraph(size: int, base: int = 2) -> nx.DiGraph: """Generate graph topology such that each points only connected to a point such that the index difference is power of base. (Default is 2) - Example: A PowerGraph with 12 nodes: + Example: A ExponentialGraph with 12 nodes: .. plot:: :context: close-figs >>> import networkx as nx >>> from bluefog.common import topology_util - >>> G = topology_util.PowerGraph(12) + >>> G = topology_util.ExponentialGraph(12) >>> nx.draw_circular(G) """ x = [1.0] @@ -125,20 +125,20 @@ def PowerGraph(size: int, base: int = 2) -> nx.DiGraph: return G -def SymmetricPowerGraph(size: int, base: int = 4) -> nx.DiGraph: +def SymmetricExponentialGraph(size: int, base: int = 4) -> nx.DiGraph: """ Generate symmeteric graph topology such that for the first half of nodes only connected to a point such that the index difference is power of base (Default is 4) and the connectivity for the second half of nodes just mirrored to the first half. - Example: A SymmetricPowerGraph with 12 nodes + Example: A SymmetricExponentialGraph with 12 nodes .. plot:: :context: close-figs >>> import networkx as nx >>> from bluefog.common import topology_util - >>> G = topology_util.SymmetricPowerGraph(12) + >>> G = topology_util.SymmetricExponentialGraph(12) >>> nx.draw_circular(G) """ x = [1.0] @@ -339,7 +339,7 @@ def InnerOuterRingGraph(world_size: int, local_size: int) -> nx.DiGraph: return G -def InnerOuterExp2Graph(world_size: int, local_size: int) -> nx.DiGraph: +def InnerOuterExpo2Graph(world_size: int, local_size: int) -> nx.DiGraph: """Generate Inner Ring and Outer Exponential-2 Graph. Within one machine all inner rank/processes is fully-connected and all @@ -349,7 +349,7 @@ def InnerOuterExp2Graph(world_size: int, local_size: int) -> nx.DiGraph: >>> import networkx as nx >>> from bluefog.common import topology_util - >>> G = topology_util.InnerOuterExp2Graph(12, 3) + >>> G = topology_util.InnerOuterExpo2Graph(12, 3) >>> nx.draw_circular(G) """ total_nodes = world_size @@ -541,7 +541,7 @@ def GetInnerOuterRingDynamicSendRecvRanks( index += 1 -def GetInnerOuterExp2DynamicSendRecvRanks( +def GetInnerOuterExpo2DynamicSendRecvRanks( world_size: int, local_size: int, self_rank: int ) -> Iterator[Tuple[List[int], List[int]]]: """ @@ -560,7 +560,7 @@ def GetInnerOuterExp2DynamicSendRecvRanks( >>> from bluefog.common import topology_util >>> world_size, local_size = bf.size(), bf.local_size() - >>> gen = topology_util.GetInnerOuterExp2DynamicSendRecvRanks(world_size, local_size, 0) + >>> gen = topology_util.GetInnerOuterExpo2DynamicSendRecvRanks(world_size, local_size, 0) >>> for _ in range(10): >>> print(next(gen)) """ diff --git a/docs/timeline.rst b/docs/timeline.rst index e8c578e9..170d7cd3 100644 --- a/docs/timeline.rst +++ b/docs/timeline.rst @@ -36,7 +36,7 @@ Example I: Logistic regression with neighbor_allreduce ------------------------------------------------------ In the first example, we show the timeline when running decentralized SGD for logistic regression, see the figure below. In this example, each rank is connected -via an undirected power-2 topology. We exploit the +via an undirected exponential-2 topology. We exploit the primitive ``neighbor_allreduce`` to perform the neighbor averaging. .. image:: ./_static/bf_timeline_example1a.png diff --git a/examples/pytorch_ImageNet_Resnet50.py b/examples/pytorch_ImageNet_Resnet50.py index fedb7b03..879e9613 100644 --- a/examples/pytorch_ImageNet_Resnet50.py +++ b/examples/pytorch_ImageNet_Resnet50.py @@ -72,9 +72,9 @@ parser.add_argument("--disable-dynamic-topology", action="store_true", default=False, help=("Disable each iteration to transmit one neighbor " + "per iteration dynamically.")) -parser.add_argument('--virtual-topology', type=str, default="power2", +parser.add_argument('--virtual-topology', type=str, default="expo2", help='The underlying virtual topology. Supporting options are ' + - '[power2(Default), ring, mesh, star].') + '[expo2(Default), ring, mesh, star].') args = parser.parse_args() args.cuda = not args.no_cuda and torch.cuda.is_available() @@ -88,13 +88,13 @@ torch.manual_seed(args.seed) if args.dist_optimizer != 'horovod': - if args.virtual_topology == "power2": + if args.virtual_topology == "expo2": pass elif args.virtual_topology == "ring": bf.set_topology(topology_util.RingGraph(bf.size(), connect_style=1)) else: raise ValueError("Unknown args.virtual_topology, supporting options are " + - "[power2(Default), ring, mesh, star].") + "[expo2(Default), ring, mesh, star].") if args.cuda: # Bluefog: pin GPU to local rank. diff --git a/examples/pytorch_average_consensus.py b/examples/pytorch_average_consensus.py index 51d67349..f531b9e4 100644 --- a/examples/pytorch_average_consensus.py +++ b/examples/pytorch_average_consensus.py @@ -27,9 +27,9 @@ help='maximum iterations') parser.add_argument('--local-size', type=int, default=4, help='number of nodes per machine') -parser.add_argument('--virtual-topology', type=str, default="power2", +parser.add_argument('--virtual-topology', type=str, default="expo2", help='The underlying virtual topology. Supporting options are ' + - '[power2(Default), ring, mesh, star, InnerOuterRing].') + '[expo2(Default), ring, mesh, star, InnerOuterRing].') parser.add_argument('--asynchronous-mode', action='store_true', default=False, help='Use one-sided ops to run asynchronous push sum algorithm') parser.add_argument('--no-cuda', action='store_true', default=False, @@ -58,12 +58,12 @@ else: x = torch.randn(args.data_size, dtype=torch.double) -if args.virtual_topology == "power2": +if args.virtual_topology == "expo2": pass -elif args.virtual_topology == "power3": - bf.set_topology(topology_util.PowerGraph(bf.size(), base=3)) -elif args.virtual_topology == "power4": - bf.set_topology(topology_util.PowerGraph(bf.size(), base=4)) +elif args.virtual_topology == "expo3": + bf.set_topology(topology_util.ExponentialGraph(bf.size(), base=3)) +elif args.virtual_topology == "expo4": + bf.set_topology(topology_util.ExponentialGraph(bf.size(), base=4)) elif args.virtual_topology == "ring": bf.set_topology(topology_util.RingGraph(bf.size(), connect_style=1)) elif args.virtual_topology == "mesh": @@ -77,7 +77,7 @@ bf.set_topology(topology_util.FullyConnectedGraph(bf.size())) else: raise ValueError("Unknown args.virtual_topology, supporting options are " + - "[power2(Default), ring, mesh, star].") + "[expo2(Default), ring, mesh, star].") x_bar = bf.allreduce(x, average=True) mse = [torch.norm(x-x_bar, p=2) / torch.norm(x_bar, p=2)] diff --git a/examples/pytorch_benchmark.py b/examples/pytorch_benchmark.py index 8332623c..5244fa91 100644 --- a/examples/pytorch_benchmark.py +++ b/examples/pytorch_benchmark.py @@ -60,9 +60,9 @@ parser.add_argument('--disable-dynamic-topology', action='store_true', default=False, help=('Disable each iteration to transmit one neighbor ' + 'per iteration dynamically.')) -parser.add_argument('--virtual-topology', type=str, default="power2", +parser.add_argument('--virtual-topology', type=str, default="expo2", help='The underlying virtual topology. Supporting options are ' + - '[power2(Default), ring, mesh, star, InnerOuterRing, InnerOuterExp2].') + '[expo2(Default), ring, mesh, star, InnerOuterRing, InnerOuterExpo2].') args = parser.parse_args() @@ -73,7 +73,7 @@ bf.init() if args.dist_optimizer != 'horovod': - if args.virtual_topology == "power2": + if args.virtual_topology == "expo2": pass elif args.virtual_topology == "ring": bf.set_topology(topology_util.RingGraph(bf.size(), connect_style=1)) @@ -81,13 +81,13 @@ assert bf.is_homogeneous, "InnerOuterRing topo should be used only homogeneous environment" bf.set_topology(topology_util.InnerOuterRingGraph( bf.size(), local_size=bf.local_size() if args.local_size == -1 else args.local_size)) - elif args.virtual_topology == "InnerOuterExp2": - assert bf.is_homogeneous, "InnerOuterExp2 topo should be used under homogeneous environment" - bf.set_topology(topology_util.InnerOuterExp2Graph( + elif args.virtual_topology == "InnerOuterExpo2": + assert bf.is_homogeneous, "InnerOuterExpo2 topo should be used under homogeneous environment" + bf.set_topology(topology_util.InnerOuterExpo2Graph( bf.size(), local_size=bf.local_size() if args.local_size == -1 else args.local_size)) else: raise ValueError("Unknown args.virtual_topology, supporting options are " + - "[power2(Default), ring, mesh, star，InnerOuterRing， InnerOuterExp2].") + "[expo2(Default), ring, mesh, star，InnerOuterRing， InnerOuterExpo2].") if args.cuda: torch.cuda.set_device(bf.local_rank()) @@ -176,8 +176,8 @@ def forward(self, x): bf.size(), local_size=bf.local_size() if args.local_size == -1 else args.local_size, self_rank=bf.rank()) - elif args.virtual_topology == 'InnerOuterExp2': - dynamic_neighbor_allreduce_gen = topology_util.GetInnerOuterExp2DynamicSendRecvRanks( + elif args.virtual_topology == 'InnerOuterExpo2': + dynamic_neighbor_allreduce_gen = topology_util.GetInnerOuterExpo2DynamicSendRecvRanks( bf.size(), local_size=bf.local_size() if args.local_size == -1 else args.local_size, self_rank=bf.rank()) diff --git a/examples/pytorch_cifar10_resnet.py b/examples/pytorch_cifar10_resnet.py index f9ccd533..d02ca894 100644 --- a/examples/pytorch_cifar10_resnet.py +++ b/examples/pytorch_cifar10_resnet.py @@ -84,9 +84,9 @@ parser.add_argument('--disable-dynamic-topology', action='store_true', default=False, help=('Disable each iteration to transmit one neighbor ' + 'per iteration dynamically.')) -parser.add_argument('--virtual-topology', type=str, default="power2", +parser.add_argument('--virtual-topology', type=str, default="expo2", help='The underlying virtual topology. Supporting options are ' + - '[power2(Default), ring, mesh, star].') + '[expo2(Default), ring, mesh, star].') args = parser.parse_args() args.cuda = (not args.no_cuda) and (torch.cuda.is_available()) @@ -100,7 +100,7 @@ bf.init() torch.manual_seed(args.seed) if args.dist_optimizer != 'horovod': - if args.virtual_topology == "power2": + if args.virtual_topology == "expo2": pass elif args.virtual_topology == "ring": bf.set_topology(topology_util.RingGraph(bf.size(), connect_style=1)) @@ -108,13 +108,13 @@ assert bf.is_homogeneous, "InnerOuterRing topo should be used only homogeneous environment" bf.set_topology(topology_util.InnerOuterRingGraph( bf.size(), local_size=bf.local_size())) - elif args.virtual_topology == "InnerOuterExp2": - assert bf.is_homogeneous, "InnerOuterExp2 topo should be used under homogeneous environment" - bf.set_topology(topology_util.InnerOuterExp2Graph( + elif args.virtual_topology == "InnerOuterExpo2": + assert bf.is_homogeneous, "InnerOuterExpo2 topo should be used under homogeneous environment" + bf.set_topology(topology_util.InnerOuterExpo2Graph( bf.size(), local_size=bf.local_size())) else: raise ValueError("Unknown args.virtual_topology, supporting options are " + - "[power2(Default), ring, mesh, star，InnerOuterRing， InnerOuterExp2].") + "[expo2(Default), ring, mesh, star，InnerOuterRing， InnerOuterExpo2].") if args.cuda: print("using cuda.") @@ -126,21 +126,6 @@ cudnn.benchmark = True -# If set > 0, will resume training from a given checkpoint. -resume_from_epoch = 0 -# for try_epoch in range(args.epochs, 0, -1): -# if os.path.exists(args.checkpoint_format.format(epoch=try_epoch)): -# resume_from_epoch = try_epoch -# break - -# Bluefog: broadcast resume_from_epoch from rank 0 (which will have -# checkpoints) to other ranks. -resume_from_epoch = bf.broadcast( - torch.tensor(resume_from_epoch), # pylint: disable=not-callable - root_rank=0, - name="resume_from_epoch", -).item() - # Bluefog: print logs on the first worker. verbose = 1 if bf.rank() == 0 else 0 @@ -234,15 +219,6 @@ '[neighbor_allreduce, gradient_allreduce, allreduce, ' + 'win_put, horovod]') -print("resume_from_epoch: ", resume_from_epoch) -# Restore from a previous checkpoint, if initial_epoch is specified. -# Bluefog: restore on the first worker which will broadcast weights to other workers. -# if resume_from_epoch > 0 and bf.rank() == 0: -# filepath = args.checkpoint_format.format(epoch=resume_from_epoch) -# checkpoint = torch.load(filepath) -# model.load_state_dict(checkpoint["model"]) -# optimizer.load_state_dict(checkpoint["optimizer"]) - # Bluefog: broadcast parameters & optimizer state. bf.broadcast_parameters(model.state_dict(), root_rank=0) bf.broadcast_optimizer_state(optimizer, root_rank=0) @@ -345,8 +321,8 @@ def adjust_learning_rate(epoch, batch_idx): bf.size(), local_size=bf.local_size(), self_rank=bf.rank()) - elif args.virtual_topology == 'InnerOuterExp2': - dynamic_neighbor_allreduce_gen = topology_util.GetInnerOuterExp2DynamicSendRecvRanks( + elif args.virtual_topology == 'InnerOuterExpo2': + dynamic_neighbor_allreduce_gen = topology_util.GetInnerOuterExpo2DynamicSendRecvRanks( bf.size(), local_size=bf.local_size(), self_rank=bf.rank()) @@ -421,7 +397,7 @@ def avg(self): return self.sum / self.n -for epoch in range(resume_from_epoch, args.epochs): +for epoch in range(args.epochs): train(epoch) validate(epoch) # save_checkpoint(epoch) diff --git a/examples/pytorch_mnist.py b/examples/pytorch_mnist.py index 0faadfe5..cd664c87 100644 --- a/examples/pytorch_mnist.py +++ b/examples/pytorch_mnist.py @@ -65,9 +65,9 @@ parser.add_argument('--disable-dynamic-topology', action='store_true', default=False, help=('Disable each iteration to transmit one neighbor ' + 'per iteration dynamically.')) -parser.add_argument('--virtual-topology', type=str, default="power2", +parser.add_argument('--virtual-topology', type=str, default="expo2", help='The underlying virtual topology. Supporting options are ' + - '[power2(Default), ring, mesh, star, InnerOuterRing, InnerOuterExp2].') + '[expo2(Default), ring, mesh, star, InnerOuterRing, InnerOuterExpo2].') parser.add_argument( "--seed", type=int, default=42, metavar="S", help="random seed (default: 42)" @@ -90,7 +90,7 @@ bf.init() if args.dist_optimizer != 'horovod': - if args.virtual_topology == "power2": + if args.virtual_topology == "expo2": pass elif args.virtual_topology == "ring": bf.set_topology(topology_util.RingGraph(bf.size(), connect_style=1)) @@ -103,13 +103,13 @@ assert bf.is_homogeneous, "InnerOuterRing topo should be used only homogeneous environment" bf.set_topology(topology_util.InnerOuterRingGraph( bf.size(), local_size=bf.local_size() if args.local_size == -1 else args.local_size)) - elif args.virtual_topology == "InnerOuterExp2": - assert bf.is_homogeneous, "InnerOuterExp2 topo should be used under homogeneous environment" - bf.set_topology(topology_util.InnerOuterExp2Graph( + elif args.virtual_topology == "InnerOuterExpo2": + assert bf.is_homogeneous, "InnerOuterExpo2 topo should be used under homogeneous environment" + bf.set_topology(topology_util.InnerOuterExpo2Graph( bf.size(), local_size=bf.local_size() if args.local_size == -1 else args.local_size)) else: raise ValueError("Unknown args.virtual_topology, supporting options are " + - "[power2(Default), ring, mesh, star，InnerOuterRing， InnerOuterExp2].") + "[expo2(Default), ring, mesh, star，InnerOuterRing， InnerOuterExpo2].") if args.cuda: # Bluefog: pin GPU to local rank. @@ -222,8 +222,8 @@ def forward(self, x): bf.size(), local_size=bf.local_size() if args.local_size == -1 else args.local_size, self_rank=bf.rank()) - elif args.virtual_topology == 'InnerOuterExp2': - dynamic_neighbor_allreduce_gen = topology_util.GetInnerOuterExp2DynamicSendRecvRanks( + elif args.virtual_topology == 'InnerOuterExpo2': + dynamic_neighbor_allreduce_gen = topology_util.GetInnerOuterExpo2DynamicSendRecvRanks( bf.size(), local_size=bf.local_size() if args.local_size == -1 else args.local_size, self_rank=bf.rank()) diff --git a/examples/pytorch_optimization.py b/examples/pytorch_optimization.py index 79ae22ed..f1a224db 100644 --- a/examples/pytorch_optimization.py +++ b/examples/pytorch_optimization.py @@ -48,7 +48,7 @@ default='logistic_regression' ) parser.add_argument( - "--topology", help="this example supports mesh, star, ring, and power_two", + "--topology", help="this example supports mesh, star, ring, and expo2", default='ring' ) args = parser.parse_args() @@ -431,8 +431,8 @@ def push_diging(X, y, w_opt, loss, maxite=2000, alpha=1e-1, **kwargs): bf.init() if args.topology == 'mesh': bf.set_topology(topology_util.MeshGrid2DGraph(bf.size()), is_weighted=True) -elif args.topology == 'power_two': - bf.set_topology(topology_util.PowerGraph(bf.size())) +elif args.topology == 'expo2': + bf.set_topology(topology_util.ExponentialGraph(bf.size())) elif args.topology == 'star': bf.set_topology(topology_util.StarGraph(bf.size()), is_weighted=True) elif args.topology == 'ring': @@ -440,7 +440,7 @@ def push_diging(X, y, w_opt, loss, maxite=2000, alpha=1e-1, **kwargs): else: raise NotImplementedError( 'Topology not supported. This example only supports' + - ' mesh, star, ring and power_two' + ' mesh, star, ring and expo2' ) diff --git a/scripts/pytorch_opt_linear_speedup_test.py b/scripts/pytorch_opt_linear_speedup_test.py index d53e4c95..77acc631 100644 --- a/scripts/pytorch_opt_linear_speedup_test.py +++ b/scripts/pytorch_opt_linear_speedup_test.py @@ -36,9 +36,9 @@ default='exact_diffusion' ) parser.add_argument( - '--virtual-topology', type=str, default="power2", + '--virtual-topology', type=str, default="expo2", help='The underlying virtual topology. Supporting options are ' + - '[power2(Default), ring, mesh, star].') + '[expo2(Default), ring, mesh, star].') parser.add_argument( '--computation-mode', type=str, default="normal", @@ -101,7 +101,7 @@ def logistic_loss_step(x_, rho, X, y, tensor_name, calculate_by_hand=True): loss_.backward() return -if args.virtual_topology == "power2": +if args.virtual_topology == "expo2": pass elif args.virtual_topology == "ring": bf.set_topology(topology_util.RingGraph(bf.size(), connect_style=0)) @@ -112,7 +112,7 @@ def logistic_loss_step(x_, rho, X, y, tensor_name, calculate_by_hand=True): bf.set_topology(topology_util.StarGraph(bf.size())) else: raise ValueError("Unknown args.virtual_topology, supporting options are " + - "[power2(Default), ring, mesh, star].") + "[expo2(Default), ring, mesh, star].") # Set up fake data # Generate data for logistic regression (synthesized data) @@ -208,4 +208,4 @@ def log(s, nl=True): print('[%d] Img/sec per %s: %.1f +-%.1f' % (bf.rank(), device, img_sec_mean, img_sec_conf)) log('Total img/sec on %d %s(s): %.1f +-%.1f' % - (bf.size(), device, img_sec_mean_all, img_sec_conf_all)) \ No newline at end of file + (bf.size(), device, img_sec_mean_all, img_sec_conf_all)) diff --git a/scripts/single_ops_test.py b/scripts/single_ops_test.py index a6e1b6ec..ef5caea8 100644 --- a/scripts/single_ops_test.py +++ b/scripts/single_ops_test.py @@ -19,9 +19,9 @@ help='number of benchmark iterations') parser.add_argument('--internal-num-iters', type=int, default=10, help='number of single ops in one benchmark step') -parser.add_argument('--virtual-topology', type=str, default="power2", +parser.add_argument('--virtual-topology', type=str, default="expo2", help='The underlying virtual topology. Supporting options are ' + - '[power2(Default), ring, mesh, star].') + '[expo2(Default), ring, mesh, star].') parser.add_argument('--seed', type=int, default=2020, help='Seed for randomness.') parser.add_argument('--profiler', action='store_true', default=False, help='disables profiler') @@ -32,12 +32,12 @@ data = torch.randn(args.data_size) bf.init() -if args.virtual_topology == "power2": +if args.virtual_topology == "expo2": pass -elif args.virtual_topology == "power3": - bf.set_topology(topology_util.PowerGraph(bf.size(), base=3)) -elif args.virtual_topology == "power4": - bf.set_topology(topology_util.PowerGraph(bf.size(), base=4)) +elif args.virtual_topology == "expo3": + bf.set_topology(topology_util.ExponentialGraph(bf.size(), base=3)) +elif args.virtual_topology == "expo4": + bf.set_topology(topology_util.ExponentialGraph(bf.size(), base=4)) elif args.virtual_topology == "ring": bf.set_topology(topology_util.RingGraph(bf.size(), connect_style=0)) elif args.virtual_topology == "mesh": @@ -49,7 +49,7 @@ bf.set_topology(topology_util.FullyConnectedGraph(bf.size())) else: raise ValueError("Unknown args.virtual_topology, supporting options are " + - "[power2(Default), ring, mesh, star].") + "[expo2(Default), ring, mesh, star].") def benchmark_step(): global args, data diff --git a/test/tensorflow_basics_test.py b/test/tensorflow_basics_test.py index dccee249..d867cfd0 100644 --- a/test/tensorflow_basics_test.py +++ b/test/tensorflow_basics_test.py @@ -26,7 +26,7 @@ from common import mpi_env_rank_and_size import bluefog.tensorflow as bf -from bluefog.common.topology_util import PowerGraph, RingGraph +from bluefog.common.topology_util import ExponentialGraph, RingGraph warnings.filterwarnings("ignore", message="numpy.dtype size changed") warnings.filterwarnings("ignore", message="numpy.ufunc size changed") @@ -67,17 +67,17 @@ def test_set_and_load_topology(self): elif size == 1: expected_topology = nx.DiGraph(np.array([[1.0]])) else: - expected_topology = PowerGraph(size) + expected_topology = ExponentialGraph(size) bf.init() topology = bf.load_topology() assert isinstance(topology, nx.DiGraph) np.testing.assert_array_equal( nx.to_numpy_array(expected_topology), nx.to_numpy_array(topology)) - def test_in_out_neighbors_power2(self): + def test_in_out_neighbors_expo2(self): rank, size = mpi_env_rank_and_size() bf.init() - bf.set_topology(PowerGraph(size)) + bf.set_topology(ExponentialGraph(size)) in_neighobrs = bf.in_neighbor_ranks() out_neighbors = bf.out_neighbor_ranks() diff --git a/test/torch_basics_test.py b/test/torch_basics_test.py index 921051e8..4494f3d2 100644 --- a/test/torch_basics_test.py +++ b/test/torch_basics_test.py @@ -28,7 +28,7 @@ from common import mpi_env_rank_and_size import bluefog.torch as bf -from bluefog.common.topology_util import PowerGraph, RingGraph, RingGraph +from bluefog.common.topology_util import ExponentialGraph, RingGraph, RingGraph from bluefog.common.topology_util import IsTopologyEquivalent warnings.filterwarnings("ignore", message="numpy.dtype size changed") @@ -87,7 +87,7 @@ def test_set_topology_fail_with_win_create(self): topology = bf.load_topology() assert isinstance(topology, nx.DiGraph) - assert IsTopologyEquivalent(topology, PowerGraph(size)) + assert IsTopologyEquivalent(topology, ExponentialGraph(size)) is_freed = bf.win_free() assert is_freed, "bf.win_free do not free window object successfully." @@ -103,16 +103,16 @@ def test_set_and_load_topology(self): elif size == 1: expected_topology = nx.DiGraph(np.array([[1.0]])) else: - expected_topology = PowerGraph(size) + expected_topology = ExponentialGraph(size) topology = bf.load_topology() assert isinstance(topology, nx.DiGraph) assert IsTopologyEquivalent(expected_topology, topology) - def test_in_out_neighbors_power2(self): + def test_in_out_neighbors_expo2(self): bf.init() rank = bf.rank() size = bf.size() - assert bf.set_topology(PowerGraph(size)) + assert bf.set_topology(ExponentialGraph(size)) in_neighobrs = bf.in_neighbor_ranks() out_neighbors = bf.out_neighbor_ranks() diff --git a/test/torch_ops_test.py b/test/torch_ops_test.py index 7be827dd..49931cc8 100644 --- a/test/torch_ops_test.py +++ b/test/torch_ops_test.py @@ -371,7 +371,7 @@ def test_neighbor_allreduce_sum_precision(self): if TEST_ON_GPU: dtypes += [torch.cuda.DoubleTensor] - # By default, we use power two ring topology. + # By default, we use exponential two ring topology. num_indegree = int(np.ceil(np.log2(size))) neighbor_ranks = [(rank - 2**i) % size for i in range(num_indegree)] sum_value = np.sum(neighbor_ranks) + rank @@ -404,7 +404,7 @@ def test_neighbor_allreduce_avg_precision(self): if TEST_ON_GPU: dtypes += [torch.cuda.DoubleTensor] - # By default, we use power two ring topology. + # By default, we use exponential two ring topology. num_indegree = int(np.ceil(np.log2(size))) neighbor_ranks = [(rank - 2**i) % size for i in range(num_indegree)] sum_value = np.sum(neighbor_ranks) + rank @@ -435,7 +435,7 @@ def test_neighbor_allreduce_dynamic_topo_check(self): if TEST_ON_GPU: dtypes += [torch.cuda.FloatTensor, torch.cuda.DoubleTensor] - # By default, we use power two ring topology. + # By default, we use exponential two ring topology. self_weight = 0.0 neighbor_weights = {(rank-1) % size : 1.0} send_ranks = [(rank + 2) % size] @@ -461,7 +461,7 @@ def test_neighbor_allreduce_dynamic_topo_move(self): if TEST_ON_GPU: dtypes += [torch.cuda.FloatTensor, torch.cuda.DoubleTensor] - # By default, we use power two ring topology. + # By default, we use exponential two ring topology. self_weight = 0.0 neighbor_weights = {(rank-1) % size : 1.0} send_ranks = [(rank + 1) % size] @@ -495,7 +495,7 @@ def test_neighbor_allreduce_dynamic_topo_avg(self): if TEST_ON_GPU: dtypes += [torch.cuda.FloatTensor, torch.cuda.DoubleTensor] - # By default, we use power two ring topology. + # By default, we use exponential two ring topology. num_indegree = int(np.ceil(np.log2(size))) neighbor_ranks = [(rank - 2**i) % size for i in range(num_indegree)] sum_value = np.sum(neighbor_ranks) + rank @@ -534,7 +534,7 @@ def test_neighbor_allreduce_avg(self): if TEST_ON_GPU: dtypes += [torch.cuda.FloatTensor, torch.cuda.DoubleTensor] - # By default, we use power two ring topology. + # By default, we use exponential two ring topology. num_indegree = int(np.ceil(np.log2(size))) neighbor_ranks = [(rank - 2**i) % size for i in range(num_indegree)] sum_value = np.sum(neighbor_ranks) + rank @@ -727,7 +727,7 @@ def test_neighbor_allreduce_sum(self): if TEST_ON_GPU: dtypes += [torch.cuda.FloatTensor, torch.cuda.DoubleTensor] - # By default, we use power two ring topology. + # By default, we use exponential two ring topology. num_indegree = int(np.ceil(np.log2(size))) neighbor_ranks = [(rank - 2**i) % size for i in range(num_indegree)] sum_value = np.sum(neighbor_ranks) + rank @@ -795,7 +795,7 @@ def test_neighbor_allreduce_fusion(self): if TEST_ON_GPU: dtypes += [torch.cuda.FloatTensor, torch.cuda.DoubleTensor] - # By default, we use power two ring topology. + # By default, we use exponential two ring topology. num_indegree = int(np.ceil(np.log2(size))) neighbor_ranks = [(rank - 2**i) % size for i in range(num_indegree)] sum_value = np.sum(neighbor_ranks) + rank @@ -846,7 +846,7 @@ def test_neighbor_allreduce_fusion_alot(self): if TEST_ON_GPU: dtypes += [torch.cuda.FloatTensor, torch.cuda.DoubleTensor] - # By default, we use power two ring topology. + # By default, we use exponential two ring topology. num_indegree = int(np.ceil(np.log2(size))) neighbor_ranks = [(rank - 2**i) % size for i in range(num_indegree)] sum_value = np.sum(neighbor_ranks) + rank @@ -892,7 +892,7 @@ def test_neighbor_allreduce_dynamic_topo_fusion(self): if TEST_ON_GPU: dtypes += [torch.cuda.FloatTensor, torch.cuda.DoubleTensor] - # By default, we use power two ring topology. + # By default, we use exponential two ring topology. num_indegree = int(np.ceil(np.log2(size))) neighbor_ranks = [(rank - 2**i) % size for i in range(num_indegree)] sum_value = np.sum(neighbor_ranks) + rank @@ -953,7 +953,7 @@ def test_neighbor_allgather(self): if TEST_ON_GPU: dtypes += [torch.cuda.FloatTensor, torch.cuda.DoubleTensor] - # By default, we use power two ring topology. + # By default, we use exponential two ring topology. num_indegree = int(np.ceil(np.log2(size))) neighbor_ranks = [(rank - 2**i) % size for i in range(num_indegree)] diff --git a/test/torch_win_ops_test.py b/test/torch_win_ops_test.py index d4871fe2..bee65e13 100644 --- a/test/torch_win_ops_test.py +++ b/test/torch_win_ops_test.py @@ -72,7 +72,7 @@ def test_win_create_and_sync_and_free(self): if TEST_ON_GPU: dtypes += [torch.cuda.FloatTensor, torch.cuda.DoubleTensor] - # By default, we use power two ring topology. + # By default, we use exponential two ring topology. dims = [1, 2, 3] for dtype, dim in itertools.product(dtypes, dims): tensor = torch.FloatTensor(*([DIM_SIZE] * dim)).fill_(1).mul_(rank) @@ -233,7 +233,7 @@ def test_win_put(self): if TEST_ON_GPU: dtypes += [torch.cuda.FloatTensor, torch.cuda.DoubleTensor] - # By default, we use power two ring topology. + # By default, we use exponential two ring topology. indegree = int(np.ceil(np.log2(size))) neighbor_ranks = [(rank - 2**i) % size for i in range(indegree)] # in-neighbor @@ -274,7 +274,7 @@ def test_get_win_version_with_win_put(self): if TEST_ON_GPU: dtypes += [torch.cuda.FloatTensor, torch.cuda.DoubleTensor] - # By default, we use power two ring topology. + # By default, we use exponential two ring topology. indegree = int(np.ceil(np.log2(size))) neighbor_ranks = [(rank - 2**i) % size for i in range(indegree)] # in-neighbor @@ -328,7 +328,7 @@ def test_win_put_with_varied_tensor_elements(self): if TEST_ON_GPU: dtypes += [torch.cuda.FloatTensor, torch.cuda.DoubleTensor] - # By default, we use power two ring topology. + # By default, we use exponential two ring topology. indegree = int(np.ceil(np.log2(size))) neighbor_ranks = [(rank - 2**i) % size for i in range(indegree)] # in-neighbor @@ -372,7 +372,7 @@ def test_win_put_with_given_destination(self): if TEST_ON_GPU: dtypes += [torch.cuda.FloatTensor, torch.cuda.DoubleTensor] - # By default, we use power two ring topology. + # By default, we use exponential two ring topology. indegree = int(np.ceil(np.log2(size))) # We use given destination to form a (right-)ring. avg_value = (rank*indegree + 1.23*((rank-1) % @@ -413,7 +413,7 @@ def test_win_accumulate(self): if TEST_ON_GPU: dtypes += [torch.cuda.FloatTensor, torch.cuda.DoubleTensor] - # By default, we use power two ring topology. + # By default, we use exponential two ring topology. outdegree = int(np.ceil(np.log2(size))) neighbor_ranks = [(rank - 2**i) % size for i in range(outdegree)] # in-neighbor @@ -449,7 +449,7 @@ def test_win_accumulate_with_varied_tensor_elements(self): if TEST_ON_GPU: dtypes += [torch.cuda.FloatTensor, torch.cuda.DoubleTensor] - # By default, we use power two ring topology. + # By default, we use exponential two ring topology. outdegree = int(np.ceil(np.log2(size))) neighbor_ranks = [(rank - 2**i) % size for i in range(outdegree)] # in-neighbor @@ -526,7 +526,7 @@ def test_win_get(self): if TEST_ON_GPU: dtypes += [torch.cuda.FloatTensor, torch.cuda.DoubleTensor] - # By default, we use power two ring topology. + # By default, we use exponential two ring topology. indegree = int(np.ceil(np.log2(size))) neighbor_ranks = [(rank - 2**i) % size for i in range(indegree)] # in-neighbor @@ -561,7 +561,7 @@ def test_get_win_version_with_win_get(self): if TEST_ON_GPU: dtypes += [torch.cuda.FloatTensor, torch.cuda.DoubleTensor] - # By default, we use power two ring topology. + # By default, we use exponential two ring topology. indegree = int(np.ceil(np.log2(size))) neighbor_ranks = [(rank - 2**i) % size for i in range(indegree)] # in-neighbor @@ -615,7 +615,7 @@ def test_win_get_with_varied_tensor_elements(self): if TEST_ON_GPU: dtypes += [torch.cuda.FloatTensor, torch.cuda.DoubleTensor] - # By 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zMV+9hUjoIMbB5ndBRoCNPmgL{l)RNj`~R^=1Q&>tRPd6DjJOon)U%UvuPj8o9e$WE5r@%>QbM0wQ@5-`^}_;XPwCQ`G2C=mqI#t`Z8Z7{qh^v z-_ex;JDeZSE&xrMc)*p-vvXjQ*1nj&`6E6{yW-JcZWSQ-u#K)G;NAjTvUG$KI2Iy5KgrRqKO$dAq~I!mHwRz%Z>v~8Vu-z(QmQ( z-%XxhS)M{+>|qA2g(ACuS7843T7LWJe_3D?=UQ^_Z;#`jS)$8T1(Po|T#pW_>5oeu sU)Hp+&fWX?w+- Bluefog Performance - Static and Dynamic Neighbor Averaging + Static and Dynamic Topology Neighbor Averaging Lauching Application Through bfrun Bluefog Docker Usage Bluefog Environment Variable diff --git a/docs/neighbor_average.rst b/docs/neighbor_average.rst index 003f4fd5..eb147b0a 100644 --- a/docs/neighbor_average.rst +++ b/docs/neighbor_average.rst @@ -1,3 +1,99 @@ -Static and Dynamic Neighbor Averaging -===================================== +Static and Dynamic Topology Neighbor Averaging +============================================== +The most distinguishable feature of Bluefog compared with other popular distributed training frameworks, such as +DistributedDataParallel provided by pytorch, Horovod, BytePS, etc., is that our core implementation rooted on the idea +that we introduce the virtual topology into the multiple processes and use the neighbor\_allreduce as +basic operation instead of allreduce. + +The core idea is very simple: all nodes/processes are connected through a virtual topology and they +fetch the information from their direct neighbors only, then do the average reduce operation. Although +the concept is simple, there are lots of deep research and theorems built upon it. Here we just touch +the most simple case to understand the basics. But let's run some code snippet to gain some insights first: + +BlueFog Code for Static and Dynamic Topology +-------------------------------------------- + +The BlueFog APIs are designed for easy use of the static and dynamic topology. +For simple static topology usage: + +.. code-block:: python + + import torch + import bluefog.torch as bf + from bluefog.common import topology_util + + bf.init() # Default topology is Exponential-2 graph + bf.set_topology(topology_util.RingGraph(bf.size())) # change to ring graph. + x = torch.Tensor([bf.rank()]) + for _ in range(100): + x = bf.neighbor_allreduce(x) # Use previous set ring graph. + print("rank {} has x={}".format(bf.rank(), x)) + +For dynamic topology case, especially dynamic Exponential 2 graph as we recommended +for most cases, you need a little bit more code: + +.. image:: _static/one-peer-exp2.png + :width: 600 + :align: center + +.. code-block:: python + + import torch + import bluefog.torch as bf + from bluefog.common import topology_util + + bf.init() + # Since Default topology is Exponential-2, the following utility function + # will generate one-peer dynamic Exponential-2 topology. + dynamic_neighbors = topology_util.GetDynamicSendRecvRanks( + bf.load_topology(), bf.rank()) + x = torch.Tensor([bf.rank()]) + for _ in range(maxite): + to_neighbors, from_neighbors = next(dynamic_neighbors) + avg_weight = 1/(len(from_neighbors) + 1) + x = bf.neighbor_allreduce(x, name='x', + self_weight=avg_weight, + neighbor_weights={r: avg_weight for r in from_neighbors}, + send_neighbors=to_neighbors) + +Notice here we use extra argument in the ``neighbor_allreduce`` function to change its behavior. +Although here we use the pre-defined utility function, you can implementation your own dynamic topology +function easily because all information it returns is just ranks (integers). + +Last note is ``hierarchical_neighbor_allreduce`` is defined over machine topology instead of previous +rank/processes topology. The usage is similar by just chaning rank integers into machine integers. + + + +Basic Theorem +------------- + +Formally, consider a digraph :math:`G=(V,E)`, where :math:`|V|=n`, +and weight matrix :math:`W` defined on :math:`G`, that is, :math:`w_{ij}\neq 0` +only for :math:`(j,i)\in E` and :math:`j=i`. +Neighbor average of vector :math:`x_1,\dots,x_n` defined over graph :math:`G` is equivalent to the +matrix multiplication: + +.. math:: + + W\mathbf{x} = W + \begin{bmatrix} + \text{---} & x_1^T & \text{---}\\ + & \cdots & \\ + \text{---} & x_n^T & \text{---} + \end{bmatrix}. + +**Theorem**: The sequence :math:`\mathbf{x}^{(k)} = W\mathbf{x}^{(k-1)}` converges to :math:`\mathbf{x}^\star=\frac{1}{n}\left(x_1+\dots+x_n\right)` +for all :math:`\mathbf{x}` if + +* :math:`W^{(k)}\mathbf{1} = \mathbf{1}`, +* :math:`\mathbf{1}^T W^{(k)}=\mathbf{1}^T`, +* :math:`W` has eigenvalues: :math:`\lambda_1=1 > |\lambda_2| \ge \dots \ge |\lambda_n| > -1`. + +Also, :math:`\|\mathbf{x}^{(k)}-\mathbf{x}^\star\|_2\le \underbrace{\max(|\lambda_2|,|\lambda_n|)}_{=:\rho}\|\mathbf{x}-\mathbf{x}^\star\|_2`. +Smaller :math:`\rho` means faster convergence. + +For dynamic topology, the combination matrix should change to :math:`W^{(k)}` and the +convergence condition becomes more complicated but similar. You can refer our papers to see more +theoratical guarantee.

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c3*XYIsQUYJ9Xnq=A+?LbV9xhRQyhQ#Z--g)MF0Q* literal 0 HcmV?d00001 diff --git a/docs/bluefog_ops.rst b/docs/bluefog_ops.rst index 9523de37..04c92cf2 100644 --- a/docs/bluefog_ops.rst +++ b/docs/bluefog_ops.rst @@ -82,6 +82,8 @@ Hierarchical Collective Ops --------------------------- In practice, the communication speed and behavior is different between intra-machine and inter-machine communcation. Hence, we also provided two hierarchical collective ops. The basic unit in this case is each (physical) machine. +Hence, unlike previous neighbor collective ops, of which the topology is defined over the connection between ranks/processes, +the topology of hierarchical collective ops is defined over the connection between machines. hierarchical_local_allreduce ############################ @@ -89,17 +91,27 @@ hierarchical_local_allreduce :alt: BluefogHierarchicalLocalAllreduceExplanation :width: 700 +Because it is (machine) local operation, the topology definition will not impact this operation. Hence, the rank 0 and rank 1 +simply applied the local allreduce average to get (8+4)/2 = 6. Other ranks is like-wise. + hierarchical_neighbor_allreduce ############################### .. image:: _static/bf_hier_neighbor_allreduce.png :alt: BluefogHierarchicalNeighborAllreduceExplanation :width: 700 -The basic logics under hierarchical operations is, within the machine, each local rank performances the local allreduce -operation first so that in the view of external machines, all processes within same machine forms a super node. +Similar to the *hierarchical_local_allreduce* operation, it first applied the local allreduce average within the machine. +So that in the view of external machines, all processes within same machine forms a super node. Then, the super node exchange the +information with their neighbor machines like *neighbor_allreduce*. For example, machine 0, 2, and 3 first formed a local average value +6, 3, and 3 respectively. Then, a machine-wise neighbor allreduce produce (6+3+3)/3 = 4. + +In order to minimize the cross machines communcation, the real implementation is four steps actually: 1. Local Average. 2. All local rank +0 processes do the *neihbor_allreduce*. 3. local rank 0 processes broadcast the received tensors to other local ranks. 4. Compute the average of +received neighbor tensors within the process. + .. warning:: - hierarchical_neighbor_allreduce only should be used under the homogeneous environment only, i.e. each machine owns same number of + hierarchical_neighbor_allreduce should be used under the homogeneous environment only, i.e., each machine owns same number of the local processes. diff --git a/docs/env_variable.rst b/docs/env_variable.rst index 1c8f5930..e8b1f1c6 100644 --- a/docs/env_variable.rst +++ b/docs/env_variable.rst @@ -26,6 +26,15 @@ You can also hide the time in logs by setting: Performance ----------- +Our implementation is based upon the architecture of Horovod. Hence, we also have the tensor fusion functionality, which +batches small allreduce/neighbor_allreduce operations into one to improve the performance. Cycle time is anthor tuning parameter +to determine within the certain period of time, the ready tensor will be fused into one if it doesnot exceed the threshold of fusion. + +* BLUEFOG_FUSION_THRESHOLD +* BLUEFOG_CYCLE_TIME + +The fusion threshold is based on the Byte size and cycle time is based on the milliseconds. + **Timeline**: You can set `BLUEFOG_TIMELINE` with some filename to turn on the timeline. See our timeline document for more details. @@ -78,7 +87,7 @@ define this number as the maximum size of win_ops can send. When the NCCL implementation is used, the callback functions are executed through a thread pool. The size of thread pool can be controlled by following: -* BLUEFOG_NUM_FINALIZER_THREADS (Default: 50) +* BLUEFOG_NUM_FINALIZER_THREADS (Default: 1) Installation ------------ diff --git a/docs/install.rst b/docs/install.rst index 24abd187..3ca67c2e 100644 --- a/docs/install.rst +++ b/docs/install.rst @@ -33,13 +33,19 @@ After you think the environment is all set, just run following command to instal Installing Bluefog from Pip (GPU) --------------------------------- All steps for GPU case are the same as CPU case except for the OpenMPI installation. -In order to get full support of GPU, you have to install `CUDA>=10.0` -and install pytorch and/or tensorflow with the GPU support version. -To maximize the efficiency of GPU and MPI, our implementation assumes the -MPI installed is GPU-aware if GPU is available. It will avoid the extra cost -that copy and moving the data from the GPU and host memory, i.e. the address of -GPU location can be used directly. However, if MPI built is not GPU-aware, -there will be a segmentation fault. To do that, you can configure the open install setting +In order to get full support of GPU, you have to install `CUDA>=10.1` +and install pytorch with the GPU support version. +It is highly recommended to use NCCL instead of OpenMPI as GPU +communication implementation. We require the ``NCCL>=2.7`` since our implementation heavily +relied on the new `ncclSend` and `ncclRecv` API introduced after version 2.7. +To install Bluefog with NCCL implementation, you need to run + +.. code-block:: bash + + BLUEFOG_WITH_NCCL=1 pip install --no-cache-dir bluefog + +You can also install the GPU-aware OpenMPI as GPU communication implementation. +To do that, you can configure the open install setting after the download of OpenMPI: .. code-block:: bash @@ -48,16 +54,15 @@ after the download of OpenMPI: make -j $(nproc) all && \ make install -If you have NCCL installed in your system, it is also recommend to use NCCL instead of OpenMPI as -communication implementation. To install Bluefog with NCCL implementation, you need to run -.. code-block:: bash +Installing Bluefog from Github Directly +--------------------------------------- - BLUEFOG_WITH_NCCL=1 pip install --no-cache-dir bluefog +.. important:: + The lastest functionality may not be able avaiable through ``pip install bluefog``. + Installing from Github is better during our early development stage. -Installing Bluefog from Github Directly ---------------------------------------- First, please check your environment as mentioned in above subsections. Then, clone or download the bluefog repository from `Github`_. Last, just run the following command under the root folder of bluefog repository: From 618a2bee31a5d0fcd5bb9a5b97ca6fb77f78f42b Mon Sep 17 00:00:00 2001 From: ybc Date: Mon, 2 Nov 2020 21:28:43 -0800 Subject: [PATCH 42/44] Merge Imagenet and Cifar 10 example together and remove pair_gossip --- bluefog/torch/__init__.py | 1 - bluefog/torch/mpi_ops.py | 3 + bluefog/torch/utility.py | 6 +- examples/pytorch_ImageNet_Resnet50.py | 343 ------------------ examples/pytorch_benchmark.py | 4 +- ...ch_cifar10_resnet.py => pytorch_resnet.py} | 99 +++-- test/torch_ops_test.py | 8 +- 7 files changed, 73 insertions(+), 391 deletions(-) delete mode 100644 examples/pytorch_ImageNet_Resnet50.py rename examples/{pytorch_cifar10_resnet.py => pytorch_resnet.py} (83%) diff --git a/bluefog/torch/__init__.py b/bluefog/torch/__init__.py index 1f5c47b3..fdf66f9a 100644 --- a/bluefog/torch/__init__.py +++ b/bluefog/torch/__init__.py @@ -49,7 +49,6 @@ from bluefog.torch.mpi_ops import neighbor_allreduce, neighbor_allreduce_nonblocking from bluefog.torch.mpi_ops import hierarchical_neighbor_allreduce from bluefog.torch.mpi_ops import hierarchical_neighbor_allreduce_nonblocking -from bluefog.torch.mpi_ops import pair_gossip, pair_gossip_nonblocking from bluefog.torch.mpi_ops import poll, synchronize, barrier from bluefog.torch.mpi_ops import win_create, win_free diff --git a/bluefog/torch/mpi_ops.py b/bluefog/torch/mpi_ops.py index 24a4a1fc..234b8039 100644 --- a/bluefog/torch/mpi_ops.py +++ b/bluefog/torch/mpi_ops.py @@ -711,6 +711,9 @@ def _hierarchical_neighbor_allreduce_nonblocking( else: raise ValueError("Arguments self_weight and neighbor_weights cannot be empty or None.") + if not send_neighbor_machines: + raise ValueError("Argument send_neighbor_machines has to be presented and non-empty.") + machine_size = size() // local_size() # Translate machine id into rank id. node_per_machine = local_size() diff --git a/bluefog/torch/utility.py b/bluefog/torch/utility.py index 15a5f343..b4e05238 100644 --- a/bluefog/torch/utility.py +++ b/bluefog/torch/utility.py @@ -107,13 +107,13 @@ def broadcast_optimizer_state(optimizer, root_rank): # forces allreduce on all model parameters, which will result in deadlock # unless every rank calls step(). Therefore, to finish state initialization # only call optimizer.step() with a torch.optim.Optimizer. - if optimizer.__module__ == bf.DistributedAllreduceOptimizer.__module__: + if optimizer.__module__ == bf.DistributedHierarchicalNeighborAllreduceOptimizer.__module__: super(optimizer.__class__, optimizer).step() elif optimizer.__module__ == bf.DistributedNeighborAllreduceOptimizer.__module__: super(optimizer.__class__, optimizer).step() - elif optimizer.__module__ == bf.DistributedPushSumOptimizer.__module__: + elif optimizer.__module__ == bf.DistributedAllreduceOptimizer.__module__: super(optimizer.__class__, optimizer).step() - elif optimizer.__module__ == bf.DistributedPullGetOptimizer.__module__: + elif optimizer.__module__ == bf.DistributedGradientAllreduceOptimizer.__module__: super(optimizer.__class__, optimizer).step() elif optimizer.__module__ == bf.DistributedWinPutOptimizer.__module__: super(optimizer.__class__, optimizer).step() diff --git a/examples/pytorch_ImageNet_Resnet50.py b/examples/pytorch_ImageNet_Resnet50.py deleted file mode 100644 index 879e9613..00000000 --- a/examples/pytorch_ImageNet_Resnet50.py +++ /dev/null @@ -1,343 +0,0 @@ -# Modifications copyright (C) 2020 Bluefog Team. All Rights Reserved. -# Copyright 2019 Uber Technologies, Inc. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== - -import argparse -import os -import math - -import torch -import torch.backends.cudnn as cudnn -import torch.nn.functional as F -import torch.optim as optim -import torch.utils.data.distributed -from torchvision import datasets, transforms, models -import tensorboardX -from tqdm import tqdm - -from bluefog.common import topology_util -import bluefog.torch as bf - -# Training settings -parser = argparse.ArgumentParser(description='PyTorch ImageNet Example', - formatter_class=argparse.ArgumentDefaultsHelpFormatter) -parser.add_argument('--train-dir', default=os.path.expanduser('~/imagenet/train'), - help='path to training data') -parser.add_argument('--val-dir', default=os.path.expanduser('~/imagenet/validation'), - help='path to validation data') -parser.add_argument('--log-dir', default='./logs', - help='tensorboard log directory') -parser.add_argument('--checkpoint-format', default='./checkpoint-{epoch}.pth.tar', - help='checkpoint file format') -parser.add_argument('--batches-per-allreduce', type=int, default=1, - help='number of batches processed locally before ' - 'executing allreduce across workers; it multiplies ' - 'total batch size.') - -# Default settings from https://arxiv.org/abs/1706.02677. -parser.add_argument('--batch-size', type=int, default=32, - help='input batch size for training') -parser.add_argument('--val-batch-size', type=int, default=32, - help='input batch size for validation') -parser.add_argument('--epochs', type=int, default=90, - help='number of epochs to train') -parser.add_argument('--base-lr', type=float, default=0.0125, - help='learning rate for a single GPU') -parser.add_argument('--warmup-epochs', type=float, default=5, - help='number of warmup epochs') -parser.add_argument('--momentum', type=float, default=0.9, - help='SGD momentum') -parser.add_argument('--wd', type=float, default=0.00005, - help='weight decay') - -parser.add_argument('--no-cuda', action='store_true', default=False, - help='disables CUDA training') -parser.add_argument('--seed', type=int, default=42, - help='random seed') -parser.add_argument('--dist-optimizer', type=str, default='neighbor_allreduce', - help='The type of distributed optimizer. Supporting options are ' + - '[neighbor_allreduce, allreduce, win_put, horovod]') -parser.add_argument("--disable-dynamic-topology", action="store_true", - default=False, help=("Disable each iteration to transmit one neighbor " + - "per iteration dynamically.")) -parser.add_argument('--virtual-topology', type=str, default="expo2", - help='The underlying virtual topology. Supporting options are ' + - '[expo2(Default), ring, mesh, star].') - -args = parser.parse_args() -args.cuda = not args.no_cuda and torch.cuda.is_available() -allreduce_batch_size = args.batch_size * args.batches_per_allreduce - -if args.dist_optimizer == 'horovod': - print("importing horovod") - import horovod.torch as bf - -bf.init() -torch.manual_seed(args.seed) - -if args.dist_optimizer != 'horovod': - if args.virtual_topology == "expo2": - pass - elif args.virtual_topology == "ring": - bf.set_topology(topology_util.RingGraph(bf.size(), connect_style=1)) - else: - raise ValueError("Unknown args.virtual_topology, supporting options are " + - "[expo2(Default), ring, mesh, star].") - -if args.cuda: - # Bluefog: pin GPU to local rank. - torch.cuda.set_device(bf.local_rank()) - torch.cuda.manual_seed(args.seed) - -cudnn.benchmark = True - -# If set > 0, will resume training from a given checkpoint. -resume_from_epoch = 0 -for try_epoch in range(args.epochs, 0, -1): - if os.path.exists(args.checkpoint_format.format(epoch=try_epoch)): - resume_from_epoch = try_epoch - break - -# Bluefog: broadcast resume_from_epoch from rank 0 (which will have -# checkpoints) to other ranks. -resume_from_epoch = bf.broadcast(torch.tensor(resume_from_epoch), root_rank=0, - name='resume_from_epoch').item() - -# Bluefog: print logs on the first worker. -verbose = 1 if bf.rank() == 0 else 0 - -# Bluefog: write TensorBoard logs on first worker. -log_writer = tensorboardX.SummaryWriter(args.log_dir) if bf.rank() == 0 else None - - -# Bluefog: limit # of CPU threads to be used per worker. -torch.set_num_threads(4) - -kwargs = {'num_workers': 4, 'pin_memory': True} if args.cuda else {} -train_dataset = \ - datasets.ImageFolder(args.train_dir, - transform=transforms.Compose([ - transforms.RandomResizedCrop(224), - transforms.RandomHorizontalFlip(), - transforms.ToTensor(), - transforms.Normalize(mean=[0.485, 0.456, 0.406], - std=[0.229, 0.224, 0.225]) - ])) -# Bluefog: use DistributedSampler to partition data among workers. Manually specify -# `num_replicas=bf.size()` and `rank=bf.rank()`. -train_sampler = torch.utils.data.distributed.DistributedSampler( - train_dataset, num_replicas=bf.size(), rank=bf.rank()) -train_loader = torch.utils.data.DataLoader( - train_dataset, batch_size=allreduce_batch_size, - sampler=train_sampler, **kwargs) - -val_dataset = \ - datasets.ImageFolder(args.val_dir, - transform=transforms.Compose([ - transforms.Resize(256), - transforms.CenterCrop(224), - transforms.ToTensor(), - transforms.Normalize(mean=[0.485, 0.456, 0.406], - std=[0.229, 0.224, 0.225]) - ])) -val_sampler = torch.utils.data.distributed.DistributedSampler( - val_dataset, num_replicas=bf.size(), rank=bf.rank()) -val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=args.val_batch_size, - sampler=val_sampler, **kwargs) - - -# Set up standard ResNet-50 model.Ã¥ -model = models.resnet50() - -# For sum/average with gradient Accumulation: scale learning rate by batches_per_allreduce -lr_scaler = args.batches_per_allreduce * bf.size() if not args.use_adasum else 1 - -if args.cuda: - # Move model to GPU. - model.cuda() - -# Bluefog: scale learning rate by the number of GPUs. -optimizer = optim.SGD(model.parameters(), - lr=(args.base_lr * - lr_scaler), - momentum=args.momentum, weight_decay=args.wd) - -# Bluefog: wrap optimizer with DistributedOptimizer. -if args.dist_optimizer == 'win_put': - optimizer = bf.DistributedWinPutOptimizer(optimizer, model=model) -elif args.dist_optimizer == 'neighbor_allreduce': - optimizer = optimizer = bf.DistributedNeighborAllreduceOptimizer( - optimizer, model=model) -elif args.dist_optimizer == 'allreduce': - optimizer = optimizer = bf.DistributedAllreduceOptimizer( - optimizer, model=model) -elif args.dist_optimizer == 'horovod': - optimizer = optimizer = bf.DistributedOptimizer( - optimizer, named_parameters=model.named_parameters() - ) -else: - raise ValueError('Unknown args.dist-optimizer type -- ' + args.dist_optimizer + '\n' + - 'Please set the argument to be one of ' + - '[neighbor_allreduce, allreduce, win_put, horovod]') - -# Restore from a previous checkpoint, if initial_epoch is specified. -# Bluefog: restore on the first worker which will broadcast weights to other workers. -if resume_from_epoch > 0 and bf.rank() == 0: - filepath = args.checkpoint_format.format(epoch=resume_from_epoch) - checkpoint = torch.load(filepath) - model.load_state_dict(checkpoint['model']) - optimizer.load_state_dict(checkpoint['optimizer']) - -# Bluefog: broadcast parameters & optimizer state. -bf.broadcast_parameters(model.state_dict(), root_rank=0) -bf.broadcast_optimizer_state(optimizer, root_rank=0) - -def train(epoch): - model.train() - train_sampler.set_epoch(epoch) - train_loss = Metric('train_loss') - train_accuracy = Metric('train_accuracy') - - with tqdm(total=len(train_loader), - desc='Train Epoch #{}'.format(epoch + 1), - disable=not verbose) as t: - for batch_idx, (data, target) in enumerate(train_loader): - adjust_learning_rate(epoch, batch_idx) - - if args.cuda: - data, target = data.cuda(), target.cuda() - optimizer.zero_grad() - # Split data into sub-batches of size batch_size - for i in range(0, len(data), args.batch_size): - data_batch = data[i:i + args.batch_size] - target_batch = target[i:i + args.batch_size] - output = model(data_batch) - train_accuracy.update(accuracy(output, target_batch)) - loss = F.cross_entropy(output, target_batch) - train_loss.update(loss) - # Average gradients among sub-batches - loss.div_(math.ceil(float(len(data)) / args.batch_size)) - loss.backward() - # Gradient is applied across all ranks - optimizer.step() - t.set_postfix({'loss': train_loss.avg.item(), - 'accuracy': 100. * train_accuracy.avg.item()}) - t.update(1) - - if log_writer: - log_writer.add_scalar('train/loss', train_loss.avg, epoch) - log_writer.add_scalar('train/accuracy', train_accuracy.avg, epoch) - - -def validate(epoch): - model.eval() - val_loss = Metric('val_loss') - val_accuracy = Metric('val_accuracy') - - with tqdm(total=len(val_loader), - desc='Validate Epoch #{}'.format(epoch + 1), - disable=not verbose) as t: - with torch.no_grad(): - for data, target in val_loader: - if args.cuda: - data, target = data.cuda(), target.cuda() - output = model(data) - - val_loss.update(F.cross_entropy(output, target)) - val_accuracy.update(accuracy(output, target)) - t.set_postfix({'loss': val_loss.avg.item(), - 'accuracy': 100. * val_accuracy.avg.item()}) - t.update(1) - - if log_writer: - log_writer.add_scalar('val/loss', val_loss.avg, epoch) - log_writer.add_scalar('val/accuracy', val_accuracy.avg, epoch) - - -# Bluefog: using `lr = base_lr * bf.size()` from the very beginning leads to worse final -# accuracy. Scale the learning rate `lr = base_lr` ---> `lr = base_lr * bf.size()` during -# the first five epochs. See https://arxiv.org/abs/1706.02677 for details. -# After the warmup reduce learning rate by 10 on the 30th, 60th and 80th epochs. -def adjust_learning_rate(epoch, batch_idx): - if epoch < args.warmup_epochs: - epoch += float(batch_idx + 1) / len(train_loader) - lr_adj = 1. / bf.size() * (epoch * (bf.size() - 1) / args.warmup_epochs + 1) - elif epoch < 30: - lr_adj = 1. - elif epoch < 60: - lr_adj = 1e-1 - elif epoch < 80: - lr_adj = 1e-2 - else: - lr_adj = 1e-3 - for param_group in optimizer.param_groups: - param_group['lr'] = args.base_lr * bf.size() * args.batches_per_allreduce * lr_adj - -if not args.disable_dynamic_topology and args.dist_optimizer != 'horovod': - dynamic_neighbor_allreduce_gen = topology_util.GetDynamicSendRecvRanks( - bf.load_topology(), bf.rank()) - -def dynamic_topology_update(epoch, batch_idx): - if args.dist_optimizer == 'win_put': - if epoch < 3: - return - num_out_neighbors = len(bf.out_neighbor_ranks()) - sent_neighbor = bf.out_neighbor_ranks()[batch_idx % num_out_neighbors] - optimizer.dst_weights = {sent_neighbor: 1.0} - elif args.dist_optimizer == 'neighbor_allreduce': - send_neighbors, recv_neighbors = next(dynamic_neighbor_allreduce_gen) - optimizer.send_neighbors = send_neighbors - optimizer.neighbor_weights = {r: 1/(len(recv_neighbors) + 1) for r in recv_neighbors} - optimizer.self_weight = 1 / (len(recv_neighbors) + 1) - optimizer.enable_topo_check = False - else: - pass - -def accuracy(output, target): - # get the index of the max log-probability - pred = output.max(1, keepdim=True)[1] - return pred.eq(target.view_as(pred)).cpu().float().mean() - - -def save_checkpoint(epoch): - if bf.rank() == 0: - filepath = args.checkpoint_format.format(epoch=epoch + 1) - state = { - 'model': model.state_dict(), - 'optimizer': optimizer.state_dict(), - } - torch.save(state, filepath) - - -# Bluefog: average metrics from distributed training. -class Metric(object): - def __init__(self, name): - self.name = name - self.sum = torch.tensor(0.) - self.n = torch.tensor(0.) - - def update(self, val): - self.sum += bf.allreduce(val.detach().cpu(), name=self.name) - self.n += 1 - - @property - def avg(self): - return self.sum / self.n - - -for epoch in range(resume_from_epoch, args.epochs): - train(epoch) - validate(epoch) - save_checkpoint(epoch) diff --git a/examples/pytorch_benchmark.py b/examples/pytorch_benchmark.py index 5244fa91..81fe27e6 100644 --- a/examples/pytorch_benchmark.py +++ b/examples/pytorch_benchmark.py @@ -78,11 +78,11 @@ elif args.virtual_topology == "ring": bf.set_topology(topology_util.RingGraph(bf.size(), connect_style=1)) elif args.virtual_topology == "InnerOuterRing": - assert bf.is_homogeneous, "InnerOuterRing topo should be used only homogeneous environment" + assert bf.is_homogeneous, "InnerOuterRing should be used only homogeneous environment" bf.set_topology(topology_util.InnerOuterRingGraph( bf.size(), local_size=bf.local_size() if args.local_size == -1 else args.local_size)) elif args.virtual_topology == "InnerOuterExpo2": - assert bf.is_homogeneous, "InnerOuterExpo2 topo should be used under homogeneous environment" + assert bf.is_homogeneous, "InnerOuterExpo2 should be used under homogeneous environment" bf.set_topology(topology_util.InnerOuterExpo2Graph( bf.size(), local_size=bf.local_size() if args.local_size == -1 else args.local_size)) else: diff --git a/examples/pytorch_cifar10_resnet.py b/examples/pytorch_resnet.py similarity index 83% rename from examples/pytorch_cifar10_resnet.py rename to examples/pytorch_resnet.py index d02ca894..654608ef 100644 --- a/examples/pytorch_cifar10_resnet.py +++ b/examples/pytorch_resnet.py @@ -53,6 +53,12 @@ 'total batch size.') parser.add_argument('--model', type=str, default='resnet18', help='model to benchmark') +parser.add_argument('--dataset', type=str, default='cifar10', + help='The dataset to train with.') +parser.add_argument('--train-dir', default=os.path.expanduser('~/imagenet/train'), + help='path to training data') +parser.add_argument('--val-dir', default=os.path.expanduser('~/imagenet/validation'), + help='path to validation data') # Default settings from https://arxiv.org/abs/1706.02677. parser.add_argument('--batch-size', type=int, default=32, @@ -77,10 +83,6 @@ parser.add_argument('--dist-optimizer', type=str, default='neighbor_allreduce', help='The type of distributed optimizer. Supporting options are [win_put, ' + 'neighbor_allreduce, allreduce, hierarchical_neighbor_allreduce, horovod]') -parser.add_argument("--average-test-result", action="store_true", - default=False, - help=("Allreduce called to average test result. Warning this will " + - "force the algorithm to sync every end of epoch.")) parser.add_argument('--disable-dynamic-topology', action='store_true', default=False, help=('Disable each iteration to transmit one neighbor ' + 'per iteration dynamically.')) @@ -135,18 +137,30 @@ kwargs = {"num_workers": 4, "pin_memory": True} if args.cuda else {} -train_dataset = datasets.CIFAR10( - os.path.join(cwd_folder_loc, "..", "data", "data-%d" % bf.rank()), - train=True, - download=True, - transform=transforms.Compose( - [ - transforms.ToTensor(), - transforms.Normalize(mean=[0.485, 0.456, 0.406], - std=[0.229, 0.224, 0.225]), - ] - ), -) +if args.dataset == "cifar10": + train_dataset = datasets.CIFAR10( + os.path.join(cwd_folder_loc, "..", "data", "data-%d" % bf.rank()), + train=True, + download=True, + transform=transforms.Compose( + [ + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]), + ] + ), + ) +elif args.dataset == "imagenet": + train_dataset = datasets.ImageFolder(args.train_dir, + transform=transforms.Compose([ + transforms.RandomResizedCrop(224), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]) + ])) +else: + raise ValueError("Args dataset should be either cifar10 or imagenet") # Bluefog: use DistributedSampler to partition data among workers. Manually specify # `num_replicas=bf.size()` and `rank=bf.rank()`. @@ -157,29 +171,40 @@ train_dataset, batch_size=allreduce_batch_size, sampler=train_sampler, **kwargs ) -val_dataset = datasets.CIFAR10( - os.path.join(cwd_folder_loc, "..", "data", "data-%d" % bf.rank()), - train=False, - transform=transforms.Compose( - [ - transforms.ToTensor(), - transforms.Normalize(mean=[0.485, 0.456, 0.406], - std=[0.229, 0.224, 0.225]), - ] - ), -) -if args.average_test_result: - val_sampler = torch.utils.data.distributed.DistributedSampler( - val_dataset, num_replicas=bf.size(), rank=bf.rank() +if args.dataset == "cifar10": + val_dataset = datasets.CIFAR10( + os.path.join(cwd_folder_loc, "..", "data", "data-%d" % bf.rank()), + train=False, + transform=transforms.Compose( + [ + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]), + ] + ), ) -else: - val_sampler = None +elif args.dataset == "imagenet": + val_dataset = datasets.ImageFolder(args.val_dir, + transform=transforms.Compose([ + transforms.Resize(256), + transforms.CenterCrop(224), + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]) + ])) + +val_sampler = torch.utils.data.distributed.DistributedSampler( + val_dataset, num_replicas=bf.size(), rank=bf.rank() +) val_loader = torch.utils.data.DataLoader( val_dataset, batch_size=args.val_batch_size, sampler=val_sampler, **kwargs ) -model = getattr(models, args.model)(num_classes=10) +if args.dataset == "cifar10": + model = getattr(models, args.model)(num_classes=10) +elif args.dataset == "imagenet": + model = getattr(models, args.model)(num_classes=1000) if args.cuda: # Move model to GPU. @@ -373,8 +398,7 @@ def save_checkpoint(epoch): dirpath = os.path.dirname(filepath) if not os.path.exists(dirpath): os.makedirs(dirpath) - state = {"model": model.state_dict( - ), "optimizer": optimizer.state_dict()} + state = {"model": model.state_dict(), "optimizer": optimizer.state_dict()} torch.save(state, filepath) @@ -386,10 +410,7 @@ def __init__(self, name): self.n = torch.tensor(0.0) # pylint: disable=not-callable def update(self, val): - if args.average_test_result: - self.sum += bf.allreduce(val.detach().cpu(), name=self.name) - else: - self.sum += val.detach().cpu() + self.sum += bf.allreduce(val.detach().cpu(), name=self.name) self.n += 1 @property diff --git a/test/torch_ops_test.py b/test/torch_ops_test.py index 49931cc8..fd4c45d9 100644 --- a/test/torch_ops_test.py +++ b/test/torch_ops_test.py @@ -983,6 +983,7 @@ def test_neighbor_allgather(self): assert sorted(candidate_ranks) == gathered_ranks, \ "bf.neighbor_allgather produces incorrect gathered tensor" + @unittest.skip("Need re-design of API.") def test_pair_gossip(self): size = bf.size() rank = bf.rank() @@ -1002,7 +1003,7 @@ def test_pair_gossip(self): for dtype, dim in itertools.product(dtypes, dims): tensor = torch.FloatTensor(*([23] * dim)).fill_(1).mul_(rank) tensor = self.cast_and_place(tensor, dtype) - gossiped_tensor = bf.pair_gossip(tensor, target_rank) + # gossiped_tensor = bf.pair_gossip(tensor, target_rank) tensor, gossiped_tensor = self.convert_cpu_fp16_to_fp32(tensor, gossiped_tensor) assert ( @@ -1014,6 +1015,7 @@ def test_pair_gossip(self): bf.set_skip_negotiate_stage(False) + @unittest.skip("Need re-design of API.") def test_pair_gossip_weighted(self): size = bf.size() rank = bf.rank() @@ -1034,8 +1036,8 @@ def test_pair_gossip_weighted(self): for dtype, dim in itertools.product(dtypes, dims): tensor = torch.FloatTensor(*([23] * dim)).fill_(1).mul_(rank) tensor = self.cast_and_place(tensor, dtype) - gossiped_tensor = bf.pair_gossip( - tensor, target_rank, self_weight=0.25, pair_weight=0.75) + # gossiped_tensor = bf.pair_gossip( + # tensor, target_rank, self_weight=0.25, pair_weight=0.75) tensor, gossiped_tensor = self.convert_cpu_fp16_to_fp32(tensor, gossiped_tensor) assert ( list(gossiped_tensor.shape) == [23] * dim From d771ad0334dc8abd5abb54340faa012c3849cf5b Mon Sep 17 00:00:00 2001 From: ybc Date: Tue, 3 Nov 2020 22:41:13 -0800 Subject: [PATCH 43/44] Update performance and API documents --- bluefog/torch/__init__.py | 2 +- docs/_static/bf_full_benchmark1.png | Bin 0 -> 32930 bytes docs/_static/bf_full_benchmark2.png | Bin 0 -> 46082 bytes docs/conf.py | 3 +- docs/index.rst | 4 +- docs/neighbor_average.rst | 3 ++ docs/performance.rst | 73 +++++++++++++++++++++++++++- docs/rationale.rst | 4 -- docs/torch_api.rst | 35 ++++++++++++- examples/pytorch_resnet.py | 4 +- 10 files changed, 116 insertions(+), 12 deletions(-) create mode 100644 docs/_static/bf_full_benchmark1.png create mode 100644 docs/_static/bf_full_benchmark2.png create mode 100644 docs/neighbor_average.rst delete mode 100644 docs/rationale.rst diff --git a/bluefog/torch/__init__.py b/bluefog/torch/__init__.py index fdf66f9a..ded43234 100644 --- a/bluefog/torch/__init__.py +++ b/bluefog/torch/__init__.py @@ -38,7 +38,6 @@ from bluefog.torch.mpi_ops import mpi_threads_supported from bluefog.torch.mpi_ops import unified_mpi_window_model_supported from bluefog.torch.mpi_ops import nccl_built, is_homogeneous -from bluefog.torch.mpi_ops import set_skip_negotiate_stage, get_skip_negotiate_stage from bluefog.torch.mpi_ops import allreduce, allreduce_nonblocking from bluefog.torch.mpi_ops import allreduce_, allreduce_nonblocking_ @@ -63,6 +62,7 @@ from bluefog.torch.mpi_ops import win_associated_p from bluefog.torch.mpi_ops import turn_on_win_ops_with_associated_p from bluefog.torch.mpi_ops import turn_off_win_ops_with_associated_p +from bluefog.torch.mpi_ops import set_skip_negotiate_stage, get_skip_negotiate_stage from bluefog.torch.mpi_ops import timeline_start_activity, timeline_end_activity from bluefog.torch.mpi_ops import timeline_context diff --git a/docs/_static/bf_full_benchmark1.png b/docs/_static/bf_full_benchmark1.png new file mode 100644 index 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'autodocsumm', ] # Include the example source for plots in API docs diff --git a/docs/index.rst b/docs/index.rst index a377419f..066996f1 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -137,11 +137,11 @@ algorithm, please read our :ref:`Ops Explanation` page. :caption: More Information Bluefog Ops Explanation + Bluefog Performance + Static and Dynamic Neighbor Averaging Lauching Application Through bfrun Bluefog Docker Usage Bluefog Environment Variable - Bluefog Perform Comparison - Bluefog Rationale Bluefog Timeline Spectrum of Machine Learning Algorithm Codebase Structure diff --git a/docs/neighbor_average.rst b/docs/neighbor_average.rst new file mode 100644 index 00000000..003f4fd5 --- /dev/null +++ b/docs/neighbor_average.rst @@ -0,0 +1,3 @@ +Static and Dynamic Neighbor Averaging +===================================== + diff --git a/docs/performance.rst b/docs/performance.rst index fb51d0de..b2a56845 100644 --- a/docs/performance.rst +++ b/docs/performance.rst @@ -1,4 +1,75 @@ Performance =========== -To be added. \ No newline at end of file + +ThroughPut Benchmark +-------------------- + +All of our benchmarks were run on the AWS. We use p3.16xlarge as basic machine. +Each machine has 8 V100 GPUs (64GB memory) with NVLink-enabled and inter-connected communication speed is 25Gbps. +For more details of machine instance, you can find it `here`_. + +To reproduce the benchmarks, run following command [#]_: + +.. code-block:: bash + + $ bfrun -np 16 -H server1:4,server2:4,server3:4,server4:4 \ + python examples/pytorch_benchmark.py --batch-size 64 \ + --dist-optimizer=neighbor_allreduce --virtual-topology InnerOuterExp2 + +At the end of run, you will see the total number of images processed per second like: + +.. code-block:: bash + + Total img/sec on 16 GPU(s): 4310.6 +- 146.9 + +We also provide another distributed optimizer `DistributedHierarchicalNeighborAllreduce`, which is similar +as neighbor_allreduce one but within each machine execute local allreduce first to form a super node. This +optimizer may produce better scaling in terms of loss and accuracy. Read :ref:`Ops Explanation` page to checkout +the details of the underlying communication operation. + +.. code-block:: bash + + $ bfrun -np 16 -H server1:4,server2:4,server3:4,server4:4 \ + python examples/pytorch_benchmark.py --batch-size 64 \ + --dist-optimizer=hierarchical_neighbor_allreduce + +We also provided the option of set dist-optimizer as horovod for easy comparison. Above three dist-optimizers produce following +benchmarks: + +.. image:: _static/bf_full_benchmark1.png + :width: 600 + :align: center + +where N\_AR represents the neighbor allreduce optimizer and H\_N\_AR represents the hierarchical_neighbor_allreduce and the black +box represents the idea of linear scaling. We can see Bluefog can achieve over 95% scaling efficiency while Horovod is around 78% +sacling efficiency under a batch size of 64. + +For more communication intensive case with a batch size of 32, +the scaling efficiency between Bluefog and Horovod becomes even larger. + +.. image:: _static/bf_full_benchmark2.png + :width: 600 + :align: center + +Accuracy and Loss +----------------- + +TO BE ADDED + +Tricks of Boosting Accuracy +--------------------------- + +Just like the tricks decaying of lr over time used in the large batch size training for ImageNet, there are several small tricks of +using Bluefog as well, including + +* Smaller Warmup LR +* Periodical Global Average +* ATC (adapt-then-combine) VS DGD(distributed-gradient-descent) style (Do the neighbor allreduce after VS in parallel with gradient computation) +* Faster learning rate decaying like decaying every 25 epoches instead of 30 epoches for ImageNet + ResNet Training + +TO BE ADDED + + +.. _here: https://aws.amazon.com/ec2/instance-types/p3/ +.. [#] We recommended using aws-ParallelCluster as cluster manager combined with our docker images to run BlueFog on AWS. \ No newline at end of file diff --git a/docs/rationale.rst b/docs/rationale.rst deleted file mode 100644 index fda777f4..00000000 --- a/docs/rationale.rst +++ /dev/null @@ -1,4 +0,0 @@ -Bluefog Rationale -================= - -To be added. \ No newline at end of file diff --git a/docs/torch_api.rst b/docs/torch_api.rst index ac54240a..6468d64b 100644 --- a/docs/torch_api.rst +++ b/docs/torch_api.rst @@ -1,5 +1,38 @@ Torch Module (API Reference) ============================ +All APIs can be roughly categorized into 5 classes: + +* Bluefog Basic Operations: + * init, shutdown, + * size, local_size, rank, local_rank, is_homogeneous + * load_topology, set_topology, in_neighbor_ranks, out_neighbor_ranks +* High-level Optimizer Wrappers: + * DistributedGradientAllreduceOptimizer + * DistributedAllreduceOptimizer + * DistributedNeighborAllreduceOptimizer + * DistributedHierarchicalNeighborAllreduceOptimizer + * DistributedWinPutOptimizer +* Low-level Synchronous Communication Operations: + * allreduce, allreduce_nonblocking, allreduce\_, allreduce_nonblocking\_ + * allgather, allgather_nonblocking + * broadcast, broadcast_nonblocking, broadcast\_, broadcast_nonblocking\_ + * neighbor_allgather, neighbor_allgather_nonblocking + * neighbor_allreduce, neighbor_allreduce_nonblocking + * hierarchical_neighbor_allreduce, hierarchical_neighbor_allreduce_nonblocking + * poll, synchronize, barrier +* Low-level Asynchronous Communication Operations: + * win_create, win_free, win_update, win_update_then_collect + * win_put_nonblocking, win_put + * win_get_nonblocking, win_get + * win_accumulate_nonblocking, win_accumulate + * win_wait, win_poll, win_mutex +* Other miscellaneous and utility functions: + * broadcast_optimizer_state, broadcast_parameters, allreduce_parameters + * timeline_start_activity, timeline_end_activity + * nccl_built, mpi_threads_supported, unified_mpi_window_model_supported + .. automodule:: bluefog.torch - :members: \ No newline at end of file + :members: + :exclude-members: check_extension + :member-order: bysource \ No newline at end of file diff --git a/examples/pytorch_resnet.py b/examples/pytorch_resnet.py index 654608ef..5f998110 100644 --- a/examples/pytorch_resnet.py +++ b/examples/pytorch_resnet.py @@ -107,11 +107,11 @@ elif args.virtual_topology == "ring": bf.set_topology(topology_util.RingGraph(bf.size(), connect_style=1)) elif args.virtual_topology == "InnerOuterRing": - assert bf.is_homogeneous, "InnerOuterRing topo should be used only homogeneous environment" + assert bf.is_homogeneous, "InnerOuterRing should be used under homogeneous environment" bf.set_topology(topology_util.InnerOuterRingGraph( bf.size(), local_size=bf.local_size())) elif args.virtual_topology == "InnerOuterExpo2": - assert bf.is_homogeneous, "InnerOuterExpo2 topo should be used under homogeneous environment" + 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