diff --git a/cmake/external/ascend.cmake b/cmake/external/ascend.cmake index bddd2023b437b..ecc75332d1f15 100644 --- a/cmake/external/ascend.cmake +++ b/cmake/external/ascend.cmake @@ -62,6 +62,7 @@ endif() if(WITH_ASCEND_CL) set(ASCEND_CL_DIR ${ASCEND_DIR}/ascend-toolkit/latest/fwkacllib/lib64) + set(ascend_hccl_lib ${ASCEND_CL_DIR}/libhccl.so) set(ascendcl_lib ${ASCEND_CL_DIR}/libascendcl.so) set(acl_op_compiler_lib ${ASCEND_CL_DIR}/libacl_op_compiler.so) set(ASCEND_CL_INC_DIR ${ASCEND_DIR}/ascend-toolkit/latest/fwkacllib/include) @@ -73,6 +74,9 @@ if(WITH_ASCEND_CL) ADD_LIBRARY(ascendcl SHARED IMPORTED GLOBAL) SET_PROPERTY(TARGET ascendcl PROPERTY IMPORTED_LOCATION ${ascendcl_lib}) + ADD_LIBRARY(ascend_hccl SHARED IMPORTED GLOBAL) + SET_PROPERTY(TARGET ascend_hccl PROPERTY IMPORTED_LOCATION ${ascend_hccl_lib}) + ADD_LIBRARY(acl_op_compiler SHARED IMPORTED GLOBAL) SET_PROPERTY(TARGET acl_op_compiler PROPERTY IMPORTED_LOCATION ${acl_op_compiler_lib}) add_custom_target(extern_ascend_cl DEPENDS ascendcl acl_op_compiler) diff --git a/cmake/external/protobuf.cmake b/cmake/external/protobuf.cmake index 718904c4a63ed..99ac57e3b32cf 100644 --- a/cmake/external/protobuf.cmake +++ b/cmake/external/protobuf.cmake @@ -205,8 +205,11 @@ elseif(WITH_ASCEND_CL AND NOT WITH_ASCEND_CXX11) SET(PROTOBUF_REPOSITORY https://gitee.com/tianjianhe/protobuf.git) SET(PROTOBUF_TAG v3.8.0) else() - SET(PROTOBUF_REPOSITORY ${GIT_URL}/protocolbuffers/protobuf.git) - SET(PROTOBUF_TAG 9f75c5aa851cd877fb0d93ccc31b8567a6706546) + SET(PROTOBUF_REPOSITORY https://gitee.com/tianjianhe/protobuf.git) + SET(PROTOBUF_TAG v3.8.0) + + # SET(PROTOBUF_REPOSITORY ${GIT_URL}/protocolbuffers/protobuf.git) + # SET(PROTOBUF_TAG 9f75c5aa851cd877fb0d93ccc31b8567a6706546) endif() cache_third_party(${TARGET_NAME} diff --git a/cmake/flags.cmake b/cmake/flags.cmake index 4e3dcac5326a4..599acdcb6ee04 100644 --- a/cmake/flags.cmake +++ b/cmake/flags.cmake @@ -151,6 +151,8 @@ set(COMMON_FLAGS -Wno-error=int-in-bool-context # Warning in Eigen gcc 7.2 -Wimplicit-fallthrough=0 # Warning in tinyformat.h -Wno-error=maybe-uninitialized # Warning in boost gcc 7.2 + -Wno-error=nonnull-compare # Warning in boost gcc 7.2 + -Wno-error=address # Warning in boost gcc 7.2 ${fsanitize} ) diff --git a/paddle/fluid/memory/allocation/allocator_facade.cc b/paddle/fluid/memory/allocation/allocator_facade.cc index 100d24c89abdd..20af91fb7ad3b 100644 --- a/paddle/fluid/memory/allocation/allocator_facade.cc +++ b/paddle/fluid/memory/allocation/allocator_facade.cc @@ -79,6 +79,7 @@ class AllocatorFacadePrivate { InitNaiveBestFitCUDAPinnedAllocator(); #endif #ifdef PADDLE_WITH_ASCEND_CL + VLOG(3) << "npu num: " < 0 ? (UNLIKELY(FLAGS_use_system_allocator) ? system_allocators_ : allocators_) : zero_size_allocators_); + VLOG(3) <, + ops::CAllReduceOpASCENDKernel, + ops::CAllReduceOpASCENDKernel, + ops::CAllReduceOpASCENDKernel) diff --git a/paddle/fluid/operators/collective/c_allreduce_min_op_npu.cc b/paddle/fluid/operators/collective/c_allreduce_min_op_npu.cc new file mode 100644 index 0000000000000..f8edace54e769 --- /dev/null +++ b/paddle/fluid/operators/collective/c_allreduce_min_op_npu.cc @@ -0,0 +1,31 @@ +/* Copyright (c) 2019 PaddlePaddle Authors. 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. */ + +#include "paddle/fluid/operators/collective/c_allreduce_op.h" + +namespace paddle { +namespace platform { +struct ASCENDPlace; +struct float16; +} // namespace platform +} // namespace paddle + +namespace ops = paddle::operators; +namespace plat = paddle::platform; + +REGISTER_OP_NPU_KERNEL(c_allreduce_min, + ops::CAllReduceOpASCENDKernel, + ops::CAllReduceOpASCENDKernel, + ops::CAllReduceOpASCENDKernel, + ops::CAllReduceOpASCENDKernel) diff --git a/paddle/fluid/operators/collective/c_allreduce_op.h b/paddle/fluid/operators/collective/c_allreduce_op.h index 24f7f427cf562..6658e5b364a8d 100644 --- a/paddle/fluid/operators/collective/c_allreduce_op.h +++ b/paddle/fluid/operators/collective/c_allreduce_op.h @@ -30,6 +30,11 @@ limitations under the License. */ #include "paddle/fluid/framework/fleet/gloo_wrapper.h" #endif +#if defined(PADDLE_WITH_ASCEND_CL) +#include "paddle/fluid/platform/collective_helper.h" +#include "paddle/fluid/platform/hccl_helper.h" +#endif + namespace paddle { namespace operators { @@ -105,6 +110,88 @@ class CAllReduceOpCPUKernel : public framework::OpKernel { } }; +template +class CAllReduceOpASCENDKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { +#if defined(PADDLE_WITH_ASCEND_CL) + auto in = ctx.Input("X"); + auto out = ctx.Output("Out"); + + auto place = ctx.GetPlace(); + hcclDataType_t dtype = platform::ToHCCLDataType(in->type()); + + int64_t numel = in->numel(); + void* sendbuff = reinterpret_cast(const_cast(in->data())); + // void* sendbuff = reinterpret_cast(const_cast(in->mutable_data(place))); + + out->Resize(in->dims()); + // void* recvbuff = reinterpret_cast(const_cast(out->data())); + void* recvbuff = reinterpret_cast(const_cast(out->mutable_data(place))); + // void* recvbuff = sendbuff; + std::string tag = ctx.Attr("tag"); + int ring_id = ctx.Attr("ring_id"); + // s他的： + std::string group = std::string(HCOM_GROUP_PREFIX) + std::to_string(ring_id); + group = "hccl_world_group";// std::string(HCOM_GROUP_PREFIX) + std::to_string(ring_id); + + auto comm = paddle::platform::HCCLCommContext::Instance().Get(); + + aclrtStream stream = nullptr; + if (ctx.Attr("use_calc_stream")) { + auto dev_ctx = platform::DeviceContextPool::Instance().Get(place); + stream = static_cast(dev_ctx)->stream(); + } else { + stream = comm->stream(); + } + + hcclRedOp_t hccl_red_type = HCCL_REP_OP_SUM; + switch (red_type) { + case kRedSum: + hccl_red_type = HCCL_REP_OP_SUM; + break; + + case kRedMax: + hccl_red_type = HCCL_REP_OP_MAX; + break; + + case kRedMin: + hccl_red_type = HCCL_REP_OP_MIN; + break; + + case kRedProd: + hccl_red_type = HCCL_REP_OP_PROD; + break; + + default: + PADDLE_THROW(platform::errors::InvalidArgument( + "Invalid reduce type: %d", red_type)); + } + + + VLOG(3) << "begin hccl allreduce, parameter is: " + << "input num: " << numel + << "dtype: " << dtype + << "hccl_red_type: " << hccl_red_type + << ", group is: " << group + << ", tag is " << tag; + + printf("sendbuff: %p\n", sendbuff); + printf("recvbuff: %p\n", recvbuff); + + // printf("sendbuff: %p, %d\n", sendbuff, ((int*)sendbuff)[0]); + // printf("recvbuff: %p, %d\n", recvbuff, ((int*)recvbuff)[0]); + + PADDLE_ENFORCE_NPU_SUCCESS(platform::dynload::hcom_all_reduce( + tag.c_str(), sendbuff, recvbuff, numel, dtype, hccl_red_type, group.c_str(), (void*)stream)); + +#else + PADDLE_THROW(platform::errors::PreconditionNotMet( + "PaddlePaddle should compile with GPU.")); +#endif + } +}; + template class CAllReduceOpCUDAKernel : public framework::OpKernel { public: @@ -114,7 +201,7 @@ class CAllReduceOpCUDAKernel : public framework::OpKernel { auto out = ctx.Output("Out"); auto place = ctx.GetPlace(); - ncclDataType_t dtype = platform::ToNCCLDataType(in->type()); + ncclDataType_t dtype = platform::ToHCCLDataType(in->type()); int64_t numel = in->numel(); const void* sendbuff = in->data(); out->Resize(in->dims()); @@ -170,6 +257,11 @@ class CAllReduceOpMaker : public framework::OpProtoAndCheckerMaker { AddOutput("Out", "(Tensor) the allreduced result."); AddAttr("ring_id", "(int default 0) communication ring id.") .SetDefault(0); +#if defined(PADDLE_WITH_ASCEND_CL) + #pragma message("hccl CAllReduceOpMaker need tag attr") + AddAttr("tag", "(string default tag) tag for all reduce.") + .SetDefault("tag"); +#endif AddAttr( "use_calc_stream", "(bool default false) eject CUDA operations to calculation stream.") diff --git a/paddle/fluid/operators/collective/c_allreduce_prod_op_npu.cc b/paddle/fluid/operators/collective/c_allreduce_prod_op_npu.cc new file mode 100644 index 0000000000000..f59723ed40ed6 --- /dev/null +++ b/paddle/fluid/operators/collective/c_allreduce_prod_op_npu.cc @@ -0,0 +1,31 @@ +/* Copyright (c) 2019 PaddlePaddle Authors. 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. */ + +#include "paddle/fluid/operators/collective/c_allreduce_op.h" + +namespace paddle { +namespace platform { +struct ASCENDPlace; +struct float16; +} // namespace platform +} // namespace paddle + +namespace ops = paddle::operators; +namespace plat = paddle::platform; + +REGISTER_OP_NPU_KERNEL(c_allreduce_prod, + ops::CAllReduceOpASCENDKernel, + ops::CAllReduceOpASCENDKernel, + ops::CAllReduceOpASCENDKernel, + ops::CAllReduceOpASCENDKernel) diff --git a/paddle/fluid/operators/collective/c_allreduce_sum_op_npu.cc b/paddle/fluid/operators/collective/c_allreduce_sum_op_npu.cc new file mode 100644 index 0000000000000..6554976b1c895 --- /dev/null +++ b/paddle/fluid/operators/collective/c_allreduce_sum_op_npu.cc @@ -0,0 +1,31 @@ +/* Copyright (c) 2019 PaddlePaddle Authors. 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. */ + +#include "paddle/fluid/operators/collective/c_allreduce_op.h" + +namespace paddle { +namespace platform { +struct ASCENDPlace; +struct float16; +} // namespace platform +} // namespace paddle + +namespace ops = paddle::operators; +namespace plat = paddle::platform; + +REGISTER_OP_NPU_KERNEL(c_allreduce_sum, + ops::CAllReduceOpASCENDKernel, + ops::CAllReduceOpASCENDKernel, + ops::CAllReduceOpASCENDKernel, + ops::CAllReduceOpASCENDKernel) diff --git a/paddle/fluid/operators/collective/c_broadcast_op.cc b/paddle/fluid/operators/collective/c_broadcast_op.cc index 928fa8549ffb9..05b4b13d96ba9 100644 --- a/paddle/fluid/operators/collective/c_broadcast_op.cc +++ b/paddle/fluid/operators/collective/c_broadcast_op.cc @@ -42,6 +42,11 @@ class CBroadcastOpMaker : public framework::OpProtoAndCheckerMaker { .SetDefault(0); AddAttr("root", "(int default 0) root id for broadcasting.") .SetDefault(0); +#if defined(PADDLE_WITH_ASCEND_CL) + #pragma message("tag") + AddAttr("tag", "(string default tag) tag for broadcasting.") + .SetDefault("tag"); +#endif AddAttr( "use_calc_stream", "(bool default false) eject CUDA operations to calculation stream.") diff --git a/paddle/fluid/operators/collective/c_broadcast_op_npu.cc b/paddle/fluid/operators/collective/c_broadcast_op_npu.cc new file mode 100644 index 0000000000000..690e143359540 --- /dev/null +++ b/paddle/fluid/operators/collective/c_broadcast_op_npu.cc @@ -0,0 +1,94 @@ +/* Copyright (c) 2019 PaddlePaddle Authors. 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. */ + +#include "paddle/fluid/operators/collective/c_broadcast_op.h" + +#if defined(PADDLE_WITH_ASCEND_CL) +#include "paddle/fluid/platform/collective_helper.h" +#include "paddle/fluid/platform/hccl_helper.h" +#endif + +namespace paddle { +namespace operators { + +template +class CBroadcastOpASCENDKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { +#if defined(PADDLE_WITH_ASCEND_CL) + auto x = ctx.Input("X"); + void *ptr = reinterpret_cast(const_cast(x->data())); + int numel = x->numel(); + hcclDataType_t dtype = platform::ToHCCLDataType(x->type()); + + auto out = ctx.Output("Out"); + + auto place = ctx.GetPlace(); + auto comm = paddle::platform::HCCLCommContext::Instance().Get(); + + aclrtStream stream = nullptr; + if (ctx.Attr("use_calc_stream")) { + auto dev_ctx = platform::DeviceContextPool::Instance().Get(place); + stream = static_cast(dev_ctx)->stream(); + } else { + stream = comm->stream(); + } + + int root = ctx.Attr("root"); + int ring_id = ctx.Attr("ring_id"); + std::string group = std::string(HCOM_GROUP_PREFIX) + std::to_string(ring_id); + std::string tag = ctx.Attr("tag"); + + VLOG(3) << "begin hccl broadcast, parameter is: "<< "root " << root + << ", group is " << group + << ", tag is " << tag; + + if (root == static_cast(comm->rank())) { + PADDLE_ENFORCE_NPU_SUCCESS(platform::dynload::hcom_broadcast(tag.c_str(), ptr, numel, + dtype, (uint32_t)root, group.c_str(), (void*)stream)); + VLOG(3) << "rank " << comm->rank() << " invoke Bcast. sent " + << x->numel(); + } else { + PADDLE_ENFORCE_NPU_SUCCESS(platform::dynload::hcom_broadcast(tag.c_str(), ptr, numel, + dtype, (uint32_t)root, group.c_str(), (void*)stream)); + VLOG(3) << "rank " << comm->rank() << " invoke Bcast. recieved " + << framework::product(out->dims()); + } + if (out != x) { + framework::TensorCopy( + *static_cast(x), place, + *platform::DeviceContextPool::Instance().Get(place), + static_cast(out)); + } + + out->Resize(x->dims()); + out->set_lod(x->lod()); +#else + PADDLE_THROW(platform::errors::PreconditionNotMet( + "PaddlePaddle should compile with GPU.")); +#endif + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +namespace plat = paddle::platform; + +REGISTER_OP_NPU_KERNEL(c_broadcast, + ops::CBroadcastOpASCENDKernel, + ops::CBroadcastOpASCENDKernel, + ops::CBroadcastOpASCENDKernel, + ops::CBroadcastOpASCENDKernel); diff --git a/paddle/fluid/operators/collective/c_comm_init_hccl_op.cc b/paddle/fluid/operators/collective/c_comm_init_hccl_op.cc new file mode 100644 index 0000000000000..e47849b83944f --- /dev/null +++ b/paddle/fluid/operators/collective/c_comm_init_hccl_op.cc @@ -0,0 +1,79 @@ +/* Copyright (c) 2021 PaddlePaddle Authors. 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. */ + +#if defined(PADDLE_WITH_ASCEND_CL) +#include "paddle/fluid/platform/collective_helper.h" +#include "paddle/fluid/platform/hccl_helper.h" + +#include + +#include "paddle/fluid/framework/op_registry.h" +#include "paddle/fluid/operators/npu_op_runner.h" + +namespace paddle { +namespace framework { +class Scope; +} // namespace framework +} // namespace paddle + +namespace paddle { +namespace operators { + +class CCommInitOpNPU : public framework::OperatorBase { + public: + CCommInitOpNPU(const std::string& type, + const framework::VariableNameMap& inputs, + const framework::VariableNameMap& outputs, + const framework::AttributeMap& attrs) + : OperatorBase(type, inputs, outputs, attrs) {} + + void RunImpl(const framework::Scope& scope, + const platform::Place& place) const override { + std::string rank_table_file = Attr("rank_table_file"); + uint32_t rank_id = Attr("rank_id"); + uint32_t device_id = Attr("device_id"); + + VLOG(3) << "begin init hccl, parameter is: " + << "rank_table_file " << rank_table_file + << " rank_id " << rank_id + << " device_id " << device_id; + + platform::HCCLCommContext::Instance().CreateHCCLComm(rank_table_file, rank_id, device_id); + } +}; + +class CCommInitOpNPUMaker : public framework::OpProtoAndCheckerMaker { + public: + void Make() override { + AddComment(R"DOC( +CCommInit operator on NPU + +Initialize collective communication context within this trainer +)DOC"); + AddAttr("rank_table_file", + "(string) path to rank_table_file"); + AddAttr("rank_id", "(int) world rank id of the process"); + AddAttr("device_id", "(int) device id of the process/thread"); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; + +REGISTER_OPERATOR(c_comm_init_hccl, ops::CCommInitOpNPU, + ops::CCommInitOpNPUMaker); + +#endif diff --git a/paddle/fluid/operators/collective/c_create_group_op.cc b/paddle/fluid/operators/collective/c_create_group_op.cc new file mode 100644 index 0000000000000..07ca91e72ae47 --- /dev/null +++ b/paddle/fluid/operators/collective/c_create_group_op.cc @@ -0,0 +1,76 @@ +/* Copyright (c) 2021 PaddlePaddle Authors. 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. */ + +#ifdef PADDLE_WITH_ASCEND_CL +#include "paddle/fluid/platform/collective_helper.h" +#include "paddle/fluid/platform/hccl_helper.h" + +#include + +#include "paddle/fluid/framework/op_registry.h" +#include "paddle/fluid/operators/npu_op_runner.h" + +namespace paddle { +namespace framework { +class Scope; +} // namespace framework +} // namespace paddle + +namespace paddle { +namespace operators { + +class CCreateGroupOpNPU : public framework::OperatorBase { + public: + CCreateGroupOpNPU(const std::string& type, + const framework::VariableNameMap& inputs, + const framework::VariableNameMap& outputs, + const framework::AttributeMap& attrs) + : OperatorBase(type, inputs, outputs, attrs) {} + + void RunImpl(const framework::Scope& scope, + const platform::Place& place) const override { + std::string group_name = Attr("group_name"); + int nranks = Attr("nranks"); + std::vector rank_ids = Attr>("rank_ids"); + paddle::platform::HCCLCommContext::Instance().CreateHCCLGroup( + group_name, (uint32_t)nranks, + std::vector(rank_ids.begin(), rank_ids.end())); + } +}; + +class CCreateGroupOpNPUMaker : public framework::OpProtoAndCheckerMaker { + public: + void Make() override { + AddComment(R"DOC( +CCreateGroup operator on NPU + +Create collective communication group on NPU +)DOC"); + AddAttr("group_name", + "(string) name of the collective communication group"); + AddAttr("nranks", "(int) number of the group"); + AddAttr>("rank_ids", + "(list of int) The world rank id of the group members"); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; + +REGISTER_OPERATOR(c_create_group, ops::CCreateGroupOpNPU, + ops::CCreateGroupOpNPUMaker); + +#endif diff --git a/paddle/fluid/operators/collective/c_hcom_op_npu_test.cc b/paddle/fluid/operators/collective/c_hcom_op_npu_test.cc new file mode 100644 index 0000000000000..590cfe62148dc --- /dev/null +++ b/paddle/fluid/operators/collective/c_hcom_op_npu_test.cc @@ -0,0 +1,192 @@ +/* Copyright (c) 2021 PaddlePaddle Authors. 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. */ + +#ifndef _WIN32 +#include +#endif + +#include + +#include +#include // NOLINT +#include + +#include "gtest/gtest.h" +#include "paddle/fluid/framework/op_registry.h" +#include "paddle/fluid/framework/operator.h" +#include "paddle/fluid/framework/program_desc.h" +#include "paddle/fluid/operators/dropout_op.h" +#include "paddle/fluid/operators/math/math_function.h" +#include "paddle/fluid/string/printf.h" +#include "paddle/fluid/operators/collective/c_broadcast_op.h" +#include "paddle/fluid/operators/collective/c_allreduce_op.h" + +#if defined(PADDLE_WITH_ASCEND_CL) +#include "paddle/fluid/platform/collective_helper.h" +#include "paddle/fluid/platform/hccl_helper.h" +#endif + +namespace f = paddle::framework; +namespace p = paddle::platform; +namespace m = paddle::operators::math; + +USE_OP(c_broadcast); +USE_OP(c_allreduce_sum); +USE_NO_KERNEL_OP(c_comm_init_hccl); +USE_NO_KERNEL_OP(c_create_group); +USE_OP_DEVICE_KERNEL(c_broadcast, NPU); +USE_OP_DEVICE_KERNEL(c_allreduce_sum, NPU); + +void Prepare(f::Scope* scope, const p::DeviceContext& ctx){ + + std::string rank_table_file = getenv("RANK_TABLE_FILE"); + int rank_id = atoi(getenv("RANK_ID")); + int device_id = atoi(getenv("DEVICE_ID")); + + printf("rank_table_file: %s, rank_id = %d, device_id = %d\n", rank_table_file.c_str(), rank_id, device_id); + + f::AttributeMap attrs; + attrs["rank_table_file"] = rank_table_file; + attrs["rank_id"] = rank_id; + attrs["device_id"] = device_id; + auto comm_init_op = + f::OpRegistry::CreateOp("c_comm_init_hccl", {}, {}, attrs); + auto place = ctx.GetPlace(); + comm_init_op->Run(*scope, place); + ctx.Wait(); + + f::AttributeMap create_attrs; + create_attrs["group_name"] = HCOM_GROUP_PREFIX + std::to_string(0); + create_attrs["nranks"] = 2; + std::vector rank_ids{0, 1}; + create_attrs["rank_ids"] = rank_ids; + auto create_group_op = f::OpRegistry::CreateOp("c_create_group", {}, {}, create_attrs); + create_group_op->Run(*scope, place); + ctx.Wait(); +} +void TestHCCLBroadcastOp(f::Scope* scope, const p::DeviceContext& ctx) { + std::cout<< "BEGIN TEST:" << __FUNCTION__ <Var("X"); + auto tensor_x = x->GetMutable(); + int num = 2; + std::vector init; + int rank_id = atoi(getenv("RANK_ID")); + std::cout<< "rank_id:" << rank_id<Resize({num, num}); + + ctx.Wait(); + + auto place = ctx.GetPlace(); + auto out = scope->Var("Out"); + auto tensor_out = out->GetMutable(); + tensor_out->Resize({num, num}); + tensor_out->mutable_data(place); // allocate + + ctx.Wait(); + + // run + f::AttributeMap attrs; + attrs["tag"]=std::string("tagx"); + attrs["root"]=0; + attrs["ring_id"]=0; + + auto op = + f::OpRegistry::CreateOp("c_broadcast", {{"X", {"X"}}}, + {{"Out", {"Out"}}}, attrs); + + op->Run(*scope, place); + + std::vector out_vec; + TensorToVector(*tensor_out, ctx, &out_vec); + + ctx.Wait(); + + EXPECT_EQ(out_vec.size(), init.size()); + for (uint32_t i = 0; i < out_vec.size(); i++) { + EXPECT_EQ(out_vec[i], 1.0); + } +} + +void TestHCCLAllReduceOp(f::Scope* scope, const p::DeviceContext& ctx) { + std::cout<< "BEGIN TEST:" << __FUNCTION__ <Var("X"); + auto tensor_x = x->GetMutable(); + + std::vector init; + int rank_id = atoi(getenv("RANK_ID")); + std::cout<< "rank_id:" << rank_id<Resize({num1, num2}); + + ctx.Wait(); + + auto place = ctx.GetPlace(); + auto out = scope->Var("Out"); + auto tensor_out = out->GetMutable(); + tensor_out->Resize({num1, num2}); + tensor_out->mutable_data(place); // allocate + + ctx.Wait(); + + // run + f::AttributeMap attrs; + attrs["tag"]=std::string("tagx"); + attrs["ring_id"]=0; + + auto op = + f::OpRegistry::CreateOp("c_allreduce_sum", {{"X", {"X"}}}, + {{"Out", {"Out"}}}, attrs); + + op->Run(*scope, place); + + std::vector out_vec; + TensorToVector(*tensor_out, ctx, &out_vec); + + ctx.Wait(); + + EXPECT_EQ(out_vec.size(), init.size()); + for (uint32_t i = 0; i < out_vec.size(); i++) { + EXPECT_EQ(out_vec[i], 2.0); + } +} +TEST(c_broadcast, NPU) { + f::Scope scope; + char * npu_id=getenv("FLAGS_selected_npus"); + + p::NPUDeviceContext ctx(p::NPUPlace(atoi(npu_id))); + + Prepare(&scope, ctx); + // TestHCCLBroadcastOp(&scope, ctx); + TestHCCLAllReduceOp(&scope, ctx); +} diff --git a/paddle/fluid/platform/CMakeLists.txt b/paddle/fluid/platform/CMakeLists.txt index 7e0675bdef58f..54226135101d9 100644 --- a/paddle/fluid/platform/CMakeLists.txt +++ b/paddle/fluid/platform/CMakeLists.txt @@ -128,7 +128,7 @@ cc_library(device_context SRCS device_context.cc init.cc DEPS simple_threadpool place eigen3 stringpiece cpu_helper cpu_info framework_proto ${GPU_CTX_DEPS} ${NPU_CTX_DEPS} ${MKLDNN_CTX_DEPS} ${dgc_deps} dlpack cudnn_workspace_helper ${XPU_CTX_DEPS}) -cc_library(collective_helper SRCS collective_helper.cc DEPS framework_proto device_context enforce) +cc_library(collective_helper SRCS collective_helper.cc collective_helper_npu.cc DEPS framework_proto device_context enforce) if(WITH_GPU) cc_library(cuda_resource_pool SRCS cuda_resource_pool.cc DEPS gpu_info) diff --git a/paddle/fluid/platform/collective_helper.h b/paddle/fluid/platform/collective_helper.h index d44199f309b63..084172d4eb700 100644 --- a/paddle/fluid/platform/collective_helper.h +++ b/paddle/fluid/platform/collective_helper.h @@ -14,20 +14,21 @@ #pragma once -#if defined(PADDLE_WITH_NCCL) #include #include #include #include #include "boost/variant.hpp" +#include "paddle/fluid/platform/enforce.h" #include "paddle/fluid/framework/data_type.h" +#include "paddle/fluid/platform/dynload/hccl.h" #include "paddle/fluid/platform/device_context.h" -#include "paddle/fluid/platform/enforce.h" namespace paddle { namespace platform { +#if defined(PADDLE_WITH_NCCL) // In order to apply hierarchical communication with NCCL, we need // a communication ring contains NCCL communicators associated to a global // ncclUniqueId. E.g. for a hierarchical case, @@ -47,6 +48,8 @@ namespace platform { // // The NCCLComm instance is created and reversed in the NCCLCommContext // singleton with a global user specified group id. +class CUDADeviceContext; + class NCCLComm { public: virtual int ring_id() const = 0; @@ -120,8 +123,162 @@ class NCCLCommContext { NCCLCommContext() = default; DISABLE_COPY_AND_ASSIGN(NCCLCommContext); }; +#endif -} // namespace platform -} // namespace paddle +#if defined(PADDLE_WITH_ASCEND_CL) +// In order to apply hierarchical communication with HCCL, we need +// a communication ring contains HCCL communicators associated to a global +// HCCLUniqueId. E.g. for a hierarchical case, +// +// 11 - 12 21 - 22 +// | | | | +// 13 - 14 - 23 - 24 +// | | +// 31 - 32 - 41 - 42 +// | | | | +// 33 - 34 43 - 44 +// +// we group (14,23,32,41) as the top, and (11,12,13,14), (21,22,23,24), +// (31,32,33,34), (41,42,43,44) as bottoms respectively. +// +// We could also use a single communication ring for the flatten case +// +// The HCCLComm instance is created and reversed in the HCCLCommContext +// singleton with a global user specified group id. +class NPUDeviceContext; + +class HCCLComm { + public: + virtual std::string rank_table_file() const = 0; + virtual uint32_t rank() const = 0; + virtual uint32_t device_id() const = 0; + virtual aclrtStream stream() const = 0; + virtual NPUDeviceContext* dev_context() const = 0; + virtual ~HCCLComm() = default; +}; + +// A singleton HCCL communicator context reserves communication ring ids +class HCCLCommContext { + public: + static HCCLCommContext& Instance() { + static HCCLCommContext comm_ctx; + return comm_ctx; + } + + HCCLComm* CreateHCCLComm(const std::string& config_file, uint32_t rank, uint32_t device_id); + + void CreateHCCLGroup(const std::string& group_name, uint32_t nranks, const std::vector& rank_ids); + + // retrieve a communicator by the ring id and place + HCCLComm* Get() const { + return comm_.get(); + } + private: + std::once_flag once_flag_; + std::mutex comm_map_mutex_; + std::unique_ptr comm_; + + HCCLComm* AssignHCCLComm(const std::string& config_file, uint32_t rank, uint32_t device_id); + + HCCLCommContext() = default; + DISABLE_COPY_AND_ASSIGN(HCCLCommContext); +}; +#endif + +#if defined(PADDLE_WITH_XPU_BKCL) +// In order to apply hierarchical communication with BKCL, we need +// a communication ring contains BKCL communicators associated to a global +// BKCLUniqueId. E.g. for a hierarchical case, +// +// 11 - 12 21 - 22 +// | | | | +// 13 - 14 - 23 - 24 +// | | +// 31 - 32 - 41 - 42 +// | | | | +// 33 - 34 43 - 44 +// +// we group (14,23,32,41) as the top, and (11,12,13,14), (21,22,23,24), +// (31,32,33,34), (41,42,43,44) as bottoms respectively. +// +// We could also use a single communication ring for the flatten case +// +// The BKCLComm instance is created and reversed in the BKCLCommContext +// singleton with a global user specified group id. +class BKCLComm { + public: + virtual int ring_id() const = 0; + virtual int nranks() const = 0; + virtual int rank() const = 0; + virtual int device_id() const = 0; + virtual BKCLContext_t comm() const = 0; + virtual XPUStream stream() const = 0; + virtual XPUDeviceContext* dev_context() const = 0; + virtual ~BKCLComm() = default; +}; + +// A singleton BKCL communicator context reserves communication ring ids +class BKCLCommContext { + public: + static BKCLCommContext& Instance() { + static BKCLCommContext comm_ctx; + return comm_ctx; + } + + BKCLComm* CreateBKCLComm(BKCLUniqueId* bkcl_id, int nranks, int rank, + int dev_id, int ring_id = 0); + + void CreateAllBKCLComms(const std::vector& dev_ids, int ring_id = 0); + + // a latter comm with the same dev_id and the same ring_id + // will override the former + BKCLComm* AssignBKCLComm(BKCLContext_t comm, int nranks, int rank, int dev_id, + int ring_id = 0); + + // retrieve a communicator by the ring id in multiprocessing mode + BKCLComm* Get(int ring_id) const { + PADDLE_ENFORCE_GT( + comm_map_.count(ring_id), 0, + platform::errors::InvalidArgument( + "Communicator in ring id %d has not been initialized.", ring_id)); + PADDLE_ENFORCE_EQ(comm_map_.at(ring_id).size(), 1, + platform::errors::InvalidArgument( + "One device id should be specified to retrieve from " + "multiple communicators.")); + return comm_map_.at(ring_id).begin()->second.get(); + } + + // retrieve a communicator by the ring id and the device id + BKCLComm* Get(int ring_id, int dev_id) const { + PADDLE_ENFORCE_GT( + comm_map_.count(ring_id), 0, + platform::errors::InvalidArgument( + "Communicator of ring id %d has not been initialized.", ring_id)); + PADDLE_ENFORCE_GT( + comm_map_.at(ring_id).count(dev_id), 0, + platform::errors::InvalidArgument( + "Communicator at device id %d has not been initialized in ring %d.", + dev_id, ring_id)); + return comm_map_.at(ring_id).at(dev_id).get(); + } + + // retrieve a communicator by the ring id and place + BKCLComm* Get(int ring_id, Place place) const { + return Get(ring_id, BOOST_GET_CONST(XPUPlace, place).device); + } + + private: + std::once_flag once_flag_; + std::mutex comm_map_mutex_; + // ring id to dev-BKCLComm + std::map>> comm_map_; + void ReleaseBKCLComms(); + + BKCLCommContext() = default; + DISABLE_COPY_AND_ASSIGN(BKCLCommContext); +}; #endif + +} // namespace platform +} // namespace paddle diff --git a/paddle/fluid/platform/collective_helper_npu.cc b/paddle/fluid/platform/collective_helper_npu.cc new file mode 100644 index 0000000000000..3cf16475ee047 --- /dev/null +++ b/paddle/fluid/platform/collective_helper_npu.cc @@ -0,0 +1,111 @@ +// Copyright (c) 2019 PaddlePaddle Authors. 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. + +#if defined(PADDLE_WITH_ASCEND_CL) +#include "paddle/fluid/platform/collective_helper.h" +#include + +namespace paddle { +namespace platform { + +class HCCLCommImpl : public HCCLComm { + public: + void set_rank_table_file(const std::string& rank_table_file) { rank_table_file_ = rank_table_file; } + std::string rank_table_file() const override { return rank_table_file_; } + + void set_rank(uint32_t rank) { rank_ = rank; } + uint32_t rank() const override { return rank_; } + + void set_device_id(uint32_t device_id) { device_id_ = device_id; } + uint32_t device_id() const override { return device_id_; } + + aclrtStream stream() const override { return dev_ctx_->stream(); } + + void set_dev_ctx(std::unique_ptr&& dev_ctx) { + dev_ctx_ = std::move(dev_ctx); + } + NPUDeviceContext* dev_context() const override { return dev_ctx_.get(); } + + private: + std::string rank_table_file_; + uint32_t rank_; + uint32_t device_id_; + std::unique_ptr dev_ctx_; +}; + +HCCLComm* HCCLCommContext::CreateHCCLComm(const std::string& rank_table_file, + uint32_t rank, uint32_t device_id) { +/* + PADDLE_ENFORCE_NOT_NULL(rank_table_file, + platform::errors::InvalidArgument( + "The rank table file should not be null.")); + + PADDLE_ENFORCE_GE(rank, 0, + platform::errors::InvalidArgument( + "Expected rank >= 0. But received rank is %d.", rank)); + + PADDLE_ENFORCE_GE(device_id, 0, + platform::errors::InvalidArgument( + "Expected dev_id >= 0. But received dev_id is %d.", device_id)); +*/ + auto* comm_wrapper = AssignHCCLComm(rank_table_file, rank, device_id); + + platform::dynload::hcom_init(rank_table_file.c_str(), std::to_string(rank).c_str()); + platform::dynload::hcom_bind_model(comm_wrapper->stream(), comm_wrapper->stream()); + + VLOG(1) << "hccl communicator of rank " << rank << " has been created"; + return comm_wrapper; +} + +HCCLComm* HCCLCommContext::AssignHCCLComm(const std::string& rank_table_file, + uint32_t rank, uint32_t device_id) { + + std::unique_ptr dev_ctx( + new NPUDeviceContext(NPUPlace(device_id))); + + VLOG(3) << "device_id" << device_id; + VLOG(3) << "dev_ctx->stream()" << dev_ctx->stream(); + + HCCLCommImpl* c = new HCCLCommImpl; + c->set_rank_table_file(rank_table_file); + c->set_rank(rank); + c->set_device_id(device_id); + c->set_dev_ctx(std::move(dev_ctx)); + // comm_ = c + comm_.reset(c); + return c; +} + +void HCCLCommContext::CreateHCCLGroup(const std::string& group_name, uint32_t nranks, + const std::vector& rank_ids) { +/* + PADDLE_ENFORCE_NOT_NULL(group_name, + platform::errors::InvalidArgument( + "The group name should not be null.")); + PADDLE_ENFORCE_GT(nranks, 0, + platform::errors::InvalidArgument( + "Expected nranks > 0. But received nranks is %d.", nranks)); + PADDLE_ENFORCE_NOT_NULL(rank_ids, + platform::errors::InvalidArgument( + "The rank ids should not be null.")); +*/ + platform::dynload::hcom_create_group(group_name.c_str(), nranks, (unsigned int*)rank_ids.data()); + + VLOG(1) << "hccl group with name " << group_name << " has been created"; +} + +} // namespace platform +} // namespace paddle + +#endif diff --git a/paddle/fluid/platform/dynload/CMakeLists.txt b/paddle/fluid/platform/dynload/CMakeLists.txt index 647bff93122b1..b08ce39714467 100644 --- a/paddle/fluid/platform/dynload/CMakeLists.txt +++ b/paddle/fluid/platform/dynload/CMakeLists.txt @@ -9,7 +9,7 @@ endif() # There is no macOS version of NCCL. # Disable nvrtc and cuda_driver api on MacOS and Windows, and only do a early test on Linux. if (NOT APPLE AND NOT WIN32) - list(APPEND CUDA_SRCS nvrtc.cc cuda_driver.cc) + list(APPEND CUDA_SRCS nvrtc.cc cuda_driver.cc) if (WITH_NCCL) list(APPEND CUDA_SRCS nccl.cc) endif() @@ -32,6 +32,8 @@ endif(CUPTI_FOUND) if(WITH_ROCM_PLATFORM) hip_library(dynload_cuda SRCS ${HIP_SRCS} DEPS dynamic_loader) hip_library(dynload_warpctc SRCS warpctc.cc DEPS dynamic_loader warpctc) +elseif (WITH_ASCEND_CL) + cc_library(dynload_warpctc SRCS warpctc.cc hccl.cc DEPS dynamic_loader warpctc) else() nv_library(dynload_cuda SRCS ${CUDA_SRCS} DEPS dynamic_loader) cc_library(dynload_warpctc SRCS warpctc.cc DEPS dynamic_loader warpctc) diff --git a/paddle/fluid/platform/dynload/base.h b/paddle/fluid/platform/dynload/base.h new file mode 100644 index 0000000000000..389ad3a94ede0 --- /dev/null +++ b/paddle/fluid/platform/dynload/base.h @@ -0,0 +1,127 @@ +/** + * Copyright 2019-2020 Huawei Technologies Co., Ltd + * + * 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. + */ + +/** + * @file base.h + * @brief HCOM data type definition + * + */ + +#ifndef HCCL_BASE_H_ +#define HCCL_BASE_H_ + +#define HCOM_GROUP_PREFIX "HCOM_GROUP_" + +#ifdef __cplusplus +extern "C" { +#endif // __cplusplus + +typedef signed char s8; +typedef signed short s16; +typedef signed int s32; +typedef signed long long s64; +typedef unsigned char u8; +typedef unsigned short u16; +typedef unsigned int u32; +typedef unsigned long long u64; + +/** + * @brief HCOM functions return value definition + */ +typedef enum tagHcclResult { + HCCL_SUCCESS = 0, /**< success */ + HCCL_E_PARA = 1, /**< parameter error */ + HCCL_E_PTR = 2, /**< empty pointer */ + HCCL_E_MEMORY = 3, /**< memory error */ + HCCL_E_INTERNAL = 4, /**< internal error */ + HCCL_E_NOT_SUPPORT = 5, /**< not support feature */ + HCCL_E_NOT_FOUND = 6, /**< not found specific resource */ + HCCL_E_UNAVAIL = 7, /**< resource unavailable */ + HCCL_E_SYSCALL = 8, /**< call system interface error */ + HCCL_E_TIMEOUT = 9, /**< timeout */ + HCCL_E_OPEN_FILE_FAILURE = 10, /**< open file fail */ + HCCL_E_TCP_CONNECT = 11, /**< tcp connect fail */ + HCCL_E_ROCE_CONNECT = 12, /**< roce connect fail */ + HCCL_E_TCP_TRANSFER = 13, /**< tcp transfer fail */ + HCCL_E_ROCE_TRANSFER = 14, /**< roce transfer fail */ + HCCL_E_RUNTIME = 15, /**< call runtime api fail */ + HCCL_E_DRV = 16, /**< call driver api fail */ + HCCL_E_PROFILING = 17, /**< call profiling api fail */ + HCCL_E_CCE = 18, /**< call cce api fail */ + HCCL_E_NETWORK = 19, /**< call network api fail */ + HCCL_E_RESERVED /**< reserved */ +} hcclResult_t; + +/* handle to communicator */ +typedef void *hcclComm_t; + +/** + * @brief HCCL Reduction opperation + */ +typedef enum tagHcclRedOp { + HCCL_REP_OP_SUM = 0, /**< sum */ + HCCL_REP_OP_PROD = 1, /**< prod */ + HCCL_REP_OP_MAX = 2, /**< max */ + HCCL_REP_OP_MIN = 3, /**< min */ + HCCL_REP_OP_RESERVED /**< reserved */ +} hcclRedOp_t; + +/** + * @brief HCCL data type + */ +typedef enum tagHcclDataType { + HCCL_DATA_TYPE_INT8 = 0, /**< int8 */ + HCCL_DATA_TYPE_INT16 = 1, /**< int16 */ + HCCL_DATA_TYPE_INT32 = 2, /**< int32 */ + HCCL_DATA_TYPE_FP16 = 3, /**< fp16 */ + HCCL_DATA_TYPE_FP32 = 4, /**< fp32 */ + HCCL_DATA_TYPE_INT64 = 5, /**< fp32 */ + HCCL_DATA_TYPE_UINT64 = 6, /**< fp32 */ + HCCL_DATA_TYPE_RESERVED /**< reserved */ +} hcclDataType_t; + +const u32 HCCL_MAX_SEGMENT_NUM = 8; // The max number of gradient segments. + +/** + * @brief the feature of the model + */ +struct model_feature { + const char *model_name; /**< The model name */ + u32 gradient_num; /**< The number of gradients */ + float *gradient_size; /**< The size of each gradient */ + float *gradient_time; /**< The BP compution time of each gradient */ +}; + +enum GradSplitForceMode { + FORCE_NONE, /**< no force */ + FORCE_SIZE, /**< force split gradient by size */ + FORCE_RESERVED /**< reserved */ +}; + +/** +* @brief stream handle. +*/ +typedef void *rtStream_t; + +/** +* @brief model handle. +*/ +typedef void *rtModel_t; + +#ifdef __cplusplus +} +#endif // __cplusplus +#endif // HCCL_BASE_H_ diff --git a/paddle/fluid/platform/dynload/dynamic_loader.cc b/paddle/fluid/platform/dynload/dynamic_loader.cc index 4c39a35030b3f..6aa5826449700 100644 --- a/paddle/fluid/platform/dynload/dynamic_loader.cc +++ b/paddle/fluid/platform/dynload/dynamic_loader.cc @@ -21,6 +21,10 @@ limitations under the License. */ #include "paddle/fluid/platform/dynload/cupti_lib_path.h" #include "paddle/fluid/platform/enforce.h" + + + + DEFINE_string(cudnn_dir, "", "Specify path for loading libcudnn.so. For instance, " "/usr/local/cudnn/lib. If empty [default], dlopen " @@ -36,6 +40,11 @@ DEFINE_string(nccl_dir, "", "For instance, /usr/local/cuda/lib64. If default, " "dlopen will search cuda from LD_LIBRARY_PATH"); +DEFINE_string(hccl_dir, "", + "Specify path for loading hccl library, such as libhccl.so. " + "For instance, /usr/local/Ascend/ascend-toolkit/latest/fwkacllib/lib64/. If default, " + "dlopen will search hccl from LD_LIBRARY_PATH"); + DEFINE_string(cupti_dir, "", "Specify path for loading cupti.so."); DEFINE_string( @@ -383,6 +392,26 @@ void* GetNCCLDsoHandle() { warning_msg); #endif } +void* GetHCCLDsoHandle() { + std::string warning_msg( + "You may need to install 'hccl2' from Huawei official website: " + "before install PaddlePaddle."); +#if defined(__APPLE__) || defined(__OSX__) + return GetDsoHandleFromSearchPath(FLAGS_nccl_dir, "libnccl.dylib", true, {}, + warning_msg); +#elif defined(PADDLE_WITH_HIP) && defined(PADDLE_WITH_RCCL) + return GetDsoHandleFromSearchPath(FLAGS_rccl_dir, "librccl.so", true); + +#elif defined(PADDLE_WITH_ASCEND_CL) + return GetDsoHandleFromSearchPath(FLAGS_hccl_dir, "libhccl.so", true, {}, + warning_msg); +#else + return GetDsoHandleFromSearchPath(FLAGS_nccl_dir, "libnccl.so", true, {}, + warning_msg); +#endif +} + + void* GetTensorRtDsoHandle() { #if defined(__APPLE__) || defined(__OSX__) diff --git a/paddle/fluid/platform/dynload/dynamic_loader.h b/paddle/fluid/platform/dynload/dynamic_loader.h index 1136184ce1fc9..10f566f87a6e9 100644 --- a/paddle/fluid/platform/dynload/dynamic_loader.h +++ b/paddle/fluid/platform/dynload/dynamic_loader.h @@ -34,6 +34,7 @@ void* GetNVRTCDsoHandle(); void* GetCUDADsoHandle(); void* GetWarpCTCDsoHandle(); void* GetNCCLDsoHandle(); +void* GetHCCLDsoHandle(); void* GetTensorRtDsoHandle(); void* GetMKLMLDsoHandle(); void* GetOpDsoHandle(const std::string& dso_name); diff --git a/paddle/fluid/platform/dynload/hccl.cc b/paddle/fluid/platform/dynload/hccl.cc new file mode 100644 index 0000000000000..d1050f45b4042 --- /dev/null +++ b/paddle/fluid/platform/dynload/hccl.cc @@ -0,0 +1,38 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. 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. */ + +#include "paddle/fluid/platform/dynload/hccl.h" + +namespace paddle { +namespace platform { +namespace dynload { + +std::once_flag hccl_dso_flag; +void *hccl_dso_handle; + +#define DEFINE_WRAP(__name) DynLoad__##__name __name + +HCCL_RAND_ROUTINE_EACH(DEFINE_WRAP); + +#if HCCL_VERSION_CODE >= 2212 +HCCL_RAND_ROUTINE_EACH_AFTER_2212(DEFINE_WRAP) +#endif + +#if HCCL_VERSION_CODE >= 2703 +HCCL_RAND_ROUTINE_EACH_AFTER_2703(DEFINE_WRAP) +#endif + +} // namespace dynload +} // namespace platform +} // namespace paddle diff --git a/paddle/fluid/platform/dynload/hccl.h b/paddle/fluid/platform/dynload/hccl.h new file mode 100644 index 0000000000000..2ad62213415c9 --- /dev/null +++ b/paddle/fluid/platform/dynload/hccl.h @@ -0,0 +1,84 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. 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. */ +#pragma once + +// #include +// #include +#include // NOLINT + +#include "paddle/fluid/platform/port.h" +#include "paddle/fluid/platform/dynload/hcom.h" +#include "paddle/fluid/platform/dynload/dynamic_loader.h" + +namespace paddle { +namespace platform { +namespace dynload { + +extern std::once_flag hccl_dso_flag; +extern void* hccl_dso_handle; + +#define DECLARE_DYNAMIC_LOAD_HCCL_WRAP(__name) \ + struct DynLoad__##__name { \ + template \ + auto operator()(Args... args) -> decltype(__name(args...)) { \ + using HCCL_func = decltype(&::__name); \ + std::call_once(hccl_dso_flag, []() { \ + hccl_dso_handle = paddle::platform::dynload::GetHCCLDsoHandle(); \ + }); \ + static void* p_##__name = dlsym(hccl_dso_handle, #__name); \ + return reinterpret_cast(p_##__name)(args...); \ + } \ + }; \ + extern DynLoad__##__name __name + +#define HCCL_RAND_ROUTINE_EACH(__macro) \ + __macro(hcom_init); \ + __macro(hcom_destroy); \ + __macro(hcom_bind_model); \ + __macro(hcom_unbind_model); \ + __macro(hcom_send); \ + __macro(hcom_receive); \ + __macro(hcom_broadcast); \ + __macro(hcom_all_gather); \ + __macro(hcom_all_reduce); \ + __macro(hcom_reduce_scatter); \ + __macro(hcom_create_group); \ + __macro(hcom_destroy_group); \ + __macro(hcom_get_rank_id); \ + __macro(hcom_get_local_rank_id); \ + __macro(hcom_get_local_rank_size); \ + __macro(hcom_get_split_strategy); \ + __macro(hcom_set_split_strategy_by_size); \ + __macro(hcom_set_split_strategy_by_index); \ + __macro(hcom_get_group_rank_from_world_rank); \ + __macro(hcom_get_world_rank_from_group_rank); + + +HCCL_RAND_ROUTINE_EACH(DECLARE_DYNAMIC_LOAD_HCCL_WRAP) + +#if HCCL_VERSION_CODE >= 2212 +#define HCCL_RAND_ROUTINE_EACH_AFTER_2212(__macro) __macro(HCCLBroadcast); +HCCL_RAND_ROUTINE_EACH_AFTER_2212(DECLARE_DYNAMIC_LOAD_HCCL_WRAP) +#endif + +#if HCCL_VERSION_CODE >= 2703 +#define HCCL_RAND_ROUTINE_EACH_AFTER_2703(__macro) \ + __macro(HCCLSend); \ + __macro(HCCLRecv); +HCCL_RAND_ROUTINE_EACH_AFTER_2703(DECLARE_DYNAMIC_LOAD_HCCL_WRAP) +#endif + +} // namespace dynload +} // namespace platform +} // namespace paddle diff --git a/paddle/fluid/platform/dynload/hcom.h b/paddle/fluid/platform/dynload/hcom.h new file mode 100644 index 0000000000000..d63e5dfc88ea4 --- /dev/null +++ b/paddle/fluid/platform/dynload/hcom.h @@ -0,0 +1,275 @@ +/** + * Copyright 2019-2020 Huawei Technologies Co., Ltd + * + * 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. + */ + +/** + * @file hcom.h + * @brief HCOM API + */ + +#ifndef HCOM_H_ +#define HCOM_H_ + +// #include +#include "paddle/fluid/platform/dynload/base.h" + +#ifdef __cplusplus +extern "C" { +#endif // __cplusplus + +/** + * @brief Initialize HCOM. + * + * @param rank_table A string identifying the rank table file path, include file name. + * @param identify A string identifying the identify for the rank. + * @return hcclResult_t + * @see hcom_destroy() + */ +extern hcclResult_t hcom_init(const char *rank_table, const char *identify); + +/** + * @brief Destroy HCOM + * + * @return hcclResult_t + * @see hcom_init() + */ +extern hcclResult_t hcom_destroy(void); + +/** + * @brief Bind the model. + * + * @param model A pointer identifying the model information. + * @param stream A pointer identifying the stream information. + * @return hcclResult_t + * @see hcom_unbind_model() + */ +extern hcclResult_t hcom_bind_model(rtModel_t model, rtStream_t stream); + +/** + * @brief Unbind the model. + * + * @param model An pointer identifying the model information. + * @return hcclResult_t + * @see hcom_unbind_model() + */ +extern hcclResult_t hcom_unbind_model(rtModel_t model); + +/** + * @brief All-gather operator. + * + * @param tag A string identifying the tag of the operator. + * @param inputPtr A pointer identifying the input data address of the operator. + * @param outputPtr A pointer identifying the output data address of the operator. + * @param inputCount An integer(u64) identifying the number of the input data. + * @param dataType The data type of the operator, must be one of the following types: int8, int32, float16, float32. + * @param group A string identifying the group name of ranks participating in the operator. + * @param stream A pointer identifying the stream information. + * @return hcclResult_t + */ +extern hcclResult_t hcom_all_gather(const char *tag, void *inputPtr, void *outputPtr, u64 inputCount, + hcclDataType_t dataType, const char *group, rtStream_t stream); + +/** + * @brief All-reduce operator. + * + * @param tag A string identifying the tag of the operator. + * @param inputPtr A pointer identifying the input data address of the operator. + * @param outputPtr A pointer identifying the output data address of the operator. + * @param count An integer(u64) identifying the number of the output data. + * @param dataType The data type of the operator, must be one of the following types: int8, int32, float16, float32. + * @param op The reduction type of the operator, must be one of the following types: sum, min, max, prod. + * @param group A string identifying the group name of ranks participating in the operator. + * @param stream A pointer identifying the stream information. + * @return hcclResult_t + */ +extern hcclResult_t hcom_all_reduce(const char *tag, void *inputPtr, void *outputPtr, u64 count, + hcclDataType_t dataType, hcclRedOp_t op, const char *group, rtStream_t stream); + +/** + * @brief Broadcast operator. + * + * @param tag A string identifying the tag of the operator. + * @param ptr A pointer identifying the data address of the operator. + * @param count An integer(u64) identifying the number of the data. + * @param dataType The data type of the operator, must be one of the following types: int8, int32, float16, float32. + * @param root An integer(u32) identifying the the root rank in the operator. + * @param group A string identifying the group name of ranks participating in the operator. + * @param stream A pointer identifying the stream information. + * @return hcclResult_t + */ +extern hcclResult_t hcom_broadcast(const char *tag, void *ptr, u64 count, hcclDataType_t dataType, u32 root, + const char *group, rtStream_t stream); + +/** + * @brief Reduce-scatter operator. + * + * @param tag A string identifying the tag of the operator. + * @param inputPtr A pointer identifying the input data address of the operator. + * @param outputPtr A pointer identifying the output data address of the operator. + * @param count An integer(u64) identifying the number of the data. + * @param dataType The data type of the operator, must be one of the following types: int8, int32, float16, float32. + * @param op The reduction type of the operator, must be one of the following types: sum, min, max, prod. + * @param group A string identifying the group name of ranks participating in the operator. + * @param stream A pointer identifying the stream information. + * @return hcclResult_t + */ +extern hcclResult_t hcom_reduce_scatter(const char *tag, void *inputPtr, void *outputPtr, u64 count, + hcclDataType_t dataType, hcclRedOp_t op, const char *group, rtStream_t stream); + +/** + * @brief Get the rank number in the group. + * + * @param group A string identifying the group name. + * @param rankSize A pointer identifying the rank number. + * @return hcclResult_t + */ +hcclResult_t hcom_get_rank_size(const char *group, u32 *rankSize); + +/** + * @brief Get the rank number of this rank's server within the group. + * + * @param group A string identifying the group name. + * @param localRankSize A pointer identifying the rank number. + * @return hcclResult_t + */ +hcclResult_t hcom_get_local_rank_size(const char *group, u32 *localRankSize); + +/** + * @brief Get the rank id of this rank. + * + * @param group A string identifying the group name. + * @param rankId A pointer identifying the rank id. + * @return hcclResult_t + */ +hcclResult_t hcom_get_rank_id(const char *group, u32 *rankId); + +/** + * @brief Get the local rank id of this rank's server within the group. + * + * @param group A string identifying the group name. + * @param localRankId A pointer identifying the local rank id. + * @return hcclResult_t + */ +hcclResult_t hcom_get_local_rank_id(const char *group, u32 *localRankId); + +/** + * @brief Get the world rank id according to the group rank id. + * + * @param group A string identifying the group name. + * @param groupRank An integer(u32) identifying the group rank id. + * @param worldRank A pointer identifying the world rank id. + * @return hcclResult_t + */ +hcclResult_t hcom_get_world_rank_from_group_rank(const char *group, u32 groupRank, u32 *worldRank); + +/** + * @brief Get the group rank id according to the world rank id. + * + * @param worldRank An integer(u32) identifying the world rank id. + * @param group A string identifying the group name. + * @param groupRank A pointer identifying the group rank id. + * @return hcclResult_t + */ +hcclResult_t hcom_get_group_rank_from_world_rank(u32 worldRank, const char *group, u32 *groupRank); + +/** + * @brief Create group. + * + * @param group A string identifying the group name. + * @param rankNum An integer(u32) identifying the number of ranks in the group. + * @param rankIds A list identifying the ranks in the group. + * @return hcclResult_t + */ +hcclResult_t hcom_create_group(const char *group, u32 rankNum, u32 *rankIds); + +/** + * @brief Destroy group + * + * @param group A string identifying the group name. + * @return hcclResult_t + */ +hcclResult_t hcom_destroy_group(const char *group); + +/** + * @brief Send operator. + * + * @param tag A string identifying the tag of the operator. + * @param inputPtr A pointer identifying the input data address of the operator. + * @param count An integer(u64) identifying the number of the data. + * @param dataType The data type of the operator, must be one of the following types: int8, int32, float16, float32. + * @param destRank An integer identifying the destination rank. + * @param srTag An integer identifying the send/recv message tag. + * The message will be send by the receive operator with the same "sr_tag". + * @param group A string identifying the group name of ranks participating in the operator. + * @param stream A pointer identifying the stream information. + * @return hcclResult_t + */ +hcclResult_t hcom_send(const char *tag, void *inputPtr, u64 count, hcclDataType_t dataType, + u32 destRank, u32 srTag, const char *group, rtStream_t stream); + +/** + * @brief Receive operator. + * + * @param tag A string identifying the tag of the operator. + * @param outputPtr A pointer identifying the output data address of the operator. + * @param count An integer(u64) identifying the number of the data. + * @param dataType The data type of the operator, must be one of the following types: int8, int32, float16, float32. + * @param srcRank An integer identifying the source rank. + * @param srTag An integer identifying the send/recv message tag. + * The message will be send by the send operator with the same "sr_tag". + * @param group A string identifying the group name of ranks participating in the operator. + * @param stream A pointer identifying the stream information. + * @return hcclResult_t + */ +hcclResult_t hcom_receive(const char *tag, void *outputPtr, u64 count, hcclDataType_t dataType, + u32 srcRank, u32 srTag, const char *group, rtStream_t stream); + +/** + * @brief Get the gradient split strategy with in the group. + * + * @param group A string identifying the group name. + * @param feature A pointer identifying the feature of the model. + * @param maxSegmentNum An integer(u32) identifying the max segments of gradients. + * @param segmentNum A pointer identifying the segments number of gradients. + * @param segmentIdx A list identifying the index of end gradient in each segment. + * @return hcclResult_t + */ +hcclResult_t hcom_get_split_strategy(const char *group, const struct model_feature *feature, + u32 maxSegmentNum, u32 *segmentNum, u32 *segmentIdx, GradSplitForceMode force = FORCE_NONE); + +/** + * @brief Set the gradient split strategy with in the group, according to gradient index. + * + * @param group A string identifying the group name. + * @param segmentNum An integer(u32) identifying the segments number of gradients. + * @param IdxList A list identifying the index of end gradient in each segment. + * @return hcclResult_t + */ +extern hcclResult_t hcom_set_split_strategy_by_index(const char *group, u32 segmentNum, const u32 *IdxList); + +/** + * @brief Set the gradient split strategy with in the group, according to gradient data size. + * + * @param group A string identifying the group name. + * @param segmentNum An integer(u32) identifying the segments number of gradients. + * @param sizeList A list identifying the percent of each segment. + * @return hcclResult_t + */ +extern hcclResult_t hcom_set_split_strategy_by_size(const char *group, u32 segmentNum, const float *sizeList); + +#ifdef __cplusplus +} +#endif // __cplusplus +#endif // HCOM_H_ diff --git a/paddle/fluid/platform/enforce.h b/paddle/fluid/platform/enforce.h index 3c8d256921d18..85d255a1f91c1 100644 --- a/paddle/fluid/platform/enforce.h +++ b/paddle/fluid/platform/enforce.h @@ -40,6 +40,7 @@ limitations under the License. */ #ifdef PADDLE_WITH_ASCEND_CL #include "acl/acl.h" +#include "paddle/fluid/platform/dynload/hcom.h" #endif // PADDLE_WITH_ASCEND_CL #include @@ -1012,6 +1013,7 @@ struct NPUStatusType {}; } DEFINE_NPU_STATUS_TYPE(aclError, ACL_ERROR_NONE); +DEFINE_NPU_STATUS_TYPE(hcclResult_t, HCCL_SUCCESS); } // namespace details inline std::string build_npu_error_msg(aclError stat) { @@ -1020,6 +1022,13 @@ inline std::string build_npu_error_msg(aclError stat) { return sout.str(); } +inline std::string build_npu_error_msg(hcclResult_t stat) { + std::ostringstream sout; + sout << " HCCL error, the error code is : " << stat << ". "; + return sout.str(); +} + + #define PADDLE_ENFORCE_NPU_SUCCESS(COND) \ do { \ auto __cond__ = (COND); \ diff --git a/paddle/fluid/platform/hccl_helper.h b/paddle/fluid/platform/hccl_helper.h new file mode 100644 index 0000000000000..e2d6be57ceecb --- /dev/null +++ b/paddle/fluid/platform/hccl_helper.h @@ -0,0 +1,364 @@ +// Copyright (c) 2018 PaddlePaddle Authors. 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. + +#pragma once + +#if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL) || defined(PADDLE_WITH_ASCEND_CL) + +#include +#include +#include +#include // NOLINT +#include +#include +#include + +#include "paddle/fluid/framework/data_type.h" +#include "paddle/fluid/platform/collective_helper.h" + +#ifdef PADDLE_WITH_NCCL +#include "paddle/fluid/platform/dynload/nccl.h" +#endif + +#ifdef PADDLE_WITH_RCCL +#include "paddle/fluid/platform/dynload/rccl.h" +#endif + +#ifdef PADDLE_WITH_ASCEND_CL +#include "paddle/fluid/platform/dynload/hccl.h" +#endif + +#include "paddle/fluid/platform/enforce.h" +#include "paddle/fluid/platform/float16.h" + +#define NCCL_ID_VARNAME "NCCLID" + +namespace paddle { +namespace platform { + +inline hcclDataType_t ToHCCLDataType(framework::proto::VarType::Type type) { + if (type == framework::proto::VarType::FP32) { + return HCCL_DATA_TYPE_FP32; + } else if (type == framework::proto::VarType::FP16) { + return HCCL_DATA_TYPE_FP16; + }else if (type == framework::proto::VarType::INT32) { + return HCCL_DATA_TYPE_INT32; + } else if (type == framework::proto::VarType::INT8) { + return HCCL_DATA_TYPE_INT8; + } + // else if (type == framework::proto::VarType::FP64) { + // return HCCL_DATA_TYPE_FP32; + // } + else { + PADDLE_THROW(platform::errors::Unimplemented( + "This datatype in hccl is not supported.")); + } +} + +// // NOTE(minqiyang): according to the ncclGroupEnd documentations: +// // https://docs.nvidia.com/deeplearning/sdk/nccl-api/ncclapidoc.html, +// // ncclGroupEnd will wait for all communicators to be initialized, which will +// // cause blocking problem when a runtime_error was thrown, so try only guard +// // NCCL actions when use it. +// class NCCLGroupGuard { +// public: +// static std::mutex &NCCLMutex() { +// static std::mutex mtx; +// return mtx; +// } + +// inline NCCLGroupGuard() { +// NCCLMutex().lock(); +// PADDLE_ENFORCE_CUDA_SUCCESS(dynload::ncclGroupStart()); +// } + +// inline ~NCCLGroupGuard() PADDLE_MAY_THROW { +// PADDLE_ENFORCE_CUDA_SUCCESS(dynload::ncclGroupEnd()); +// NCCLMutex().unlock(); +// } +// }; + +// struct NCCLContext { +// std::unique_ptr ctx_; +// ncclComm_t comm_; + +// explicit NCCLContext(int dev_id) +// : ctx_(new CUDADeviceContext(CUDAPlace(dev_id))), comm_{nullptr} {} + +// gpuStream_t stream() const { return ctx_->stream(); } +// ncclComm_t comm() const { return comm_; } + +// int device_id() const { +// return BOOST_GET_CONST(platform::CUDAPlace, ctx_->GetPlace()).device; +// } +// }; + +// struct NCCLContextMap { +// std::unordered_map contexts_; +// std::vector order_; + +// explicit NCCLContextMap(const std::vector &places, +// ncclUniqueId *nccl_id = nullptr, +// size_t num_trainers = 1, size_t trainer_id = 0) { +// PADDLE_ENFORCE_EQ(!places.empty(), true, +// platform::errors::InvalidArgument( +// "The NCCL place should not be empty.")); +// order_.reserve(places.size()); +// for (auto &p : places) { +// int dev_id = BOOST_GET_CONST(CUDAPlace, p).device; +// order_.emplace_back(dev_id); +// contexts_.emplace(dev_id, NCCLContext(dev_id)); +// } +// PADDLE_ENFORCE_EQ( +// order_.size(), contexts_.size(), +// platform::errors::Unavailable("NCCL Context Map does not support " +// "contain two or more same device.")); + +// std::unique_ptr comms(new ncclComm_t[order_.size()]); +// // if num_trainers == 1, should create a new nccl id for local comms. +// if (num_trainers == 1 && nccl_id == nullptr) { +// std::lock_guard guard(NCCLGroupGuard::NCCLMutex()); +// PADDLE_RETRY_CUDA_SUCCESS(platform::dynload::ncclCommInitAll( +// comms.get(), static_cast(order_.size()), order_.data())); +// } else { +// PADDLE_ENFORCE_NOT_NULL(nccl_id, platform::errors::InvalidArgument( +// "The NCCL id should not be null.")); +// { +// int nranks = num_trainers * order_.size(); +// NCCLGroupGuard gurad; +// for (size_t i = 0; i < order_.size(); ++i) { +// int gpu_id = order_[i]; +// int rank; +// if (order_.size() > 1) { +// rank = trainer_id * order_.size() + i; +// } else { +// rank = trainer_id; +// } +// VLOG(1) << "init nccl rank:" << rank << ", nranks:" << nranks +// << ", gpu_id:" << gpu_id << ", dev_id:" << order_[i]; +// SetDeviceId(gpu_id); +// PADDLE_RETRY_CUDA_SUCCESS(platform::dynload::ncclCommInitRank( +// comms.get() + i, nranks, *nccl_id, rank)); +// } +// } +// } +// int i = 0; +// for (auto &dev_id : order_) { +// contexts_.at(dev_id).comm_ = comms[i++]; +// } +// } + +// NCCLContextMap(const NCCLContextMap &other) = delete; +// NCCLContextMap &operator=(const NCCLContextMap &other) = delete; + +// CUDADeviceContext *DevCtx(int dev_id) const { return at(dev_id).ctx_.get(); } + +// CUDADeviceContext *DevCtx(platform::Place p) const { +// return DevCtx(BOOST_GET_CONST(CUDAPlace, p).device); +// } + +// const NCCLContext &at(platform::Place p) const { +// return this->at(BOOST_GET_CONST(CUDAPlace, p).device); +// } + +// const NCCLContext &at(int dev_id) const { return contexts_.at(dev_id); } + +// void WaitAll() { +// for (auto &p : contexts_) { +// p.second.ctx_->Wait(); +// } +// } +// }; + +// inline std::string GetFlatNCCLVarName(size_t pos) { +// if (pos == 0) { +// return NCCL_ID_VARNAME; +// } +// return string::Sprintf("%s_%d", NCCL_ID_VARNAME, static_cast(pos)); +// } + +// inline std::string GetHierarchicalExterNCCLVarName(size_t pos) { +// return string::Sprintf("Hierarchical_exter_%s_%d", NCCL_ID_VARNAME, +// static_cast(pos)); +// } +// inline std::string GetHierarchicalInterNCCLVarName(size_t pos) { +// return string::Sprintf("Hierarchical_inter_%s_%d", NCCL_ID_VARNAME, +// static_cast(pos)); +// } + +// class NCCLCommunicator { +// public: +// NCCLCommunicator() {} +// virtual ~NCCLCommunicator() PADDLE_MAY_THROW {} + +// NCCLContextMap *DefaultFlatCtx() const { +// if (flat_ctxs_.size() == 0) { +// return nullptr; +// } + +// return flat_ctxs_[0].get(); +// } + +// std::vector> *GetFlatCtxs() { +// return &flat_ctxs_; +// } + +// NCCLContextMap *GetFlatCtx(size_t run_order) const { +// return flat_ctxs_[run_order % flat_ctxs_.size()].get(); +// } + +// NCCLContextMap *GetRunEnvNCCLCtx(size_t run_order, +// bool use_hierarchical_allreduce) const { +// if (!use_hierarchical_allreduce) { +// return GetFlatCtx(run_order); +// } + +// return GetHierarchicalInterCtx(run_order); +// } + + +// *When nccl inits nccl comm using ncclCommInitAll, it meets error when +// *allreduce ophandle and sync_batch_norm_op use ncclallreduce parallelly. So +// *create a new nccl comm for sync_batch_norm_op. And these codes should be +// *polished with a unified nccl management. + +// NCCLContextMap *GetSyncBatchNormCtx( +// framework::Scope *scope, const std::vector &places) { +// auto *nccl_id_var = scope->FindVar(NCCL_ID_VARNAME); +// if (nccl_id_var != nullptr) { +// return DefaultFlatCtx(); +// } + +// if (sync_batch_norm_ctx_.get() == nullptr) { +// sync_batch_norm_ctx_.reset(new NCCLContextMap(places)); +// } +// return sync_batch_norm_ctx_.get(); +// } + +// void InitFlatCtxs(const std::vector &places, +// const std::vector &nccl_ids, +// size_t trainers_num, size_t trainer_id) { +// if (nccl_ids.size() == 0) { +// auto ptr = new platform::NCCLContextMap(places); +// VLOG(1) << "init local trainer"; +// flat_ctxs_.emplace_back(ptr); +// } else { +// for (size_t i = 0; i < nccl_ids.size(); i++) { +// auto ptr = new platform::NCCLContextMap(places, nccl_ids[i], +// trainers_num, trainer_id); +// VLOG(1) << "init trainer_id:" << trainer_id << ", comm no:" << i; +// flat_ctxs_.emplace_back(ptr); +// } +// } + +// // as Executor have no way to use ncclComm created by ParallelExecutor, +// // we assign all flatten contexts to NCCLCommContext to fix. +// int nranks = static_cast(trainers_num * places.size()); +// int nrings = static_cast(flat_ctxs_.size()); +// for (int ring_id = 0; ring_id < nrings; ++ring_id) { +// for (size_t p = 0; p < places.size(); ++p) { +// int rank = trainer_id * places.size() + p; +// int dev_id = BOOST_GET_CONST(CUDAPlace, places[p]).device; +// auto &ctx = flat_ctxs_[ring_id]->contexts_.at(dev_id); +// NCCLCommContext::Instance().AssignNCCLComm(ctx.comm_, nranks, rank, +// dev_id, ring_id); +// } +// } +// } + +// void InitHierarchicalCtxs(const std::vector &places, +// const std::vector &inter_nccl_ids, +// const std::vector &exter_nccl_ids, +// size_t trainers_num, size_t trainer_id, +// size_t inter_trainers_num, +// size_t exter_trainers_num) { +// PADDLE_ENFORCE_EQ( +// trainers_num, inter_trainers_num * exter_trainers_num, +// platform::errors::InvalidArgument( +// "trainers_num:%llu != inter_trainers_num:%llu * " +// "exter_trainers_num:%llu", +// trainers_num, inter_trainers_num, exter_trainers_num)); + +// PADDLE_ENFORCE_GT( +// inter_trainers_num, 1, +// platform::errors::InvalidArgument( +// "The inter_trainers_num:%llu should be larger than 1.", +// inter_trainers_num)); + +// int inter_trainer_id = trainer_id % inter_trainers_num; +// for (size_t i = 0; i < inter_nccl_ids.size(); i++) { +// VLOG(1) << "init inter_trainer_id:" << inter_trainer_id +// << ", comm no:" << i; +// auto local = new NCCLContextMap(places, inter_nccl_ids[i], +// inter_trainers_num, inter_trainer_id); + +// h_inter_ctxs_.emplace_back(local); +// } + +// int exter_trainer_id = -1; +// if (trainer_id % inter_trainers_num == 0) { +// exter_trainer_id = trainer_id / inter_trainers_num; +// } + +// if (exter_trainer_id >= 0) { +// for (size_t i = 0; i < exter_nccl_ids.size(); i++) { +// auto ex = new NCCLContextMap(places, exter_nccl_ids[i], +// exter_trainers_num, exter_trainer_id); +// VLOG(1) << "init exter_trainer_id:" << exter_trainer_id +// << ", comm no:" << i; +// h_exter_ctxs_.emplace_back(ex); +// } +// } +// } + +// bool NeedExterAllReduce() const { return h_exter_ctxs_.size() > 0; } + +// NCCLContextMap *GetHierarchicalInterCtx(size_t run_order) const { +// PADDLE_ENFORCE_GT(h_inter_ctxs_.size(), 0, +// platform::errors::InvalidArgument( +// "Hierarchical ctxs should be initialized firstly!")); +// return h_inter_ctxs_[run_order % h_inter_ctxs_.size()].get(); +// } + +// NCCLContextMap *GetHierarchicalExterCtx(size_t run_order) const { +// PADDLE_ENFORCE_GT(h_exter_ctxs_.size(), 0, +// platform::errors::InvalidArgument( +// "Hierarchical ctxs should be initialized firstly!")); +// return h_exter_ctxs_[run_order % h_exter_ctxs_.size()].get(); +// } + +// std::vector> *GetHierarchicalInterCtxs() { +// return &h_inter_ctxs_; +// } + +// std::vector> *GetHierarchicalExterCtxs() { +// return &h_exter_ctxs_; +// } + +// protected: +// // Support multi nccl comm on default nccl ring while NCCLContextMap can't. +// std::vector> flat_ctxs_; + +// // h_inter_ctxs_ and h_exter_ctxs_ are for 2d allreduce. +// // And h_exter_ctxs_ can support multi comm too. +// std::vector> h_inter_ctxs_; +// std::vector> h_exter_ctxs_; + +// // just used for sync_batch_norm op. +// std::unique_ptr sync_batch_norm_ctx_; +// }; + +} // namespace platform +} // namespace paddle +#endif diff --git a/paddle/fluid/pybind/op_function_generator.cc b/paddle/fluid/pybind/op_function_generator.cc index 3ac1116fde5d5..81e1b1b41242c 100644 --- a/paddle/fluid/pybind/op_function_generator.cc +++ b/paddle/fluid/pybind/op_function_generator.cc @@ -326,7 +326,7 @@ GenerateOpFunctions(const std::string& module_name) { } ins_initializer += "}"; - if (input_args.back() == ',') { + if (!input_args.empty() && input_args.back() == ',') { input_args.pop_back(); }