diff --git a/cmake/generic.cmake b/cmake/generic.cmake index d0b5eaec2e2a50acf17e5dd1d1aeb0ec3e614fbf..471e3929069d0d28105404b4f0f6baa303faf0e0 100644 --- a/cmake/generic.cmake +++ b/cmake/generic.cmake @@ -244,11 +244,11 @@ function(cc_test TARGET_NAME) cmake_parse_arguments(cc_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) add_executable(${TARGET_NAME} ${cc_test_SRCS}) # Support linking flags: --whole-archive (Linux) / -force_load (MacOS) - target_circle_link_libraries(${TARGET_NAME} ${cc_test_DEPS} paddle_gtest_main paddle_memory gtest gflags) + target_circle_link_libraries(${TARGET_NAME} ${cc_test_DEPS} paddle_gtest_main paddle_memory gtest gflags glog) if("${cc_test_DEPS}" MATCHES "ARCHIVE_START") list(REMOVE_ITEM cc_test_DEPS ARCHIVE_START ARCHIVE_END) endif() - add_dependencies(${TARGET_NAME} ${cc_test_DEPS} paddle_gtest_main paddle_memory gtest gflags) + add_dependencies(${TARGET_NAME} ${cc_test_DEPS} paddle_gtest_main paddle_memory gtest gflags glog) add_test(NAME ${TARGET_NAME} COMMAND ${TARGET_NAME} ${cc_test_ARGS} WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}) @@ -311,8 +311,8 @@ function(nv_test TARGET_NAME) set(multiValueArgs SRCS DEPS) cmake_parse_arguments(nv_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) cuda_add_executable(${TARGET_NAME} ${nv_test_SRCS}) - target_link_libraries(${TARGET_NAME} ${nv_test_DEPS} paddle_gtest_main paddle_memory gtest gflags) - add_dependencies(${TARGET_NAME} ${nv_test_DEPS} paddle_gtest_main paddle_memory gtest gflags) + target_link_libraries(${TARGET_NAME} ${nv_test_DEPS} paddle_gtest_main paddle_memory gtest gflags glog) + add_dependencies(${TARGET_NAME} ${nv_test_DEPS} paddle_gtest_main paddle_memory gtest gflags glog) add_test(${TARGET_NAME} ${TARGET_NAME}) endif() endfunction(nv_test) diff --git a/doc/design/dist_refactor/distributed_architecture.md b/doc/design/fluid_dist/distributed_architecture.md similarity index 100% rename from doc/design/dist_refactor/distributed_architecture.md rename to doc/design/fluid_dist/distributed_architecture.md diff --git a/doc/design/dist_refactor/multi_cpu.md b/doc/design/fluid_dist/multi_cpu.md similarity index 100% rename from doc/design/dist_refactor/multi_cpu.md rename to doc/design/fluid_dist/multi_cpu.md diff --git a/doc/design/dist_refactor/parameter_server.md b/doc/design/fluid_dist/parameter_server.md similarity index 86% rename from doc/design/dist_refactor/parameter_server.md rename to doc/design/fluid_dist/parameter_server.md index 805dd13048d41b995d2a01cda52b2ea33e4bbe1d..6ce48dfbfce8b094684b412ebfda7e505ddc30ae 100644 --- a/doc/design/dist_refactor/parameter_server.md +++ b/doc/design/fluid_dist/parameter_server.md @@ -59,6 +59,17 @@ After converting: queue. It will block until the queue has the required number of tensors. +### Sparse Update + +For embedding layers, the gradient may have many rows containing only 0 when training, +if the gradient uses a dense tensor to do parameter optimization, +it could spend unnecessary memory, slow down the calculations and waste +the bandwidth while doing distributed training. +In Fluid, we introduce [SelectedRows](../selected_rows.md) to represent a list of rows containing +non-zero gradient data. So when we do parameter optimization both locally and remotely, +we only need to send those non-zero rows to the optimizer operators: + + ### Benefits @@ -91,6 +102,6 @@ After converting: `min_count` attribute), does our current design support it? (similar question for the *Add* OP) +### References -### References: [1] [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45166.pdf) diff --git a/doc/design/dist_refactor/src/compiler.graffle b/doc/design/fluid_dist/src/compiler.graffle similarity index 100% rename from doc/design/dist_refactor/src/compiler.graffle rename to doc/design/fluid_dist/src/compiler.graffle diff --git a/doc/design/dist_refactor/src/compiler.png b/doc/design/fluid_dist/src/compiler.png similarity index 100% rename from doc/design/dist_refactor/src/compiler.png rename to doc/design/fluid_dist/src/compiler.png diff --git a/doc/design/dist_refactor/src/dist-graph.graffle b/doc/design/fluid_dist/src/dist-graph.graffle similarity index 100% rename from doc/design/dist_refactor/src/dist-graph.graffle rename to doc/design/fluid_dist/src/dist-graph.graffle diff --git a/doc/design/dist_refactor/src/dist-graph.png b/doc/design/fluid_dist/src/dist-graph.png similarity index 100% rename from doc/design/dist_refactor/src/dist-graph.png rename to doc/design/fluid_dist/src/dist-graph.png diff --git a/doc/design/dist_refactor/src/distributed_architecture.graffle b/doc/design/fluid_dist/src/distributed_architecture.graffle similarity index 100% rename from doc/design/dist_refactor/src/distributed_architecture.graffle rename to doc/design/fluid_dist/src/distributed_architecture.graffle diff --git a/doc/design/dist_refactor/src/distributed_architecture.png b/doc/design/fluid_dist/src/distributed_architecture.png similarity index 100% rename from doc/design/dist_refactor/src/distributed_architecture.png rename to doc/design/fluid_dist/src/distributed_architecture.png diff --git a/doc/design/dist_refactor/src/local-graph.graffle b/doc/design/fluid_dist/src/local-graph.graffle similarity index 100% rename from doc/design/dist_refactor/src/local-graph.graffle rename to doc/design/fluid_dist/src/local-graph.graffle diff --git a/doc/design/dist_refactor/src/local-graph.png b/doc/design/fluid_dist/src/local-graph.png similarity index 100% rename from doc/design/dist_refactor/src/local-graph.png rename to doc/design/fluid_dist/src/local-graph.png diff --git a/doc/design/dist_refactor/src/local_architecture.graffle b/doc/design/fluid_dist/src/local_architecture.graffle similarity index 100% rename from doc/design/dist_refactor/src/local_architecture.graffle rename to doc/design/fluid_dist/src/local_architecture.graffle diff --git a/doc/design/dist_refactor/src/local_architecture.png b/doc/design/fluid_dist/src/local_architecture.png similarity index 100% rename from doc/design/dist_refactor/src/local_architecture.png rename to doc/design/fluid_dist/src/local_architecture.png diff --git a/doc/design/dist_refactor/src/multi-threads.graffle b/doc/design/fluid_dist/src/multi-threads.graffle similarity index 100% rename from doc/design/dist_refactor/src/multi-threads.graffle rename to doc/design/fluid_dist/src/multi-threads.graffle diff --git a/doc/design/dist_refactor/src/multi-threads/multi-threads@3x.png b/doc/design/fluid_dist/src/multi-threads/multi-threads@3x.png similarity index 100% rename from doc/design/dist_refactor/src/multi-threads/multi-threads@3x.png rename to doc/design/fluid_dist/src/multi-threads/multi-threads@3x.png diff --git a/doc/design/dist_refactor/src/multi-threads/single-thread@3x.png b/doc/design/fluid_dist/src/multi-threads/single-thread@3x.png similarity index 100% rename from doc/design/dist_refactor/src/multi-threads/single-thread@3x.png rename to doc/design/fluid_dist/src/multi-threads/single-thread@3x.png diff --git a/doc/design/dist_refactor/src/paddle-compile.graffle b/doc/design/fluid_dist/src/paddle-compile.graffle similarity index 100% rename from doc/design/dist_refactor/src/paddle-compile.graffle rename to doc/design/fluid_dist/src/paddle-compile.graffle diff --git a/doc/design/dist_refactor/src/paddle-compile.png b/doc/design/fluid_dist/src/paddle-compile.png similarity index 100% rename from doc/design/dist_refactor/src/paddle-compile.png rename to doc/design/fluid_dist/src/paddle-compile.png diff --git a/doc/design/dist_refactor/src/remote_executor.graffle b/doc/design/fluid_dist/src/remote_executor.graffle similarity index 100% rename from doc/design/dist_refactor/src/remote_executor.graffle rename to doc/design/fluid_dist/src/remote_executor.graffle diff --git a/doc/design/dist_refactor/src/remote_executor.png b/doc/design/fluid_dist/src/remote_executor.png similarity index 100% rename from doc/design/dist_refactor/src/remote_executor.png rename to doc/design/fluid_dist/src/remote_executor.png diff --git a/doc/design/fluid_dist/src/sparse_update.graffle b/doc/design/fluid_dist/src/sparse_update.graffle new file mode 100644 index 0000000000000000000000000000000000000000..08d689a58f83698d8c1158ee3990ed8abf3a7a9a Binary files /dev/null and b/doc/design/fluid_dist/src/sparse_update.graffle differ diff --git a/doc/design/fluid_dist/src/sparse_update.png b/doc/design/fluid_dist/src/sparse_update.png new file mode 100644 index 0000000000000000000000000000000000000000..8c872e6ac479f7d1b818a4a207956c43155d0ad7 Binary files /dev/null and b/doc/design/fluid_dist/src/sparse_update.png differ diff --git a/doc/v2/dev/index_cn.rst b/doc/v2/dev/index_cn.rst index c488191b8174531905e44cb9443ee539d4cb1ed3..aee3c68de05de26df3cd79170fa7f4ecad4bf386 100644 --- a/doc/v2/dev/index_cn.rst +++ b/doc/v2/dev/index_cn.rst @@ -1,9 +1,24 @@ 开发标准 ======== +PaddlePaddle遵守如下三个部分的代码和文档规范。 + +PaddlePaddle使用git做版本管理，docker作为构建和测试环境。代码中包含了Cuda, C++, Python, Shell等多种编程语言。语言规范遵守Google C++ Style, Pep-8, 代码库中包含自动化检查工具做风格检查。代码注释需要遵守Doxygen规范，不满足风格要求的代码会编译失败。关于如何使用git, 构建测试及代码开发, 我们提供了如下指南。 .. toctree:: :maxdepth: 1 contribute_to_paddle_cn.md + +PaddlePaddle面向国内外用户，包含了中文和英文两部分的文档。设计文档和issue问题描述都推荐使用英文。对于设计文档，重在问题描述，背景阐述，然后才是解决方案。文档由Sphinx生成，因此代码注释也需要符合Sphinx文档标准。推荐本地使用paddlepaddle.org工具编译生成和预览文档，请参阅如下文档。 + +.. toctree:: + :maxdepth: 1 + write_docs_cn.rst + +PaddlePaddle V2 使用新增Layer方式定义新的操作。组合基础API可以实现多种复杂Layer, 满足绝大多数应用。如需要定制Layer，请参阅如下文档，欢迎提交patch。 + +.. toctree:: + :maxdepth: 1 + new_layer_cn.rst diff --git a/doc/v2/howto/cluster/cmd_argument_cn.md b/doc/v2/howto/cluster/cmd_argument_cn.md index 40e1dde4858b802c2e703bcca4b71730facde5ef..c0ba093cbf2eac5c3b60a0b071b31776a11998f3 100644 --- a/doc/v2/howto/cluster/cmd_argument_cn.md +++ b/doc/v2/howto/cluster/cmd_argument_cn.md @@ -71,6 +71,13 @@ paddle.init( - trainer_id：**必选，默认0**，每个trainer的唯一ID，从0开始的整数 - pservers：**必选，默认127.0.0.1**，当前训练任务启动的pserver的IP列表，多个IP使用“,”隔开 +```python +trainer = paddle.trainer.SGD(..., is_local=False) +``` + +参数说明 + +- is_local: **必选, 默认True**, 是否使用PServer更新参数 ## 准备数据集 diff --git a/doc/v2/howto/cluster/cmd_argument_en.md b/doc/v2/howto/cluster/cmd_argument_en.md index 40179c28f83800c1c74a6045f8fac6841bdafeaa..df1381a00fa0fa129eecffe002164c489a4183aa 100644 --- a/doc/v2/howto/cluster/cmd_argument_en.md +++ b/doc/v2/howto/cluster/cmd_argument_en.md @@ -73,6 +73,14 @@ Parameter Description - trainer_id: **required, default 0**, ID for every trainer, start from 0. - pservers: **required, default 127.0.0.1**, list of IPs of parameter servers, separated by ",". +```python +trainer = paddle.trainer.SGD(..., is_local=False) +``` + +Parameter Description + +- is_local: **required, default True**, whether update parameters by PServer. + ## Prepare Training Dataset Here's some example code [prepare.py](https://github.com/PaddlePaddle/Paddle/tree/develop/doc/howto/usage/cluster/src/word2vec/prepare.py), it will download public `imikolov` dataset and split it into multiple files according to job parallelism(trainers count). Modify `SPLIT_COUNT` at the begining of `prepare.py` to change the count of output files. diff --git a/paddle/fluid/framework/block_desc.cc b/paddle/fluid/framework/block_desc.cc index d72b64700f7cf680501fd3e355d20e694f1f097d..3693bc25d81a8309df1a6ddf3d9b08d484596ea9 100644 --- a/paddle/fluid/framework/block_desc.cc +++ b/paddle/fluid/framework/block_desc.cc @@ -135,6 +135,14 @@ OpDesc *BlockDesc::PrependOp() { return ops_.front().get(); } +OpDesc *BlockDesc::InsertOp(size_t index) { + need_update_ = true; + auto it = ops_.begin() + index; + std::unique_ptr new_op(new OpDesc(this)); + it = ops_.insert(it, std::move(new_op)); + return (*it).get(); +} + void BlockDesc::RemoveOp(size_t s, size_t e) { if (ops_.begin() + s == ops_.end() || ops_.begin() + e == ops_.end()) { return; diff --git a/paddle/fluid/framework/block_desc.h b/paddle/fluid/framework/block_desc.h index 3bd90f38907c0a45ae0c9bb00706e5c127f08417..185f018ac1b5863e0ee86fdaa17df1ccbc6e030e 100644 --- a/paddle/fluid/framework/block_desc.h +++ b/paddle/fluid/framework/block_desc.h @@ -87,6 +87,8 @@ class BlockDesc { OpDesc *PrependOp(); + OpDesc *InsertOp(size_t index); + void RemoveOp(size_t s, size_t e); std::vector AllOps() const; diff --git a/paddle/fluid/framework/executor.cc b/paddle/fluid/framework/executor.cc index f8e7d0d99074936ad81c4ddc52be6907ead4c27d..5cae38b2a857b2037f0e5ae4da50d1591da0c11a 100644 --- a/paddle/fluid/framework/executor.cc +++ b/paddle/fluid/framework/executor.cc @@ -34,6 +34,15 @@ DEFINE_bool(check_nan_inf, false, namespace paddle { namespace framework { +struct ExecutorPrepareContext { + ExecutorPrepareContext(const framework::ProgramDesc& prog, size_t block_id) + : prog_(prog), block_id_(block_id) {} + + framework::ProgramDesc prog_; + size_t block_id_; + std::vector> ops_; +}; + Executor::Executor(const platform::Place& place) : place_(place) {} static void CreateTensor(Variable* var, proto::VarType::Type var_type) { @@ -85,73 +94,9 @@ static void CheckTensorNANOrInf(const std::string& name, void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id, bool create_local_scope, bool create_vars) { - // TODO(tonyyang-svail): - // - only runs on the first device (i.e. no interdevice communication) - // - will change to use multiple blocks for RNN op and Cond Op - PADDLE_ENFORCE_LT(static_cast(block_id), pdesc.Size()); - auto& block = pdesc.Block(block_id); - - Scope* local_scope = scope; - if (create_vars) { - if (create_local_scope) { - local_scope = &scope->NewScope(); - for (auto& var : block.AllVars()) { - if (var->Name() == framework::kEmptyVarName) { - continue; - } - - if (var->Persistable()) { - auto* ptr = scope->Var(var->Name()); - CreateTensor(ptr, var->GetType()); - VLOG(3) << "Create Variable " << var->Name() - << " global, which pointer is " << ptr; - } else { - auto* ptr = local_scope->Var(var->Name()); - CreateTensor(ptr, var->GetType()); - VLOG(3) << "Create Variable " << var->Name() - << " locally, which pointer is " << ptr; - } - } - } else { - for (auto& var : block.AllVars()) { - auto* ptr = local_scope->Var(var->Name()); - CreateTensor(ptr, var->GetType()); - VLOG(3) << "Create variable " << var->Name() << ", which pointer is " - << ptr; - } - } // if (create_local_scope) - } // if (create_vars) - - for (auto& op_desc : block.AllOps()) { - auto op = paddle::framework::OpRegistry::CreateOp(*op_desc); - - VLOG(4) << place_ << " " << op->DebugStringEx(local_scope); - op->Run(*local_scope, place_); - VLOG(3) << place_ << " " << op->DebugStringEx(local_scope); - - if (FLAGS_benchmark) { - VLOG(2) << "Memory used after operator " + op->Type() + " running: " - << memory::memory_usage(place_); - } - if (FLAGS_check_nan_inf) { - for (auto& vname : op->OutputVars(true)) { - auto* var = local_scope->FindVar(vname); - if (var == nullptr) continue; - if (var->IsType()) { - CheckTensorNANOrInf(vname, var->Get()); - } - } - } - } - if (create_vars && create_local_scope) { - scope->DeleteScope(local_scope); - } - if (FLAGS_benchmark) { - VLOG(2) << "-------------------------------------------------------"; - VLOG(2) << "Memory used after deleting local scope: " - << memory::memory_usage(place_); - VLOG(2) << "-------------------------------------------------------"; - } + auto* ctx = Prepare(pdesc, block_id); + RunPreparedContext(ctx, scope, create_local_scope, create_vars); + delete ctx; } // Check whether the block already has feed operators and feed_holder. @@ -313,5 +258,81 @@ void Executor::Run(const ProgramDesc& program, Scope* scope, delete copy_program; } +ExecutorPrepareContext* Executor::Prepare(const ProgramDesc& program, + int block_id) { + auto* ctx = new ExecutorPrepareContext(program, block_id); + PADDLE_ENFORCE_LT(static_cast(block_id), program.Size()); + auto& block = program.Block(block_id); + for (auto& op_desc : block.AllOps()) { + ctx->ops_.push_back(OpRegistry::CreateOp(*op_desc)); + } + return ctx; +} + +void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope, + bool create_local_scope, bool create_vars) { + auto& block = ctx->prog_.Block(ctx->block_id_); + + Scope* local_scope = scope; + if (create_vars) { + if (create_local_scope) { + local_scope = &scope->NewScope(); + for (auto& var : block.AllVars()) { + if (var->Name() == framework::kEmptyVarName) { + continue; + } + + if (var->Persistable()) { + auto* ptr = scope->Var(var->Name()); + CreateTensor(ptr, var->GetType()); + VLOG(3) << "Create Variable " << var->Name() + << " global, which pointer is " << ptr; + } else { + auto* ptr = local_scope->Var(var->Name()); + CreateTensor(ptr, var->GetType()); + VLOG(3) << "Create Variable " << var->Name() + << " locally, which pointer is " << ptr; + } + } + } else { + for (auto& var : block.AllVars()) { + auto* ptr = local_scope->Var(var->Name()); + CreateTensor(ptr, var->GetType()); + VLOG(3) << "Create variable " << var->Name() << ", which pointer is " + << ptr; + } + } // if (create_local_scope) + } // if (create_vars) + + for (auto& op : ctx->ops_) { + VLOG(4) << place_ << " " << op->DebugStringEx(local_scope); + op->Run(*local_scope, place_); + VLOG(3) << place_ << " " << op->DebugStringEx(local_scope); + + if (FLAGS_benchmark) { + VLOG(2) << "Memory used after operator " + op->Type() + " running: " + << memory::memory_usage(place_); + } + if (FLAGS_check_nan_inf) { + for (auto& vname : op->OutputVars(true)) { + auto* var = local_scope->FindVar(vname); + if (var == nullptr) continue; + if (var->IsType()) { + CheckTensorNANOrInf(vname, var->Get()); + } + } + } + } + if (create_vars && create_local_scope) { + scope->DeleteScope(local_scope); + } + if (FLAGS_benchmark) { + VLOG(2) << "-------------------------------------------------------"; + VLOG(2) << "Memory used after deleting local scope: " + << memory::memory_usage(place_); + VLOG(2) << "-------------------------------------------------------"; + } +} + } // namespace framework } // namespace paddle diff --git a/paddle/fluid/framework/executor.h b/paddle/fluid/framework/executor.h index c1f4d4e02a951e8b127b66cae125309e4798cc76..28ce3315154cea45412984df4daf7385ce2cf572 100644 --- a/paddle/fluid/framework/executor.h +++ b/paddle/fluid/framework/executor.h @@ -22,7 +22,7 @@ limitations under the License. */ namespace paddle { namespace framework { - +struct ExecutorPrepareContext; class Executor { public: // TODO(dzhwinter) : Do not rely on this function, it will be removed @@ -38,8 +38,8 @@ class Executor { * ProgramDesc * Scope */ - void Run(const ProgramDesc&, Scope*, int, bool create_local_scope = true, - bool create_vars = true); + void Run(const ProgramDesc& prog, Scope* scope, int block_id, + bool create_local_scope = true, bool create_vars = true); void Run(const ProgramDesc& program, Scope* scope, std::map& feed_targets, @@ -47,6 +47,13 @@ class Executor { const std::string& feed_holder_name = "feed", const std::string& fetch_holder_name = "fetch"); + static ExecutorPrepareContext* Prepare(const ProgramDesc& program, + int block_id); + + void RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope, + bool create_local_scope = true, + bool create_vars = true); + private: const platform::Place place_; }; diff --git a/paddle/fluid/framework/operator.cc b/paddle/fluid/framework/operator.cc index ac6289c5abe8f40ae9ee32aa3d58cdef3ff0e836..371c2fad97b1efd06eea9ac631122f194e65d656 100644 --- a/paddle/fluid/framework/operator.cc +++ b/paddle/fluid/framework/operator.cc @@ -74,6 +74,9 @@ void OperatorBase::Run(const Scope& scope, const platform::Place& place) { platform::SetDeviceId(dev_id); #endif } + // profile + auto* dev_ctx = platform::DeviceContextPool::Instance().Get(place); + platform::RecordEvent record_event(Type(), dev_ctx); RunImpl(scope, place); } @@ -497,9 +500,7 @@ void OperatorWithKernel::RunImpl(const Scope& scope, RuntimeInferShapeContext infer_shape_ctx(*this, scope); this->InferShape(&infer_shape_ctx); platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance(); - auto dev_ctx = pool.Get(place); - // profile - platform::RecordEvent record_event(Type(), dev_ctx); + auto* dev_ctx = pool.Get(place); // check if op[type] has kernel registered. auto& all_op_kernels = AllOpKernels(); auto kernels_iter = all_op_kernels.find(type_); diff --git a/paddle/fluid/framework/reader.h b/paddle/fluid/framework/reader.h index 1be3f4ef1f46bd8d72a285afa69b52d6f519ccf5..e820c3d07e85fd1dea9080786b48ad031330ee00 100644 --- a/paddle/fluid/framework/reader.h +++ b/paddle/fluid/framework/reader.h @@ -65,12 +65,25 @@ class ReaderHolder { ReaderBase* Get() const { return reader_.get(); } - void ReadNext(std::vector* out) { reader_->ReadNext(out); } - void ReInit() { reader_->ReInit(); } + void ReadNext(std::vector* out) { + PADDLE_ENFORCE_NOT_NULL(reader_); + reader_->ReadNext(out); + } + void ReInit() { + PADDLE_ENFORCE_NOT_NULL(reader_); + reader_->ReInit(); + } - DDim shape(size_t idx) const { return reader_->shape(idx); } - std::vector shapes() const { return reader_->shapes(); } + DDim shape(size_t idx) const { + PADDLE_ENFORCE_NOT_NULL(reader_); + return reader_->shape(idx); + } + std::vector shapes() const { + PADDLE_ENFORCE_NOT_NULL(reader_); + return reader_->shapes(); + } void set_shapes(const std::vector& shapes) { + PADDLE_ENFORCE_NOT_NULL(reader_); reader_->set_shapes(shapes); } diff --git a/paddle/fluid/framework/scope.cc b/paddle/fluid/framework/scope.cc index ea6c8cebd3ff16451c974bf3a0ded9d822a9caf8..17e38b1cf042657834b4d0d1c12cbbb92f19fa45 100644 --- a/paddle/fluid/framework/scope.cc +++ b/paddle/fluid/framework/scope.cc @@ -16,6 +16,7 @@ limitations under the License. */ #include // for unique_ptr #include // for call_once +#include #include "glog/logging.h" #include "paddle/fluid/framework/threadpool.h" #include "paddle/fluid/string/printf.h" @@ -102,6 +103,18 @@ void Scope::DeleteScope(Scope* scope) { } } +void Scope::EraseVars(std::vector& var_names) { + std::set var_set(var_names.begin(), var_names.end()); + for (auto it = vars_.begin(); it != vars_.end();) { + if (var_set.find(it->first) != var_set.end()) { + delete it->second; + it = vars_.erase(it); + } else { + ++it; + } + } +} + void Scope::Rename(const std::string& origin_name, const std::string& new_name) const { auto origin_it = vars_.find(origin_name); diff --git a/paddle/fluid/framework/scope.h b/paddle/fluid/framework/scope.h index d8fad162e59c8ea6465b2dc78c90709a758ace24..c1e1f49caaa5a60df0e97289aada465b45213971 100644 --- a/paddle/fluid/framework/scope.h +++ b/paddle/fluid/framework/scope.h @@ -51,6 +51,8 @@ class Scope { /// Create a variable with a scope-unique name. Variable* Var(std::string* name = nullptr); + void EraseVars(std::vector& var_names); + /// Find a variable in the scope or any of its ancestors. Returns /// nullptr if cannot find. Variable* FindVar(const std::string& name) const; diff --git a/paddle/fluid/inference/tests/test_helper.h b/paddle/fluid/inference/tests/test_helper.h index 0f5fe6d0aa9a5522c67a3c06f8677f1f2f259eb3..dce541c0971a6ff9a3728e915fe8c3d009c23550 100644 --- a/paddle/fluid/inference/tests/test_helper.h +++ b/paddle/fluid/inference/tests/test_helper.h @@ -115,11 +115,11 @@ void TestInference(const std::string& dirname, #endif } - // Enable the profiler - paddle::platform::EnableProfiler(state); - // 2. Initialize the inference_program and load parameters std::unique_ptr inference_program; + + // Enable the profiler + paddle::platform::EnableProfiler(state); { paddle::platform::RecordEvent record_event( "init_program", @@ -143,6 +143,10 @@ void TestInference(const std::string& dirname, inference_program = paddle::inference::Load(executor, *scope, dirname); } } + // Disable the profiler and print the timing information + paddle::platform::DisableProfiler(paddle::platform::EventSortingKey::kDefault, + "load_program_profiler.txt"); + paddle::platform::ResetProfiler(); // 3. Get the feed_target_names and fetch_target_names const std::vector& feed_target_names = @@ -165,6 +169,12 @@ void TestInference(const std::string& dirname, // 6. Run the inference program { + // Ignore the profiling results of the first run + executor.Run(*inference_program, scope, feed_targets, fetch_targets); + + // Enable the profiler + paddle::platform::EnableProfiler(state); + // Run repeat times to profile the performance for (int i = 0; i < repeat; ++i) { paddle::platform::RecordEvent record_event( @@ -173,12 +183,13 @@ void TestInference(const std::string& dirname, executor.Run(*inference_program, scope, feed_targets, fetch_targets); } - } - // Disable the profiler and print the timing information - paddle::platform::DisableProfiler(paddle::platform::EventSortingKey::kDefault, - "profiler.txt"); - paddle::platform::ResetProfiler(); + // Disable the profiler and print the timing information + paddle::platform::DisableProfiler( + paddle::platform::EventSortingKey::kDefault, + "run_inference_profiler.txt"); + paddle::platform::ResetProfiler(); + } delete scope; } diff --git a/paddle/fluid/operators/CMakeLists.txt b/paddle/fluid/operators/CMakeLists.txt index 5d436a7e0c3752c889c19820507589f34d3bee94..625e0f7561899d30b40f9daa56f743a37bdaa27f 100644 --- a/paddle/fluid/operators/CMakeLists.txt +++ b/paddle/fluid/operators/CMakeLists.txt @@ -222,8 +222,6 @@ cc_test(scatter_test SRCS scatter_test.cc DEPS tensor) cc_test(beam_search_decode_op_test SRCS beam_search_decode_op_test.cc DEPS lod_tensor) cc_test(beam_search_op_test SRCS beam_search_op_test.cc DEPS lod_tensor beam_search_op) cc_test(strided_memcpy_test SRCS strided_memcpy_test.cc DEPS tensor paddle_memory) -if(WITH_GPU) - cc_test(nccl_op_test SRCS nccl_op_test.cu.cc DEPS nccl_op gpu_info device_context) -endif() cc_test(save_load_op_test SRCS save_load_op_test.cc DEPS save_op load_op) cc_test(save_load_combine_op_test SRCS save_load_combine_op_test.cc DEPS save_combine_op load_combine_op) +nv_test(nccl_op_test SRCS nccl_op_test.cu.cc DEPS nccl_op gpu_info device_context) diff --git a/paddle/fluid/operators/delete_var_op.cc b/paddle/fluid/operators/delete_var_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..1fe9404c00335edbe3594486f8c403e69f2ab08f --- /dev/null +++ b/paddle/fluid/operators/delete_var_op.cc @@ -0,0 +1,52 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. +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/framework/op_registry.h" +#include "paddle/fluid/framework/operator.h" + +namespace paddle { +namespace operators { +class DeleteVarOp : public framework::OperatorBase { + public: + DeleteVarOp(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 { + // get device context from pool + platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance(); + auto &dev_ctx = *pool.Get(place); + dev_ctx.Wait(); + + auto delete_var_names = Inputs("X"); + const_cast(scope).EraseVars(delete_var_names); + } +}; + +class DeleteVarOpInfoMaker : public framework::OpProtoAndCheckerMaker { + public: + DeleteVarOpInfoMaker(OpProto *proto, OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", "The input of delete op").AsDuplicable(); + AddComment(R"DOC( +Delete Operator. +It should not be configured by users directly. +)DOC"); + } +}; + +} // namespace operators +} // namespace paddle + +REGISTER_OPERATOR(delete_var, paddle::operators::DeleteVarOp, + paddle::framework::EmptyGradOpMaker, + paddle::operators::DeleteVarOpInfoMaker); diff --git a/paddle/fluid/operators/detail/bytebuffer_stream.cc b/paddle/fluid/operators/detail/bytebuffer_stream.cc index a9488156e073e515926240c9bb66d7b6edf8f82e..741dd51de9e75feb608161579e56cb160b058ebb 100644 --- a/paddle/fluid/operators/detail/bytebuffer_stream.cc +++ b/paddle/fluid/operators/detail/bytebuffer_stream.cc @@ -85,4 +85,4 @@ google::protobuf::int64 GrpcByteBufferSource::ByteCount() const { } // namespace detail } // namespace operators -} // namespace paddle \ No newline at end of file +} // namespace paddle diff --git a/paddle/fluid/operators/detail/sendrecvop_utils.cc b/paddle/fluid/operators/detail/sendrecvop_utils.cc index f196fc9862d2374583d50820a6c3b63c866bf048..39117eeeb611b025c426938c60ddf82c6af232ca 100644 --- a/paddle/fluid/operators/detail/sendrecvop_utils.cc +++ b/paddle/fluid/operators/detail/sendrecvop_utils.cc @@ -82,7 +82,7 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var, DestroyCallback destroy_callback = [](void* backing) {}; void* buf = malloc(1024); - void* payload; + void* payload = nullptr; size_t payload_size; ProtoEncodeHelper e((char*)buf, 1024); e.WriteString(VarMsg::kVarnameFieldNumber, name); @@ -297,4 +297,4 @@ void DeserializeFromByteBuffer(const ::grpc::ByteBuffer& msg, } // namespace detail } // namespace operators -} // namespace paddle \ No newline at end of file +} // namespace paddle diff --git a/paddle/fluid/operators/detection_map_op.h b/paddle/fluid/operators/detection_map_op.h index a009e9dfce130bfd6c506c71b58a418a569bdf7a..8c15bfa36bfe72586cfcbdbd8efc4542253adaca 100644 --- a/paddle/fluid/operators/detection_map_op.h +++ b/paddle/fluid/operators/detection_map_op.h @@ -273,7 +273,6 @@ class DetectionMAPOpKernel : public framework::OpKernel { std::map>>& true_pos, std::map>>& false_pos, const int class_num) const { - constexpr T kEPS = static_cast(1e-6); const int* pos_count_data = input_pos_count.data(); for (int i = 0; i < class_num; ++i) { label_pos_count[i] = pos_count_data[i]; @@ -282,12 +281,11 @@ class DetectionMAPOpKernel : public framework::OpKernel { auto SetData = [](const framework::LoDTensor& pos_tensor, std::map>>& pos) { const T* pos_data = pos_tensor.data(); - auto pos_data_lod = pos_tensor.lod(); - for (size_t i = 0; i < pos_data_lod.size(); ++i) { - for (size_t j = pos_data_lod[0][i]; j < pos_data_lod[0][i + 1]; ++j) { + auto pos_data_lod = pos_tensor.lod()[0]; + for (size_t i = 0; i < pos_data_lod.size() - 1; ++i) { + for (size_t j = pos_data_lod[i]; j < pos_data_lod[i + 1]; ++j) { T score = pos_data[j * 2]; - int flag = 1; - if (pos_data[j * 2 + 1] < kEPS) flag = 0; + int flag = pos_data[j * 2 + 1]; pos[i].push_back(std::make_pair(score, flag)); } } diff --git a/paddle/fluid/operators/elementwise_add_op.cc b/paddle/fluid/operators/elementwise_add_op.cc index e9068fcd50ba9309a37939788ca8f67f1f6e25cd..4aab54f60236ecc5fa7f70e22f1553c3bfe68198 100644 --- a/paddle/fluid/operators/elementwise_add_op.cc +++ b/paddle/fluid/operators/elementwise_add_op.cc @@ -29,8 +29,11 @@ class ElementwiseAddOpMaker : public ElementwiseOpMaker { } // namespace paddle namespace ops = paddle::operators; -REGISTER_OP(elementwise_add, ops::ElementwiseOp, ops::ElementwiseAddOpMaker, - elementwise_add_grad, ops::ElementwiseOpGrad); +REGISTER_OPERATOR(elementwise_add, ops::ElementwiseOp, + ops::ElementwiseAddOpMaker, ops::ElementwiseOpInferVarType, + paddle::framework::DefaultGradOpDescMaker); +REGISTER_OPERATOR(elementwise_add_grad, ops::ElementwiseOpGrad); + REGISTER_OP_CPU_KERNEL( elementwise_add, ops::ElementwiseAddKernel, diff --git a/paddle/fluid/operators/elementwise_op.h b/paddle/fluid/operators/elementwise_op.h index fe31bbaed44fced68b7b51dd2c2031950ec4247d..f04d8d8fd82ed2336dff9c5b88808dc32de6630a 100644 --- a/paddle/fluid/operators/elementwise_op.h +++ b/paddle/fluid/operators/elementwise_op.h @@ -41,6 +41,16 @@ class ElementwiseOp : public framework::OperatorWithKernel { } }; +class ElementwiseOpInferVarType : public framework::VarTypeInference { + public: + void operator()(const framework::OpDesc& op_desc, + framework::BlockDesc* block) const override { + auto x_var = op_desc.Input("X")[0]; + auto out_var = op_desc.Output("Out")[0]; + block->Var(out_var)->SetType(block->Var(x_var)->GetType()); + } +}; + class ElementwiseOpMaker : public framework::OpProtoAndCheckerMaker { public: ElementwiseOpMaker(OpProto* proto, OpAttrChecker* op_checker) diff --git a/paddle/fluid/operators/math/math_function.cu b/paddle/fluid/operators/math/math_function.cu index 36655508be2ea9e748333171073c7dc258de52f2..3abbcdb71d03eaf6f8eba3d97150d27ac5a5405e 100644 --- a/paddle/fluid/operators/math/math_function.cu +++ b/paddle/fluid/operators/math/math_function.cu @@ -45,6 +45,9 @@ void gemm( const half* h_B = reinterpret_cast(B); half* h_C = reinterpret_cast(C); + // TODO(kexinzhao): add processing code for compute capability < 53 case + PADDLE_ENFORCE_GE(context.GetComputeCapability(), 53, + "cublas Hgemm requires GPU compute capability >= 53"); PADDLE_ENFORCE(platform::dynload::cublasHgemm( context.cublas_handle(), cuTransB, cuTransA, N, M, K, &h_alpha, h_B, ldb, h_A, lda, &h_beta, h_C, N)); @@ -106,6 +109,9 @@ void gemm( const half* h_B = reinterpret_cast(B); half* h_C = reinterpret_cast(C); + // TODO(kexinzhao): add processing code for compute capability < 53 case + PADDLE_ENFORCE_GE(context.GetComputeCapability(), 53, + "cublas Hgemm requires GPU compute capability >= 53"); PADDLE_ENFORCE(platform::dynload::cublasHgemm( context.cublas_handle(), cuTransB, cuTransA, N, M, K, &h_alpha, h_B, ldb, h_A, lda, &h_beta, h_C, ldc)); @@ -251,6 +257,9 @@ void batched_gemm( const half* h_B = reinterpret_cast(B); half* h_C = reinterpret_cast(C); + // TODO(kexinzhao): add processing code for compute capability < 53 case + PADDLE_ENFORCE_GE(context.GetComputeCapability(), 53, + "cublas Hgemm requires GPU compute capability >= 53"); PADDLE_ENFORCE(platform::dynload::cublasHgemmStridedBatched( context.cublas_handle(), cuTransB, cuTransA, N, M, K, &h_alpha, h_B, ldb, strideB, h_A, lda, strideA, &h_beta, h_C, ldc, strideC, batchCount)); diff --git a/paddle/fluid/operators/math/math_function_test.cu b/paddle/fluid/operators/math/math_function_test.cu index 442e62d563ebd40316d001914c93447c102cbf61..8982d9d066165a9da0461288685baa0c60e5f114 100644 --- a/paddle/fluid/operators/math/math_function_test.cu +++ b/paddle/fluid/operators/math/math_function_test.cu @@ -72,6 +72,11 @@ TEST(math_function, notrans_mul_trans_fp16) { CUDAPlace gpu_place(0); CUDADeviceContext context(gpu_place); + // fp16 GEMM in cublas requires GPU compute capability >= 53 + if (context.GetComputeCapability() < 53) { + return; + } + float16* input1_ptr = input1.mutable_data({2, 3}, cpu_place); fill_fp16_data(input1_ptr, input1.numel(), {0, 1, 2, 3, 4, 5}); @@ -149,6 +154,11 @@ TEST(math_function, trans_mul_notrans_fp16) { CUDAPlace gpu_place(0); CUDADeviceContext context(gpu_place); + // fp16 GEMM in cublas requires GPU compute capability >= 53 + if (context.GetComputeCapability() < 53) { + return; + } + float16* input1_ptr = input1.mutable_data({2, 3}, cpu_place); fill_fp16_data(input1_ptr, input1.numel(), {0, 1, 2, 3, 4, 5}); @@ -248,6 +258,11 @@ TEST(math_function, gemm_notrans_cublas_fp16) { CUDAPlace gpu_place(0); CUDADeviceContext context(gpu_place); + // fp16 GEMM in cublas requires GPU compute capability >= 53 + if (context.GetComputeCapability() < 53) { + return; + } + int m = 2; int n = 3; int k = 3; @@ -355,6 +370,11 @@ TEST(math_function, gemm_trans_cublas_fp16) { CUDAPlace gpu_place(0); CUDADeviceContext context(gpu_place); + // fp16 GEMM in cublas requires GPU compute capability >= 53 + if (context.GetComputeCapability() < 53) { + return; + } + int m = 2; int n = 3; int k = 3; diff --git a/paddle/fluid/operators/nccl_op.cc b/paddle/fluid/operators/nccl_op.cc index 9185666c56c4621d42429c9cfdb079001c6336f1..329656d26da0d32a4e30dd2aeecb9f7aa7f9a84d 100644 --- a/paddle/fluid/operators/nccl_op.cc +++ b/paddle/fluid/operators/nccl_op.cc @@ -14,7 +14,6 @@ limitations under the License. */ #include "paddle/fluid/framework/op_registry.h" #include "paddle/fluid/operators/nccl/nccl_gpu_common.h" -#include "paddle/fluid/operators/nccl/nccl_gpu_common.h" namespace paddle { namespace operators { diff --git a/paddle/fluid/operators/nccl_op_test.cu.cc b/paddle/fluid/operators/nccl_op_test.cu.cc index b4021a5dacd80a042042eadf9f5d1a932f0f00a2..90f6f955cea51ded2dbb2bde459113458d7749a4 100644 --- a/paddle/fluid/operators/nccl_op_test.cu.cc +++ b/paddle/fluid/operators/nccl_op_test.cu.cc @@ -14,19 +14,15 @@ limitations under the License. */ #include #include -#include #include #include #include -#include #include -#include "paddle/fluid/framework/block_desc.h" #include "paddle/fluid/framework/init.h" #include "paddle/fluid/framework/op_desc.h" #include "paddle/fluid/framework/op_registry.h" #include "paddle/fluid/framework/program_desc.h" -#include "paddle/fluid/framework/var_desc.h" #include "paddle/fluid/operators/nccl/nccl_gpu_common.h" #include "paddle/fluid/platform/device_context.h" #include "paddle/fluid/platform/enforce.h" @@ -41,26 +37,35 @@ USE_CUDA_ONLY_OP(ncclBcast); namespace f = paddle::framework; namespace p = paddle::platform; -static std::vector gpu_list; - // test data amount -const f::DDim kDims = {100, 100}; +const f::DDim kDims = {20, 20}; // nccl op common tester, init communicator. class NCCLTester : public ::testing::Test { public: virtual void SetUp() override { + int count = p::GetCUDADeviceCount(); + if (count <= 1) { + LOG(WARNING) + << "Cannot test multi-gpu nccl, because the CUDA device count is " + << count; + exit(0); + } + for (int i = 0; i < count; ++i) { + gpu_list_.emplace_back(i); + } + paddle::platform::CPUPlace cpu_place; - for (size_t i = 0; i < gpu_list.size(); ++i) { + for (size_t i = 0; i < gpu_list_.size(); ++i) { p::CUDAPlace place(i); - dev_ctxs.emplace_back(new p::CUDADeviceContext(place)); + dev_ctxs_.emplace_back(new p::CUDADeviceContext(place)); } NCCLInitOp(); } virtual void TearDown() override { - for (auto &device_context : dev_ctxs) { + for (auto &device_context : dev_ctxs_) { delete device_context; } } @@ -70,36 +75,40 @@ class NCCLTester : public ::testing::Test { std::unique_ptr op1(new f::OpDesc); op1->SetType("ncclInit"); + op1->SetInput("parallel_scopes", {"p_scopes"}); op1->SetOutput("Communicator", {"comm"}); - op1->SetAttr("gpus", {gpu_list}); - auto *var = g_scope.Var("comm"); + auto *var = g_scope_.Var("comm"); var->GetMutable(); + auto *scope_var = g_scope_.Var("p_scopes"); + auto *p_scopes = scope_var->GetMutable>(); + (*p_scopes).resize(gpu_list_.size()); + auto op = f::OpRegistry::CreateOp(*op1); VLOG(1) << "invoke NCCLInitOp."; - op->Run(g_scope, cpu_place); + op->Run(g_scope_, cpu_place); VLOG(1) << "NCCLInitOp finished."; } + int GetGPUData(int gpu_id) { return gpu_id + 42; } + template void PerThreadProgram(int gpu_id, const f::OpDesc &op_desc, f::Scope *scope) { - std::unique_lock lk(mu); + std::unique_lock lk(mu_); const f::OpDesc *op1 = &op_desc; p::CUDAPlace place(gpu_id); - auto &ctx = dev_ctxs.at(gpu_id); + auto &ctx = dev_ctxs_.at(gpu_id); auto *send_tensor = scope->Var("st")->GetMutable(); auto *recv_tensor = scope->Var("rt")->GetMutable(); if (!send_tensor->numel()) { - send_tensor->Resize(kDims); send_tensor->mutable_data(kDims, place); - std::vector send_vector(f::product(kDims), gpu_id); + std::vector send_vector(f::product(kDims), GetGPUData(gpu_id)); paddle::framework::TensorFromVector(send_vector, *ctx, send_tensor); - ctx->Wait(); VLOG(1) << "Send Tensor filled with elements " << send_tensor->numel(); } @@ -118,30 +127,14 @@ class NCCLTester : public ::testing::Test { } public: - std::vector dev_ctxs; - f::Scope g_scope; - std::mutex mu; + std::vector dev_ctxs_; + f::Scope g_scope_; + std::mutex mu_; + std::vector gpu_list_; }; // ncclInitOp with desc -TEST(NCCL, ncclInitOp) { - std::unique_ptr op_desc(new f::OpDesc); - - op_desc->SetType("ncclInit"); - op_desc->SetOutput("Communicator", {"x1"}); - op_desc->SetAttr("gpus", {gpu_list}); - - f::Scope g_scope; - paddle::platform::CPUPlace cpu_place; - - auto *var = g_scope.Var("x1"); - var->GetMutable(); - - auto op = f::OpRegistry::CreateOp(*op_desc); - VLOG(1) << "invoke NCCLInitOp."; - op->Run(g_scope, cpu_place); - VLOG(1) << "NCCLInitOp finished."; -} +TEST_F(NCCLTester, ncclInitOp) {} // ncclAllReduceOp with desc TEST_F(NCCLTester, ncclAllReduceOp) { @@ -155,23 +148,25 @@ TEST_F(NCCLTester, ncclAllReduceOp) { std::vector ths; - for (size_t i = 0; i < gpu_list.size(); ++i) { - dev_scopes.emplace_back(&g_scope.NewScope()); - std::thread th(&NCCLTester::PerThreadProgram, this, gpu_list[i], + for (size_t i = 0; i < gpu_list_.size(); ++i) { + dev_scopes.emplace_back(&g_scope_.NewScope()); + std::thread th(&NCCLTester::PerThreadProgram, this, gpu_list_[i], *op2.get(), dev_scopes[i]); ths.emplace_back(std::move(th)); } - for (size_t i = 0; i < gpu_list.size(); ++i) { + for (size_t i = 0; i < gpu_list_.size(); ++i) { ths[i].join(); } - // check results - float result = std::accumulate(gpu_list.begin(), gpu_list.end(), 0); + float expected_result = 0.0; + for (int gpu_id : gpu_list_) { + expected_result = expected_result + GetGPUData(gpu_id); + } for (size_t i = 0; i < dev_scopes.size(); ++i) { p::CPUPlace cpu_place; - p::CUDAPlace gpu_place(gpu_list[i]); + p::CUDAPlace gpu_place(gpu_list_[i]); auto &recv_tensor = dev_scopes[i]->FindVar("rt")->Get(); auto *rt = recv_tensor.data(); @@ -180,12 +175,12 @@ TEST_F(NCCLTester, ncclAllReduceOp) { auto *ct = result_tensor->mutable_data(cpu_place); paddle::memory::Copy( - cpu_place, ct, p::CUDAPlace(gpu_list[i]), rt, + cpu_place, ct, p::CUDAPlace(gpu_list_[i]), rt, recv_tensor.numel() * sizeof(float), - static_cast(dev_ctxs[i])->stream()); + static_cast(dev_ctxs_[i])->stream()); for (int64_t j = 0; j < f::product(kDims); ++j) { - ASSERT_NEAR(ct[j], result, 1e-5); + ASSERT_NEAR(ct[j], expected_result, 1e-5); } } } @@ -204,22 +199,24 @@ TEST_F(NCCLTester, ncclReduceOp) { std::vector ths; - for (size_t i = 0; i < gpu_list.size(); ++i) { - dev_scopes.emplace_back(&g_scope.NewScope()); - std::thread th(&NCCLTester::PerThreadProgram, this, gpu_list[i], + for (size_t i = 0; i < gpu_list_.size(); ++i) { + dev_scopes.emplace_back(&g_scope_.NewScope()); + std::thread th(&NCCLTester::PerThreadProgram, this, gpu_list_[i], *op2.get(), dev_scopes[i]); ths.emplace_back(std::move(th)); } - for (size_t i = 0; i < gpu_list.size(); ++i) { + for (size_t i = 0; i < gpu_list_.size(); ++i) { ths[i].join(); } - // check results on - float result = std::accumulate(gpu_list.begin(), gpu_list.end(), 0); + float expected_result = 0.0; + for (int gpu_id : gpu_list_) { + expected_result = expected_result + GetGPUData(gpu_id); + } p::CPUPlace cpu_place; - p::CUDAPlace gpu_place(gpu_list[kRoot]); + p::CUDAPlace gpu_place(gpu_list_[kRoot]); auto &recv_tensor = dev_scopes[kRoot]->FindVar("rt")->Get(); auto *rt = recv_tensor.data(); @@ -229,12 +226,12 @@ TEST_F(NCCLTester, ncclReduceOp) { auto *ct = result_tensor->mutable_data(cpu_place); paddle::memory::Copy( - cpu_place, ct, p::CUDAPlace(gpu_list[kRoot]), rt, + cpu_place, ct, p::CUDAPlace(gpu_list_[kRoot]), rt, recv_tensor.numel() * sizeof(float), - static_cast(dev_ctxs[kRoot])->stream()); + static_cast(dev_ctxs_[kRoot])->stream()); for (int64_t j = 0; j < f::product(kDims); ++j) { - ASSERT_NEAR(ct[j], result, 1e-5); + ASSERT_NEAR(ct[j], expected_result, 1e-5); } } @@ -252,23 +249,22 @@ TEST_F(NCCLTester, ncclBcastOp) { std::vector ths; - for (size_t i = 0; i < gpu_list.size(); ++i) { - dev_scopes.emplace_back(&g_scope.NewScope()); - std::thread th(&NCCLTester::PerThreadProgram, this, gpu_list[i], + for (size_t i = 0; i < gpu_list_.size(); ++i) { + dev_scopes.emplace_back(&g_scope_.NewScope()); + std::thread th(&NCCLTester::PerThreadProgram, this, gpu_list_[i], *op2.get(), dev_scopes[i]); ths.emplace_back(std::move(th)); } - for (size_t i = 0; i < gpu_list.size(); ++i) { + for (size_t i = 0; i < gpu_list_.size(); ++i) { ths[i].join(); } const int idx = 1; - // check results on - float result = kRoot; + float result = GetGPUData(kRoot); p::CPUPlace cpu_place; - p::CUDAPlace gpu_place(gpu_list[idx]); + p::CUDAPlace gpu_place(gpu_list_[idx]); auto &recv_tensor = dev_scopes[idx]->FindVar("rt")->Get(); auto *rt = recv_tensor.data(); @@ -277,42 +273,11 @@ TEST_F(NCCLTester, ncclBcastOp) { auto *ct = result_tensor->mutable_data(cpu_place); paddle::memory::Copy( - cpu_place, ct, p::CUDAPlace(gpu_list[idx]), rt, + cpu_place, ct, p::CUDAPlace(gpu_list_[idx]), rt, recv_tensor.numel() * sizeof(float), - static_cast(dev_ctxs[idx])->stream()); + static_cast(dev_ctxs_[idx])->stream()); for (int64_t j = 0; j < f::product(kDims); ++j) { ASSERT_NEAR(ct[j], result, 1e-5); } } - -int main(int argc, char **argv) { - // FIXME(tonyyang-svail): - // Due to the driver issue on our CI, disable for now - return 0; - const int dev_count = p::GetCUDADeviceCount(); - if (dev_count <= 1) { - LOG(WARNING) - << "Cannot test multi-gpu nccl, because the CUDA device count is " - << dev_count; - return 0; - } - - std::vector places; - - places.emplace_back(paddle::platform::CPUPlace()); - int count = paddle::platform::GetCUDADeviceCount(); - for (int i = 0; i < count; ++i) { - places.emplace_back(paddle::platform::CUDAPlace(i)); - gpu_list.emplace_back(i); - } - - VLOG(0) << " DeviceCount " << count; - paddle::platform::DeviceContextPool::Init(places); - - testing::InitGoogleTest(&argc, argv); - - // device context should be release before scope. - // otherwise driver will down. - return RUN_ALL_TESTS(); -} diff --git a/paddle/fluid/operators/prior_box_op.cc b/paddle/fluid/operators/prior_box_op.cc index be7898c22190339e0717317807b91e038f4949f6..7ba55437cb20f802cc12ceea7777d7d78bba62a6 100644 --- a/paddle/fluid/operators/prior_box_op.cc +++ b/paddle/fluid/operators/prior_box_op.cc @@ -111,7 +111,8 @@ class PriorBoxOpMaker : public framework::OpProtoAndCheckerMaker { }); AddAttr>( "max_sizes", - "(vector) List of max sizes of generated prior boxes."); + "(vector) List of max sizes of generated prior boxes.") + .SetDefault(std::vector{}); AddAttr>( "aspect_ratios", "(vector) List of aspect ratios of generated prior boxes."); diff --git a/paddle/fluid/operators/prior_box_op.h b/paddle/fluid/operators/prior_box_op.h index 0113d2f09af90eec32632180741db3a1fc40c724..18bb2deb6b5acf626dfb2883a5771d9d195d45c0 100644 --- a/paddle/fluid/operators/prior_box_op.h +++ b/paddle/fluid/operators/prior_box_op.h @@ -97,9 +97,6 @@ class PriorBoxOpKernel : public framework::OpKernel { boxes->mutable_data(ctx.GetPlace()); vars->mutable_data(ctx.GetPlace()); - T inv_img_width = 1.0 / img_width; - T inv_img_height = 1.0 / img_height; - auto e_boxes = framework::EigenTensor::From(*boxes); for (int h = 0; h < feature_height; ++h) { for (int w = 0; w < feature_width; ++w) { @@ -110,36 +107,30 @@ class PriorBoxOpKernel : public framework::OpKernel { for (size_t s = 0; s < min_sizes.size(); ++s) { auto min_size = min_sizes[s]; // first prior: aspect_ratio = 1, size = min_size - box_width = box_height = min_size; + box_width = box_height = min_size / 2.; // xmin - e_boxes(h, w, idx, 0) = (center_x - box_width * 0.5) * inv_img_width; + e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width; // ymin - e_boxes(h, w, idx, 1) = - (center_y - box_height * 0.5) * inv_img_height; + e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height; // xmax - e_boxes(h, w, idx, 2) = (center_x + box_width * 0.5) * inv_img_width; + e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width; // ymax - e_boxes(h, w, idx, 3) = - (center_y + box_height * 0.5) * inv_img_height; + e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height; idx++; if (max_sizes.size() > 0) { auto max_size = max_sizes[s]; // second prior: aspect_ratio = 1, // size = sqrt(min_size * max_size) - box_width = box_height = sqrt(min_size * max_size); + box_width = box_height = sqrt(min_size * max_size) / 2.; // xmin - e_boxes(h, w, idx, 0) = - (center_x - box_width * 0.5) * inv_img_width; + e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width; // ymin - e_boxes(h, w, idx, 1) = - (center_y - box_height * 0.5) * inv_img_height; + e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height; // xmax - e_boxes(h, w, idx, 2) = - (center_x + box_width * 0.5) * inv_img_width; + e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width; // ymax - e_boxes(h, w, idx, 3) = - (center_y + box_height * 0.5) * inv_img_height; + e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height; idx++; } @@ -149,20 +140,16 @@ class PriorBoxOpKernel : public framework::OpKernel { if (fabs(ar - 1.) < 1e-6) { continue; } - box_width = min_size * sqrt(ar); - box_height = min_size / sqrt(ar); + box_width = min_size * sqrt(ar) / 2.; + box_height = min_size / sqrt(ar) / 2.; // xmin - e_boxes(h, w, idx, 0) = - (center_x - box_width * 0.5) * inv_img_width; + e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width; // ymin - e_boxes(h, w, idx, 1) = - (center_y - box_height * 0.5) * inv_img_height; + e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height; // xmax - e_boxes(h, w, idx, 2) = - (center_x + box_width * 0.5) * inv_img_width; + e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width; // ymax - e_boxes(h, w, idx, 3) = - (center_y + box_height * 0.5) * inv_img_height; + e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height; idx++; } } diff --git a/paddle/fluid/operators/reader/reader_op_registry.cc b/paddle/fluid/operators/reader/reader_op_registry.cc index 7ea4f4b8d9feecac5bc2d0338bbbe9ab7a532040..f80769d7cd2d35261cd55fc1d6c8c20197f5e88c 100644 --- a/paddle/fluid/operators/reader/reader_op_registry.cc +++ b/paddle/fluid/operators/reader/reader_op_registry.cc @@ -49,6 +49,10 @@ FileReaderMakerBase::FileReaderMakerBase( } void FileReaderInferShape::operator()(framework::InferShapeContext* ctx) const { + PADDLE_ENFORCE( + !ctx->IsRuntime(), + "'FileReaderInferShape' should only be invoked during compile time."); + PADDLE_ENFORCE(ctx->HasOutput("Out"), "The output file reader should not be null."); const auto shape_concat = ctx->Attrs().Get>("shape_concat"); @@ -56,16 +60,14 @@ void FileReaderInferShape::operator()(framework::InferShapeContext* ctx) const { std::vector shapes = RestoreShapes(shape_concat, ranks); ctx->SetReaderDims("Out", shapes); - if (ctx->IsRuntime()) { - const auto lod_levels = ctx->Attrs().Get>("lod_levels"); - PADDLE_ENFORCE_EQ(lod_levels.size(), shapes.size(), - "The number of 'lod_levels'(%d) doesn't match the number " - "of 'shapes'(%d).", - lod_levels.size(), shapes.size()); - framework::VarDesc* reader = - boost::get(ctx->GetOutputVarPtrs("Out")[0]); - reader->SetLoDLevels(lod_levels); - } + const auto lod_levels = ctx->Attrs().Get>("lod_levels"); + PADDLE_ENFORCE_EQ(lod_levels.size(), shapes.size(), + "The number of 'lod_levels'(%d) doesn't match the number " + "of 'shapes'(%d).", + lod_levels.size(), shapes.size()); + framework::VarDesc* reader = + boost::get(ctx->GetOutputVarPtrs("Out")[0]); + reader->SetLoDLevels(lod_levels); } void FileReaderInferVarType::operator()(const framework::OpDesc& op_desc, @@ -77,19 +79,21 @@ void FileReaderInferVarType::operator()(const framework::OpDesc& op_desc, void DecoratedReaderInferShape::operator()( framework::InferShapeContext* ctx) const { + PADDLE_ENFORCE(!ctx->IsRuntime(), + "'DecoratedReaderInferShape' should only be invoked during " + "compile time."); + PADDLE_ENFORCE(ctx->HasInput("UnderlyingReader"), "Input(UnderlyingReader) should not be null."); PADDLE_ENFORCE(ctx->HasOutput("Out"), "The output decorated reader should not be null."); ctx->SetReaderDims("Out", ctx->GetReaderDims("UnderlyingReader")); - if (ctx->IsRuntime()) { - framework::VarDesc* in_reader = boost::get( - ctx->GetInputVarPtrs("UnderlyingReader")[0]); - framework::VarDesc* out_reader = - boost::get(ctx->GetOutputVarPtrs("Out")[0]); - out_reader->SetLoDLevels(in_reader->GetLoDLevels()); - } + framework::VarDesc* in_reader = boost::get( + ctx->GetInputVarPtrs("UnderlyingReader")[0]); + framework::VarDesc* out_reader = + boost::get(ctx->GetOutputVarPtrs("Out")[0]); + out_reader->SetLoDLevels(in_reader->GetLoDLevels()); } void DecoratedReaderInferVarType::operator()( const framework::OpDesc& op_desc, framework::BlockDesc* block) const { diff --git a/paddle/fluid/platform/CMakeLists.txt b/paddle/fluid/platform/CMakeLists.txt index a1a743d94e204ca506c994f8fabb6bbf8c22cea5..7eec6ab657723c6390dfa14a78d6c49a76f2a279 100644 --- a/paddle/fluid/platform/CMakeLists.txt +++ b/paddle/fluid/platform/CMakeLists.txt @@ -48,7 +48,6 @@ nv_test(device_context_test SRCS device_context_test.cu DEPS device_context gpu_ nv_test(cudnn_helper_test SRCS cudnn_helper_test.cc DEPS dynload_cuda) nv_test(transform_test SRCS transform_test.cu DEPS paddle_memory place device_context) -nv_test(nccl_test SRCS nccl_test.cu DEPS dynload_cuda gpu_info device_context) cc_library(device_tracer SRCS device_tracer.cc DEPS profiler_proto ${GPU_CTX_DEPS}) cc_library(profiler SRCS profiler.cc DEPS device_context device_tracer) diff --git a/paddle/fluid/platform/device_context.cc b/paddle/fluid/platform/device_context.cc index bb9fbd468f38fffc94107e321e777fc0e772fbe6..98b4178177b0a8bafd6fe34a92be2a07a2fbc5a7 100644 --- a/paddle/fluid/platform/device_context.cc +++ b/paddle/fluid/platform/device_context.cc @@ -127,6 +127,7 @@ class EigenCudaStreamDevice : public Eigen::StreamInterface { CUDADeviceContext::CUDADeviceContext(CUDAPlace place) : place_(place) { SetDeviceId(place_.device); + compute_capability = GetCUDAComputeCapability(place_.device); multi_process = GetCUDAMultiProcessors(place_.device); max_threads_per_mp = GetCUDAMaxThreadsPerMultiProcessor(place_.device); PADDLE_ENFORCE(cudaStreamCreate(&stream_)); @@ -162,6 +163,10 @@ void CUDADeviceContext::Wait() const { PADDLE_ENFORCE(cudaGetLastError()); } +int CUDADeviceContext::GetComputeCapability() const { + return compute_capability; +} + int CUDADeviceContext::GetMaxPhysicalThreadCount() const { return multi_process * max_threads_per_mp; } diff --git a/paddle/fluid/platform/device_context.h b/paddle/fluid/platform/device_context.h index e779644190de1246cd650fbf91eeaeb03494643f..603b890af13b529c490c29112a73a09cc815d07a 100644 --- a/paddle/fluid/platform/device_context.h +++ b/paddle/fluid/platform/device_context.h @@ -79,6 +79,9 @@ class CUDADeviceContext : public DeviceContext { /*! \brief Return place in the device context. */ Place GetPlace() const override; + /*! \brief Return compute capability in the device context. */ + int GetComputeCapability() const; + /*! \brief Return the max physical thread count in the device context */ int GetMaxPhysicalThreadCount() const; @@ -104,6 +107,7 @@ class CUDADeviceContext : public DeviceContext { cudnnHandle_t cudnn_handle_; cublasHandle_t cublas_handle_; + int compute_capability; int multi_process; int max_threads_per_mp; }; diff --git a/paddle/fluid/platform/gpu_info.cc b/paddle/fluid/platform/gpu_info.cc index da4041bad0d82fe1c8c7a12fd0c7177e6dbddef3..dd70ff9ff574b32bc96a9e8255b1bf77a5cc84e4 100644 --- a/paddle/fluid/platform/gpu_info.cc +++ b/paddle/fluid/platform/gpu_info.cc @@ -33,6 +33,15 @@ int GetCUDADeviceCount() { return count; } +int GetCUDAComputeCapability(int id) { + PADDLE_ENFORCE_LT(id, GetCUDADeviceCount(), "id must less than GPU count"); + cudaDeviceProp device_prop; + PADDLE_ENFORCE(cudaGetDeviceProperties(&device_prop, id), + "cudaGetDeviceProperties failed in " + "paddle::platform::GetCUDAComputeCapability"); + return device_prop.major * 10 + device_prop.minor; +} + int GetCUDAMultiProcessors(int id) { PADDLE_ENFORCE_LT(id, GetCUDADeviceCount(), "id must less than GPU count"); int count; diff --git a/paddle/fluid/platform/gpu_info.h b/paddle/fluid/platform/gpu_info.h index c38ccf0f2ade1d2405177b541b33fd84283726ff..fa469fa77f5ca780da153cc87da8d04f239711f3 100644 --- a/paddle/fluid/platform/gpu_info.h +++ b/paddle/fluid/platform/gpu_info.h @@ -30,6 +30,9 @@ const std::string kEnvFractionGpuMemoryToUse = //! Get the total number of GPU devices in system. int GetCUDADeviceCount(); +//! Get the compute capability of the ith GPU (format: major * 10 + minor) +int GetCUDAComputeCapability(int i); + //! Get the MultiProcessors of the ith GPU. int GetCUDAMultiProcessors(int i); diff --git a/paddle/fluid/platform/nccl_test.cu b/paddle/fluid/platform/nccl_test.cu deleted file mode 100644 index 32a293796c09e5254c5eb48d11fa74617b3465ac..0000000000000000000000000000000000000000 --- a/paddle/fluid/platform/nccl_test.cu +++ /dev/null @@ -1,153 +0,0 @@ -/* 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 -#include -#include - -#include "glog/logging.h" -#include "gtest/gtest.h" - -#include "paddle/fluid/framework/init.h" -#include "paddle/fluid/platform/device_context.h" -#include "paddle/fluid/platform/dynload/nccl.h" -#include "paddle/fluid/platform/enforce.h" -#include "paddle/fluid/platform/gpu_info.h" - -static int dev_count = 0; - -namespace paddle { -namespace platform { - -TEST(NCCL, init) { - std::vector comms; - comms.resize(dev_count); - PADDLE_ENFORCE(dynload::ncclCommInitAll(comms.data(), dev_count, nullptr)); - - for (int i = 0; i < dev_count; ++i) { - dynload::ncclCommDestroy(comms[i]); - } -} - -template -struct PerThreadData { - thrust::device_vector send_buff; - thrust::device_vector recv_buff; - CUDADeviceContext dev_ctx; - - T* SendBuff() { return thrust::raw_pointer_cast(send_buff.data()); } - - T* RecvBuff() { return thrust::raw_pointer_cast(recv_buff.data()); } - - PerThreadData(int gpu_id, size_t size) : dev_ctx(CUDAPlace(gpu_id)) { - send_buff.resize(size); - for (size_t i = 0; i < size; ++i) { - send_buff[i] = static_cast(i); - } - recv_buff.resize(size); - } -}; - -static constexpr int ELEM_COUNT = 10000; - -TEST(NCCL, all_reduce) { - std::vector comms; - comms.resize(dev_count); - VLOG(1) << "Initializing ncclComm"; - dynload::ncclCommInitAll(comms.data(), dev_count, nullptr); - VLOG(1) << "ncclComm initialized"; - VLOG(1) << "Creating thread data"; - std::vector>> data; - data.reserve(dev_count); - for (int i = 0; i < dev_count; ++i) { - VLOG(1) << "Creating thread data for device " << i; - SetDeviceId(i); - data.emplace_back(new PerThreadData(i, ELEM_COUNT)); - } - VLOG(1) << "Thread data created"; - - VLOG(1) << "Check send_buf data"; - for (int i = 0; i < dev_count; ++i) { - VLOG(1) << "Check on device " << i; - SetDeviceId(i); - thrust::host_vector tmp = data[i]->send_buff; - for (size_t j = 0; j < tmp.size(); ++j) { - ASSERT_NEAR(static_cast(j), tmp[j], 1e-5); - } - } - - VLOG(1) << "Invoking ncclAllReduce"; - - dynload::ncclGroupStart(); - for (int i = 0; i < dev_count; ++i) { - VLOG(1) << "Invoking ncclAllReduce with device " << i; - SetDeviceId(i); - PADDLE_ENFORCE(dynload::ncclAllReduce( - data[i]->SendBuff(), data[i]->RecvBuff(), ELEM_COUNT, ncclDouble, - ncclSum, comms[i], data[i]->dev_ctx.stream())); - VLOG(1) << "Invoked ncclAllReduce for device " << i; - } - dynload::ncclGroupEnd(); - - VLOG(1) << "Invoked ncclAllReduce"; - - VLOG(1) << "Sync devices"; - for (int i = 0; i < dev_count; ++i) { - VLOG(1) << "Sync device " << i; - SetDeviceId(i); - data[i]->dev_ctx.Wait(); - } - VLOG(1) << "device synced"; - - for (int i = 0; i < dev_count; ++i) { - SetDeviceId(i); - VLOG(1) << "Checking vector on device " << i; - thrust::host_vector tmp = data[i]->recv_buff; - for (size_t j = 0; j < tmp.size(); ++j) { - auto elem = static_cast(j); - elem *= dev_count; - ASSERT_NEAR(tmp[j], elem, 1e-4); - } - } - - for (int i = 0; i < dev_count; ++i) { - dynload::ncclCommDestroy(comms[i]); - } -} -} // namespace platform -} // namespace paddle - -int main(int argc, char** argv) { - dev_count = paddle::platform::GetCUDADeviceCount(); - if (dev_count <= 1) { - LOG(WARNING) - << "Cannot test multi-gpu nccl, because the CUDA device count is " - << dev_count; - return 0; - } - - std::vector places; - - places.emplace_back(paddle::platform::CPUPlace()); - int count = paddle::platform::GetCUDADeviceCount(); - for (int i = 0; i < count; ++i) { - places.emplace_back(paddle::platform::CUDAPlace(i)); - } - - VLOG(0) << " DeviceCount " << count; - paddle::platform::DeviceContextPool::Init(places); - - testing::InitGoogleTest(&argc, argv); - return RUN_ALL_TESTS(); -} diff --git a/paddle/fluid/pybind/protobuf.cc b/paddle/fluid/pybind/protobuf.cc index b0a2497d919b65afbe5eeaf4fe47c19baa1aba1c..45a64f43846e79c27295e52c59dca6bdfaa120a3 100644 --- a/paddle/fluid/pybind/protobuf.cc +++ b/paddle/fluid/pybind/protobuf.cc @@ -161,6 +161,8 @@ void BindBlockDesc(py::module &m) { py::return_value_policy::reference) .def("prepend_op", &BlockDesc::PrependOp, py::return_value_policy::reference) + .def("insert_op", &BlockDesc::InsertOp, + py::return_value_policy::reference) .def("remove_op", &BlockDesc::RemoveOp) .def("var", [](BlockDesc &self, py::bytes byte_name) { diff --git a/python/paddle/fluid/__init__.py b/python/paddle/fluid/__init__.py index 0df3fd0343dbdaee88192a402d990fdfc2235811..84a57aff516ad2d7ba1efaf1d530e77747d3b254 100644 --- a/python/paddle/fluid/__init__.py +++ b/python/paddle/fluid/__init__.py @@ -37,7 +37,7 @@ from distribute_transpiler_simple import SimpleDistributeTranspiler from concurrency import (Go, make_channel, channel_send, channel_recv, channel_close) import clip -from memory_optimization_transpiler import memory_optimize +from memory_optimization_transpiler import memory_optimize, release_memory import profiler import unique_name @@ -63,6 +63,7 @@ __all__ = framework.__all__ + executor.__all__ + concurrency.__all__ + [ 'SimpleDistributeTranspiler', 'DistributeTranspiler', 'memory_optimize', + 'release_memory', 'profiler', 'unique_name', ] diff --git a/python/paddle/fluid/backward.py b/python/paddle/fluid/backward.py index 09d137c9017bb4185308af94287fe0e8aa005505..b6f20daee3a585777a23255355f0a0e31328d23f 100644 --- a/python/paddle/fluid/backward.py +++ b/python/paddle/fluid/backward.py @@ -457,7 +457,8 @@ def append_backward(loss, parameter_list=None, no_grad_set=None, "Out": [_append_grad_suffix_(loss.name)] }, {"shape": [1], "value": 1.0, - "dtype": loss.dtype}) + "dtype": loss.dtype, + "force_cpu": False}) root_block.desc.append_op().copy_from(op_desc) block_no_grad_set = set(map(_strip_grad_suffix_, no_grad_dict[0])) diff --git a/python/paddle/fluid/layers/detection.py b/python/paddle/fluid/layers/detection.py index 2bf7cf21ca94c742a15d980194b896d9ec8ad91b..ea189749bc6cc1e37c1efc6fea424143b887cecd 100644 --- a/python/paddle/fluid/layers/detection.py +++ b/python/paddle/fluid/layers/detection.py @@ -130,8 +130,13 @@ def detection_output(loc, target_box=loc, code_type='decode_center_size') - nmsed_outs = helper.create_tmp_variable(dtype=decoded_box.dtype) + old_shape = scores.shape + scores = ops.reshape(x=scores, shape=(-1, old_shape[-1])) + scores = ops.softmax(x=scores) + scores = ops.reshape(x=scores, shape=old_shape) scores = nn.transpose(scores, perm=[0, 2, 1]) + + nmsed_outs = helper.create_tmp_variable(dtype=decoded_box.dtype) helper.append_op( type="multiclass_nms", inputs={'Scores': scores, @@ -562,16 +567,16 @@ def multi_box_head(inputs, base_size, num_classes, aspect_ratios, - min_ratio, - max_ratio, + min_ratio=None, + max_ratio=None, min_sizes=None, max_sizes=None, steps=None, step_w=None, step_h=None, offset=0.5, - variance=[0.1, 0.1, 0.1, 0.1], - flip=False, + variance=[0.1, 0.1, 0.2, 0.2], + flip=True, clip=False, kernel_size=1, pad=0, @@ -614,7 +619,7 @@ def multi_box_head(inputs, the inputs[i] will be automatically calculated. Default: None. offset(float): Prior boxes center offset. Default: 0.5 variance(list|tuple): the variances to be encoded in prior boxes. - Default:[0.1, 0.1, 0.1, 0.1]. + Default:[0.1, 0.1, 0.2, 0.2]. flip(bool): Whether to flip aspect ratios. Default:False. clip(bool): Whether to clip out-of-boundary boxes. Default: False. kernel_size(int): The kernel size of conv2d. Default: 1. @@ -668,6 +673,19 @@ def multi_box_head(inputs, helper = LayerHelper("prior_box", **locals()) dtype = helper.input_dtype() + attrs = { + 'min_sizes': min_sizes, + 'aspect_ratios': aspect_ratios, + 'variances': variance, + 'flip': flip, + 'clip': clip, + 'step_w': step_w, + 'step_h': step_h, + 'offset': offset + } + if len(max_sizes) > 0 and max_sizes[0] > 0: + attrs['max_sizes'] = max_sizes + box = helper.create_tmp_variable(dtype) var = helper.create_tmp_variable(dtype) helper.append_op( @@ -676,17 +694,7 @@ def multi_box_head(inputs, "Image": image}, outputs={"Boxes": box, "Variances": var}, - attrs={ - 'min_sizes': min_sizes, - 'max_sizes': max_sizes, - 'aspect_ratios': aspect_ratios, - 'variances': variance, - 'flip': flip, - 'clip': clip, - 'step_w': step_w, - 'step_h': step_h, - 'offset': offset - }) + attrs=attrs, ) return box, var def _reshape_with_axis_(input, axis=1): @@ -714,7 +722,7 @@ def multi_box_head(inputs, if num_layer <= 2: assert min_sizes is not None and max_sizes is not None assert len(min_sizes) == num_layer and len(max_sizes) == num_layer - else: + elif min_sizes is None and max_sizes is None: min_sizes = [] max_sizes = [] step = int(math.floor(((max_ratio - min_ratio)) / (num_layer - 2))) @@ -759,9 +767,6 @@ def multi_box_head(inputs, min_size = [min_size] if not _is_list_or_tuple_(max_size): max_size = [max_size] - if not (len(max_size) == len(min_size)): - raise ValueError( - 'the length of max_size and min_size should be equal.') aspect_ratio = [] if aspect_ratios is not None: @@ -779,7 +784,7 @@ def multi_box_head(inputs, num_boxes = box.shape[2] - # get box_loc + # get loc num_loc_output = num_boxes * 4 mbox_loc = nn.conv2d( input=input, @@ -796,7 +801,7 @@ def multi_box_head(inputs, mbox_loc_flatten = ops.reshape(mbox_loc, shape=new_shape) mbox_locs.append(mbox_loc_flatten) - # get conf_loc + # get conf num_conf_output = num_boxes * num_classes conf_loc = nn.conv2d( input=input, diff --git a/python/paddle/fluid/memory_optimization_transpiler.py b/python/paddle/fluid/memory_optimization_transpiler.py index 4fa2d03ef563b98b2eec576bf87d4b2e54ca0a36..41d1eca82e8b680977f44f1756c25c37340668a4 100644 --- a/python/paddle/fluid/memory_optimization_transpiler.py +++ b/python/paddle/fluid/memory_optimization_transpiler.py @@ -29,7 +29,10 @@ dtype_to_size = { core.VarDesc.VarType.BOOL: 1 } -sub_block_ops = ["while", "while_grad", "parallel_do", "parallel_do_grad"] +sub_block_ops = [ + "while", "while_grad", "parallel_do", "parallel_do_grad", + "conditional_block", "conditional_block_grad" +] PRINT_LOG = False @@ -122,36 +125,80 @@ class ControlFlowGraph(object): else: return block_desc.find_var_recursive(str(var_name)) - def memory_optimize(self): - def check_var_validity(block_desc, x, is_forward): - if str(x) == "@EMPTY@": - return False - if not self._has_var(block_desc, x, is_forward): - return False - if self._find_var(block_desc, x, is_forward).persistable(): - return False - if self._find_var( - block_desc, x, - is_forward).type() != core.VarDesc.VarType.LOD_TENSOR: - return False - if x in self._skip_opt: - return False - if not self._find_var(block_desc, x, is_forward).shape(): - return False - return True + def _check_var_validity(self, block_desc, x, is_forward): + if str(x) == "@EMPTY@": + return False + if not self._has_var(block_desc, x, is_forward): + return False + if self._find_var(block_desc, x, is_forward).persistable(): + return False + if self._find_var(block_desc, x, + is_forward).type() != core.VarDesc.VarType.LOD_TENSOR: + return False + if x in self._skip_opt: + return False + if not self._find_var(block_desc, x, is_forward).shape(): + return False + return True - self._build_graph() + def _update_skip_opt_set(self): + for i in range(self.op_size): + op = self._ops[i] + if op.type() == "fill_constant" and op.attr("force_cpu") == True: + self._skip_opt.update(op.output_arg_names()) + + def release_memory(self): self._dataflow_analyze() + self._update_skip_opt_set() + fwd_id = 0 + bwd_id = 0 + for i in range(self.op_size): + op = self._ops[i] + if op.type() in sub_block_ops: + continue + block_desc = op.block() + is_forward = i < self._forward_num + in_diff, out_diff = self._get_diff(self._live_in[i], + self._live_out[i]) + can_optimize = filter( + lambda x: self._check_var_validity(block_desc, x, is_forward), + in_diff) + if can_optimize: + index = i + fwd_id + 1 if is_forward else i - self._forward_num + bwd_id + 1 + delete_op = block_desc.insert_op(index) + delete_op.set_type("delete_var") + delete_op.set_input("X", can_optimize) + if is_forward: + fwd_id += 1 + else: + bwd_id += 1 + + def memory_optimize(self, level=0): + def compare_shape(x_shape, cache_shape, opt_level): + if opt_level == 0: + return x_shape == cache_shape + if opt_level == 1: + if (x_shape[0] == -1) ^ (cache_shape[0] == -1): + return False + x_size = abs(reduce(lambda x, y: x * y, x_shape)) + cache_size = abs(reduce(lambda x, y: x * y, cache_shape)) + if x_size <= cache_size: + return True + return False + + self._dataflow_analyze() + self._update_skip_opt_set() self.pool = [] for i in range(self.op_size): op = self._ops[i] if op.type() in sub_block_ops: continue block_desc = op.block() + self.current_block_desc = block_desc is_forward = i < self._forward_num if self.pool: defs_can_optimize = filter( - lambda x: check_var_validity(block_desc, x, is_forward), + lambda x: self._check_var_validity(block_desc, x, is_forward), self._defs[i]) out_pair = [ (x, self._find_var(block_desc, x, is_forward).shape()) @@ -164,7 +211,7 @@ class ControlFlowGraph(object): for index, cache_pair in enumerate(self.pool): cache_var = cache_pair[0] cache_shape = cache_pair[1] - if x_shape == cache_shape: + if compare_shape(x_shape, cache_shape, level): if self._has_var(block_desc, cache_var, is_forward): x_dtype = self._find_var(block_desc, x, is_forward).dtype() @@ -196,7 +243,7 @@ class ControlFlowGraph(object): in_diff, out_diff = self._get_diff(self._live_in[i], self._live_out[i]) can_optimize = filter( - lambda x: check_var_validity(block_desc, x, is_forward), + lambda x: self._check_var_validity(block_desc, x, is_forward), in_diff) if can_optimize: for var_name in can_optimize: @@ -270,7 +317,8 @@ def _get_cfgs(input_program): ([block_desc.op(i) for i in range(op_size)], op_size, set())) sub_block_pair = [("while", "while_grad"), ("parallel_do", - "parallel_do_grad")] + "parallel_do_grad"), + ("conditional_block", "conditional_block_grad")] ops_list.extend(_process_sub_block_pair(pdesc, sub_block_pair)) @@ -281,9 +329,15 @@ def _get_cfgs(input_program): return cfgs -def memory_optimize(input_program, print_log=False): +def memory_optimize(input_program, print_log=False, level=0): global PRINT_LOG PRINT_LOG = print_log cfgs = _get_cfgs(input_program) for cfg in cfgs: - cfg.memory_optimize() + cfg.memory_optimize(level) + + +def release_memory(input_program): + cfgs = _get_cfgs(input_program) + for cfg in cfgs: + cfg.release_memory() diff --git a/python/paddle/fluid/optimizer.py b/python/paddle/fluid/optimizer.py index 1c12d53e4f352b7e4dea980301973a69665e49f9..421963a2f9120dae3a72142681f0a30232c11166 100644 --- a/python/paddle/fluid/optimizer.py +++ b/python/paddle/fluid/optimizer.py @@ -92,7 +92,10 @@ class Optimizer(object): # create learning rate variable for every parameter param = param_and_grad[0] param_lr = param.optimize_attr['learning_rate'] - return self.global_learning_rate() * param_lr + if param_lr == 1.0: + return self.global_learning_rate() + else: + return self.global_learning_rate() * param_lr def _create_accumulators(self, block, parameters): """Create all accumulators needed by the parameters diff --git a/python/paddle/fluid/tests/book_memory_optimization/test_memopt_fit_a_line.py b/python/paddle/fluid/tests/book_memory_optimization/test_memopt_fit_a_line.py index c9d2a5ecaab0669f308b5b9c5cf74d0212fa462a..ad79e96b958b36a06c8a3cc990dbe3608e32c9ac 100644 --- a/python/paddle/fluid/tests/book_memory_optimization/test_memopt_fit_a_line.py +++ b/python/paddle/fluid/tests/book_memory_optimization/test_memopt_fit_a_line.py @@ -50,6 +50,7 @@ sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.01) sgd_optimizer.minimize(avg_cost) fluid.memory_optimize(fluid.default_main_program(), print_log=True) +# fluid.release_memory(fluid.default_main_program()) BATCH_SIZE = 200 @@ -69,8 +70,6 @@ exe.run(fluid.default_startup_program()) PASS_NUM = 100 for pass_id in range(PASS_NUM): - fluid.io.save_persistables(exe, "./fit_a_line.model/") - fluid.io.load_persistables(exe, "./fit_a_line.model/") for data in train_reader(): avg_loss_value, = exe.run(fluid.default_main_program(), feed=feeder.feed(data), diff --git a/python/paddle/fluid/tests/book_memory_optimization/test_memopt_image_classification_train.py b/python/paddle/fluid/tests/book_memory_optimization/test_memopt_image_classification_train.py index 80ff11f8d78b0a22fc6aefd722c9e6a2c23fbd5c..204669d7e6176e9e8250e8aebc2d10441fa24b67 100644 --- a/python/paddle/fluid/tests/book_memory_optimization/test_memopt_image_classification_train.py +++ b/python/paddle/fluid/tests/book_memory_optimization/test_memopt_image_classification_train.py @@ -125,9 +125,10 @@ opts = optimizer.minimize(avg_cost) batch_size = fluid.layers.create_tensor(dtype='int64') batch_acc = fluid.layers.accuracy(input=predict, label=label, total=batch_size) -fluid.memory_optimize(fluid.default_main_program()) +# fluid.memory_optimize(fluid.default_main_program(), level=0) +fluid.release_memory(fluid.default_main_program()) -BATCH_SIZE = 128 +BATCH_SIZE = 16 PASS_NUM = 1 # fix the order of training data @@ -159,8 +160,7 @@ for pass_id in range(PASS_NUM): print("loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" + str( pass_acc)) # this model is slow, so if we can train two mini batch, we think it works properly. - - if i > 2: + if i > 0: exit(0) if math.isnan(float(loss)): sys.exit("got NaN loss, training failed.") diff --git a/python/paddle/fluid/tests/book_memory_optimization/test_memopt_machine_translation.py b/python/paddle/fluid/tests/book_memory_optimization/test_memopt_machine_translation.py index 689a75afc7ccdf84142f5531a438e1f9af7af4ca..a24834a6f0b19d1265f6c8d7089d31583af82d1f 100644 --- a/python/paddle/fluid/tests/book_memory_optimization/test_memopt_machine_translation.py +++ b/python/paddle/fluid/tests/book_memory_optimization/test_memopt_machine_translation.py @@ -105,7 +105,8 @@ def main(): optimizer = fluid.optimizer.Adagrad(learning_rate=1e-4) optimizer.minimize(avg_cost) - fluid.memory_optimize(fluid.default_main_program()) + # fluid.memory_optimize(fluid.default_main_program()) + fluid.release_memory(fluid.default_main_program()) # fix the order of training data train_data = paddle.batch( diff --git a/python/paddle/fluid/tests/unittests/test_detection_map_op.py b/python/paddle/fluid/tests/unittests/test_detection_map_op.py index f3197a623efb1730d27467c5650bc90f2762e7b2..a905a854ad157ffa3d7816dfbd445f3e344a1249 100644 --- a/python/paddle/fluid/tests/unittests/test_detection_map_op.py +++ b/python/paddle/fluid/tests/unittests/test_detection_map_op.py @@ -166,8 +166,6 @@ class TestDetectionMAPOp(OpTest): elif not difficult: label_count[label] += 1 - true_pos = collections.defaultdict(list) - false_pos = collections.defaultdict(list) for (label, score, tp, fp) in tf_pos: true_pos[label].append([score, tp]) false_pos[label].append([score, fp]) diff --git a/python/paddle/fluid/tests/unittests/test_learning_rate_scheduler.py b/python/paddle/fluid/tests/unittests/test_learning_rate_scheduler.py index 00a6f7c237d58458ea083abf47dd09585cd6f235..6382e290eb30c621da64d5c600be6d8a7c6254f1 100644 --- a/python/paddle/fluid/tests/unittests/test_learning_rate_scheduler.py +++ b/python/paddle/fluid/tests/unittests/test_learning_rate_scheduler.py @@ -98,6 +98,9 @@ class TestLearningRateDecay(unittest.TestCase): exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) + + fluid.memory_optimize(fluid.default_main_program()) + for step in range(10): lr_val, = exe.run(fluid.default_main_program(), feed={}, diff --git a/python/paddle/fluid/tests/unittests/test_optimizer.py b/python/paddle/fluid/tests/unittests/test_optimizer.py index 9d87f4daa98da42fcc33aba4b51a4528343fb137..e775db1d10f4561b6fb90631757a25c9f74cb777 100644 --- a/python/paddle/fluid/tests/unittests/test_optimizer.py +++ b/python/paddle/fluid/tests/unittests/test_optimizer.py @@ -21,31 +21,43 @@ from paddle.fluid.backward import append_backward class TestOptimizer(unittest.TestCase): def test_sgd_optimizer(self): - init_program = framework.Program() - program = framework.Program() - block = program.global_block() - mul_x = block.create_parameter( - dtype="float32", shape=[5, 10], lod_level=0, name="mul.x") - mul_y = block.create_var( - dtype="float32", shape=[10, 8], lod_level=0, name="mul.y") - mul_out = block.create_var( - dtype="float32", shape=[5, 8], lod_level=0, name="mul.out") - mean_out = block.create_var( - dtype="float32", shape=[1], lod_level=0, name="mean.out") - block.append_op( - type="mul", - inputs={"X": mul_x, - "Y": mul_y}, - outputs={"Out": mul_out}, - attrs={"x_num_col_dims": 1}) - block.append_op( - type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out}) - sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.01) - opts, _ = sgd_optimizer.minimize(mean_out, init_program) + def check_sgd_optimizer(optimizer_attr): + init_program = framework.Program() + program = framework.Program() + block = program.global_block() + mul_x = block.create_parameter( + dtype="float32", + shape=[5, 10], + lod_level=0, + name="mul.x", + optimize_attr=optimizer_attr) + mul_y = block.create_var( + dtype="float32", shape=[10, 8], lod_level=0, name="mul.y") + mul_out = block.create_var( + dtype="float32", shape=[5, 8], lod_level=0, name="mul.out") + mean_out = block.create_var( + dtype="float32", shape=[1], lod_level=0, name="mean.out") + block.append_op( + type="mul", + inputs={"X": mul_x, + "Y": mul_y}, + outputs={"Out": mul_out}, + attrs={"x_num_col_dims": 1}) + block.append_op( + type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out}) + sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.01) + opts, _ = sgd_optimizer.minimize(mean_out, init_program) + return opts + + opts = check_sgd_optimizer({'learning_rate': 1.1}) self.assertEqual(len(opts), 3) self.assertEqual([op.type for op in opts], ["fill_constant", "elementwise_mul", "sgd"]) + opts = check_sgd_optimizer({'learning_rate': 1.0}) + self.assertEqual(len(opts), 1) + self.assertEqual([op.type for op in opts], ["sgd"]) + class TestMomentumOptimizer(unittest.TestCase): class MockMomentum(optimizer.MomentumOptimizer): @@ -60,7 +72,11 @@ class TestMomentumOptimizer(unittest.TestCase): program = framework.Program() block = program.global_block() mul_x = block.create_parameter( - dtype="float32", shape=[5, 10], lod_level=0, name="mul.x") + dtype="float32", + shape=[5, 10], + lod_level=0, + name="mul.x", + optimize_attr={'learning_rate': 1.1}) mul_y = block.create_var( dtype="float32", shape=[10, 8], lod_level=0, name="mul.y") mul_out = block.create_var( @@ -110,7 +126,11 @@ class TestMomentumOptimizer(unittest.TestCase): program = framework.Program() block = program.global_block() mul_x = block.create_parameter( - dtype="float32", shape=[5, 10], lod_level=0, name="mul.x") + dtype="float32", + shape=[5, 10], + lod_level=0, + name="mul.x", + optimize_attr={'learning_rate': 1.1}) mul_y = block.create_var( dtype="float32", shape=[10, 8], lod_level=0, name="mul.y") mul_out = block.create_var( @@ -169,7 +189,11 @@ class TestAdagradOptimizer(unittest.TestCase): program = framework.Program() block = program.global_block() mul_x = block.create_parameter( - dtype="float32", shape=[5, 10], lod_level=0, name="mul.x") + dtype="float32", + shape=[5, 10], + lod_level=0, + name="mul.x", + optimize_attr={'learning_rate': 1.1}) mul_y = block.create_var( dtype="float32", shape=[10, 8], lod_level=0, name="mul.y") mul_out = block.create_var( @@ -229,7 +253,11 @@ class TestAdamOptimizer(unittest.TestCase): program = framework.Program() block = program.global_block() mul_x = block.create_parameter( - dtype="float32", shape=[5, 10], lod_level=0, name="mul.x") + dtype="float32", + shape=[5, 10], + lod_level=0, + name="mul.x", + optimize_attr={'learning_rate': 1.1}) mul_y = block.create_var( dtype="float32", shape=[10, 8], lod_level=0, name="mul.y") mul_out = block.create_var( @@ -292,7 +320,11 @@ class TestAdamaxOptimizer(unittest.TestCase): program = framework.Program() block = program.global_block() mul_x = block.create_parameter( - dtype="float32", shape=[5, 10], lod_level=0, name="mul.x") + dtype="float32", + shape=[5, 10], + lod_level=0, + name="mul.x", + optimize_attr={'learning_rate': 1.1}) mul_y = block.create_var( dtype="float32", shape=[10, 8], lod_level=0, name="mul.y") mul_out = block.create_var( @@ -352,7 +384,11 @@ class TestDecayedAdagradOptimizer(unittest.TestCase): program = framework.Program() block = program.global_block() mul_x = block.create_parameter( - dtype="float32", shape=[5, 10], lod_level=0, name="mul.x") + dtype="float32", + shape=[5, 10], + lod_level=0, + name="mul.x", + optimize_attr={'learning_rate': 1.1}) mul_y = block.create_var( dtype="float32", shape=[10, 8], lod_level=0, name="mul.y") mul_out = block.create_var(