diff --git a/cmake/generic.cmake b/cmake/generic.cmake index b0a6dfe29020781e57d57861137861366864abdb..3bdf7c209b42ba514017c4a9a8beaba7b299b481 100644 --- a/cmake/generic.cmake +++ b/cmake/generic.cmake @@ -446,6 +446,9 @@ function(nv_library TARGET_NAME) message(FATAL "Please specify source file or library in nv_library.") endif() endif(nv_library_SRCS) + if (WIN32) + set_target_properties(${TARGET_NAME} PROPERTIES VS_USER_PROPS ${WIN_PROPS}) + endif(WIN32) endif() endfunction(nv_library) @@ -461,6 +464,9 @@ function(nv_binary TARGET_NAME) add_dependencies(${TARGET_NAME} ${nv_binary_DEPS}) common_link(${TARGET_NAME}) endif() + if (WIN32) + set_target_properties(${TARGET_NAME} PROPERTIES VS_USER_PROPS ${WIN_PROPS}) + endif(WIN32) endif() endfunction(nv_binary) @@ -482,6 +488,9 @@ function(nv_test TARGET_NAME) set_property(TEST ${TARGET_NAME} PROPERTY ENVIRONMENT FLAGS_cpu_deterministic=true) set_property(TEST ${TARGET_NAME} PROPERTY ENVIRONMENT FLAGS_init_allocated_mem=true) set_property(TEST ${TARGET_NAME} PROPERTY ENVIRONMENT FLAGS_cudnn_deterministic=true) + if (WIN32) + set_target_properties(${TARGET_NAME} PROPERTIES VS_USER_PROPS ${WIN_PROPS}) + endif(WIN32) endif() endfunction(nv_test) diff --git a/cmake/inference_lib.cmake b/cmake/inference_lib.cmake index f19f0eb43d34bd0f3748d7beb1fcf403fa1c9037..f4603051a0e7e9f206d5344fd948f7750a09c173 100644 --- a/cmake/inference_lib.cmake +++ b/cmake/inference_lib.cmake @@ -19,9 +19,8 @@ set(PADDLE_INSTALL_DIR "${CMAKE_BINARY_DIR}/paddle_install_dir" CACHE STRING set(PADDLE_INFERENCE_INSTALL_DIR "${CMAKE_BINARY_DIR}/paddle_inference_install_dir" CACHE STRING "A path setting paddle inference shared and static libraries") -# TODO(zhaolong) -# At present, the size of static lib in Windows exceeds the system limit, -# so the generation of static lib is temporarily turned off. +# At present, the size of static lib in Windows is very large, +# so we need to crop the library size. if(WIN32) #todo: remove the option option(WITH_STATIC_LIB "Compile demo with static/shared library, default use dynamic." OFF) @@ -196,7 +195,11 @@ set(PADDLE_INFERENCE_C_INSTALL_DIR "${CMAKE_BINARY_DIR}/paddle_inference_c_insta copy_part_of_thrid_party(inference_lib_dist ${PADDLE_INFERENCE_C_INSTALL_DIR}) set(src_dir "${PADDLE_SOURCE_DIR}/paddle/fluid") -set(paddle_fluid_c_lib ${PADDLE_BINARY_DIR}/paddle/fluid/inference/capi/libpaddle_fluid_c.*) +if(WIN32) + set(paddle_fluid_c_lib ${PADDLE_BINARY_DIR}/paddle/fluid/inference/capi/${CMAKE_BUILD_TYPE}/paddle_fluid_c.*) +else(WIN32) + set(paddle_fluid_c_lib ${PADDLE_BINARY_DIR}/paddle/fluid/inference/capi/libpaddle_fluid_c.*) +endif(WIN32) copy(inference_lib_dist SRCS ${src_dir}/inference/capi/paddle_c_api.h ${paddle_fluid_c_lib} diff --git a/cmake/init.cmake b/cmake/init.cmake index 7dfe60f9dd8f021facba6925a465cb58bc5de25d..902dfb11fc0afa40ea5bd2b36543b2432a3bf384 100644 --- a/cmake/init.cmake +++ b/cmake/init.cmake @@ -26,4 +26,7 @@ if(WITH_GPU) set(CMAKE_CUDA_FLAGS_MINSIZEREL "-O1 -DNDEBUG") endif() +if(WIN32) + set(WIN_PROPS ${CMAKE_SOURCE_DIR}/cmake/paddle_win.props) +endif() diff --git a/cmake/paddle_win.props b/cmake/paddle_win.props new file mode 100644 index 0000000000000000000000000000000000000000..7e434c6d907cc40733a81d8a7cdbe7c285a2bd41 --- /dev/null +++ b/cmake/paddle_win.props @@ -0,0 +1,91 @@ + + + + + + true + false + true + false + false + InheritFromHost + InheritFromHost + InheritFromHost + InheritFromHost + InheritFromHost + + -ccbin "%(VCBinDir)" -x cu [GenerateRelocatableDeviceCode] [Include] [RequiredIncludes] [InterleaveSourceInPTX] [GPUDebugInfo] [GenerateLineInfo] [Keep] [KeepDir] [MaxRegCount] [PtxAsOptionV] [TargetMachinePlatform] [NvccCompilation] [CudaRuntime] [AdditionalOptions] + --use-local-env + [CodeGeneration] + -clean + + -Xcompiler "/EHsc [Warning] /nologo [Optimization] [ProgramDataBaseFileName] $(CudaForceSynchronousPdbWrites) [RuntimeChecks] [Runtime] [TypeInfo]" + + %(BaseCommandLineTemplate) [CompileOut] "%(FullPath)" + %(BaseCommandLineTemplate) [HostDebugInfo] [Emulation] [FastMath] [Defines] %(HostCommandLineTemplate) [CompileOut] "%(FullPath)" + + +# (Approximate command-line. Settings inherited from host are not visible below.) +# (Please see the output window after a build for the full command-line) + +# Driver API (NVCC Compilation Type is .cubin, .gpu, or .ptx) +set CUDAFE_FLAGS=--sdk_dir "$(WindowsSdkDir)" +"$(CudaToolkitNvccPath)" %(BuildCommandLineTemplate) %(DriverApiCommandLineTemplate) + +# Runtime API (NVCC Compilation Type is hybrid object or .c file) +set CUDAFE_FLAGS=--sdk_dir "$(WindowsSdkDir)" +"$(CudaToolkitNvccPath)" %(BuildCommandLineTemplate) %(RuntimeApiCommandLineTemplate) + + Compiling CUDA source file %(Identity)... + Skipping CUDA source file %(Identity) (excluded from build). + + + %(Filename)%(Extension).cache + $(IntDir)%(PropsCacheOutputFile) + + $(MSBuildProjectFullPath) + + + + true + $(IntDir)$(TargetName).device-link.obj + + + true + + true + + InheritFromProject + InheritFromProject + + + + + + + + + + + + + -Xcompiler "/EHsc [Warning] /nologo [Optimization] [RuntimeChecks] [Runtime] [TypeInfo]" + "$(CudaToolkitNvccPath)" -dlink [LinkOut] %(HostCommandLineTemplate) [AdditionalLibraryDirectories] [AdditionalDependencies] [AdditionalOptions] [CodeGeneration] [GPUDebugInfo] [TargetMachinePlatform] [Inputs] + +# (Approximate command-line. Settings inherited from host are not visible below.) +# (Please see the output window after a build for the full command-line) + +%(LinkCommandLineTemplate) + + + + + %(AdditionalLibraryDirectories);$(CudaToolkitLibDir) + + + + %(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir) + + + + diff --git a/paddle/fluid/inference/CMakeLists.txt b/paddle/fluid/inference/CMakeLists.txt index cf6fcb7b64365b382c648dd83639e0c44670014d..f85e1f651165646095dae06032cdec962a37a2d1 100644 --- a/paddle/fluid/inference/CMakeLists.txt +++ b/paddle/fluid/inference/CMakeLists.txt @@ -44,14 +44,9 @@ add_subdirectory(api) set(STATIC_INFERENCE_API paddle_inference_api analysis_predictor zero_copy_tensor reset_tensor_array analysis_config paddle_pass_builder activation_functions ${mkldnn_quantizer_cfg}) -# TODO(xingzhaolong, jiweibo): remove this and create_static_lib(paddle_fluid) on windows GPU -if(WIN32 AND WITH_GPU) - cc_library(paddle_fluid DEPS ${fluid_modules} ${STATIC_INFERENCE_API}) -else() - create_static_lib(paddle_fluid ${fluid_modules} ${STATIC_INFERENCE_API}) -endif() +create_static_lib(paddle_fluid ${fluid_modules} ${STATIC_INFERENCE_API}) -if(NOT APPLE AND NOT WIN32) +if(NOT APPLE) # TODO(liuyiqu: Temporarily disable the link flag because it is not support on Mac. set(LINK_FLAGS "-Wl,--retain-symbols-file ${CMAKE_CURRENT_SOURCE_DIR}/paddle_fluid.sym") set_target_properties(paddle_fluid PROPERTIES LINK_FLAGS "${LINK_FLAGS}") diff --git a/paddle/fluid/inference/api/analysis_predictor.cc b/paddle/fluid/inference/api/analysis_predictor.cc index 42e62011f84c18b875a3fa48b95a05f152fb5791..6c68b385bcbc04844309c581bf3afd848fe4d1aa 100644 --- a/paddle/fluid/inference/api/analysis_predictor.cc +++ b/paddle/fluid/inference/api/analysis_predictor.cc @@ -245,7 +245,18 @@ bool AnalysisPredictor::PrepareExecutor() { void AnalysisPredictor::MkldnnPreSet(const std::vector &inputs) { #ifdef PADDLE_WITH_MKLDNN - VLOG(2) << "AnalysisPredictor::Run get_cur_mkldnn_session_id=" + std::vector> inputs_shape; + for (size_t i = 0; i < inputs.size(); ++i) { + inputs_shape.emplace_back(inputs[i].shape); + } + MkldnnPreSet(inputs_shape); +#endif +} + +void AnalysisPredictor::MkldnnPreSet( + const std::vector> &inputs_shape) { +#ifdef PADDLE_WITH_MKLDNN + VLOG(2) << "AnalysisPredictor::ZeroCopyRun get_cur_mkldnn_session_id=" << platform::MKLDNNDeviceContext::tls().get_cur_mkldnn_session_id(); // In cache clearing mode. if (config_.mkldnn_cache_capacity_ > 0) { @@ -257,9 +268,9 @@ void AnalysisPredictor::MkldnnPreSet(const std::vector &inputs) { config_.mkldnn_cache_capacity_); // Set current_input_shape for caching dynamic shape. std::stringstream ss; - for (size_t i = 0; i < inputs.size(); ++i) { - for (size_t j = 0; j < inputs[i].shape.size(); ++j) { - ss << inputs[i].shape[j] << "-"; + for (size_t i = 0; i < inputs_shape.size(); ++i) { + for (size_t j = 0; j < inputs_shape[i].size(); ++j) { + ss << inputs_shape[i][j] << "-"; } } VLOG(2) << "Set input shape=" << ss.str(); @@ -742,6 +753,18 @@ std::unique_ptr AnalysisPredictor::GetOutputTensor( bool AnalysisPredictor::ZeroCopyRun() { paddle::platform::SetNumThreads(config_.cpu_math_library_num_threads()); +#ifdef PADDLE_WITH_MKLDNN + if (config_.use_mkldnn_) { + std::vector> shape_vector; + auto names = GetInputNames(); + for (size_t i = 0; i < names.size(); ++i) { + auto in_tensor = GetInputTensor(names[i]); + shape_vector.emplace_back(in_tensor->shape()); + } + MkldnnPreSet(shape_vector); + } +#endif + executor_->Run(); // Fix TensorArray reuse not cleaned bug. tensor_array_batch_cleaner_.CollectTensorArrays(sub_scope_); @@ -750,6 +773,9 @@ bool AnalysisPredictor::ZeroCopyRun() { // recover the cpu_math_library_num_threads to 1, in order to avoid thread // conflict when integrating it into deployment service. paddle::platform::SetNumThreads(1); +#ifdef PADDLE_WITH_MKLDNN + if (config_.use_mkldnn_) MkldnnPostReset(); +#endif #if defined(PADDLE_WITH_MKLML) // Frees unused memory allocated by the Intel® MKL Memory Allocator to // avoid memory leak. See: diff --git a/paddle/fluid/inference/api/analysis_predictor.h b/paddle/fluid/inference/api/analysis_predictor.h index 365f86c21105a7f1ffb7c300e0ab38c6aaa230fc..c4a7173b0104b767193e7d7eee3b10f272d396a2 100644 --- a/paddle/fluid/inference/api/analysis_predictor.h +++ b/paddle/fluid/inference/api/analysis_predictor.h @@ -317,6 +317,17 @@ class AnalysisPredictor : public PaddlePredictor { /// \param[in] inputs tensors /// void MkldnnPreSet(const std::vector &inputs); + + /// + /// \brief PreSet for Mkldnn multi-thread and dynamic shape input. + /// + /// Used in AnalysisPredictor::Run(), do not support + /// AnalysisPredictor::ZeroCopyRun() now. + /// + /// \param[in] inputs tensor shape + /// + void MkldnnPreSet(const std::vector> &inputs_shape); + /// /// \brief PostReset for Mkldnn multi-thread and dynamic shape input. /// diff --git a/paddle/fluid/inference/api/demo_ci/run.sh b/paddle/fluid/inference/api/demo_ci/run.sh index a3e7bec398af7e193a75395ad40175336f5f7503..6d283ca56cb652515b5ade923ea85e38142bf08c 100755 --- a/paddle/fluid/inference/api/demo_ci/run.sh +++ b/paddle/fluid/inference/api/demo_ci/run.sh @@ -68,11 +68,6 @@ rm -rf * for WITH_STATIC_LIB in ON OFF; do if [ $(echo `uname` | grep "Win") != "" ]; then - # TODO(xingzhaolong, jiweibo): remove this if windows GPU library is ready. - if [ $TEST_GPU_CPU == ON] && [ $WITH_STATIC_LIB ==ON ]; then - return 0 - fi - # -----simple_on_word2vec on windows----- cmake .. -G "Visual Studio 14 2015" -A x64 -DPADDLE_LIB=${inference_install_dir} \ -DWITH_MKL=$TURN_ON_MKL \ diff --git a/paddle/fluid/operators/conv_cudnn_op.cu b/paddle/fluid/operators/conv_cudnn_op.cu index 00af724ac7fce64b9a210bf43a150acf20f34dce..f8b76f387cc1954cc4329daf04a6bdcab5c0775f 100644 --- a/paddle/fluid/operators/conv_cudnn_op.cu +++ b/paddle/fluid/operators/conv_cudnn_op.cu @@ -50,8 +50,9 @@ class CUDNNConvOpKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { auto& dev_ctx = ctx.template device_context(); - PADDLE_ENFORCE_EQ(platform::is_gpu_place(ctx.GetPlace()), true, - "It must use CUDAPlace."); + PADDLE_ENFORCE_EQ( + platform::is_gpu_place(ctx.GetPlace()), true, + paddle::platform::errors::PreconditionNotMet("It must use CUDAPlace.")); const Tensor* input = ctx.Input("Input"); auto* filter = ctx.Input("Filter"); auto* output = ctx.Output("Output"); @@ -60,14 +61,16 @@ class CUDNNConvOpKernel : public framework::OpKernel { std::vector paddings = ctx.Attr>("paddings"); std::vector dilations = ctx.Attr>("dilations"); int groups = ctx.Attr("groups"); + bool exhaustive_search = FLAGS_cudnn_exhaustive_search || ctx.Attr("exhaustive_search"); + bool deterministic = FLAGS_cudnn_deterministic; + auto exhaustive_deterministic = exhaustive_search && deterministic; + PADDLE_ENFORCE_EQ(exhaustive_deterministic, false, + platform::errors::InvalidArgument( + "Cann't set exhaustive_search True and " + "FLAGS_cudnn_deterministic True at same time.")); - if (exhaustive_search && FLAGS_cudnn_deterministic) { - PADDLE_THROW( - "Cann't set exhaustive_search True and " - "FLAGS_cudnn_deterministic True at same time."); - } const std::string padding_algorithm = ctx.Attr("padding_algorithm"); const std::string data_format = ctx.Attr("data_format"); @@ -197,7 +200,8 @@ class CUDNNConvOpKernel : public framework::OpKernel { &transformed_input); } break; default: - PADDLE_THROW("ConvOp only support tensors with 4 or 5 dimensions."); + PADDLE_THROW(platform::errors::InvalidArgument( + "ConvOp only support tensors with 4 or 5 dimensions.")); } } else { @@ -317,8 +321,9 @@ class CUDNNConvGradOpKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { auto& dev_ctx = ctx.template device_context(); - PADDLE_ENFORCE_EQ(platform::is_gpu_place(ctx.GetPlace()), true, - "It must use CUDAPlace."); + PADDLE_ENFORCE_EQ( + platform::is_gpu_place(ctx.GetPlace()), true, + paddle::platform::errors::PreconditionNotMet("It must use CUDAPlace.")); auto input = ctx.Input("Input"); auto filter = ctx.Input("Filter"); auto output_grad = ctx.Input(framework::GradVarName("Output")); @@ -337,14 +342,16 @@ class CUDNNConvGradOpKernel : public framework::OpKernel { std::vector paddings = ctx.Attr>("paddings"); std::string padding_algorithm = ctx.Attr("padding_algorithm"); int groups = ctx.Attr("groups"); + bool exhaustive_search = FLAGS_cudnn_exhaustive_search || ctx.Attr("exhaustive_search"); bool deterministic = FLAGS_cudnn_deterministic; - if (exhaustive_search && deterministic) { - PADDLE_THROW( - "Can't set exhaustive_search True and " - "FLAGS_cudnn_deterministic True at same time."); - } + auto exhaustive_deterministic = exhaustive_search && deterministic; + PADDLE_ENFORCE_EQ(exhaustive_deterministic, false, + platform::errors::InvalidArgument( + "Cann't set exhaustive_search True and " + "FLAGS_cudnn_deterministic True at same time.")); + const std::string data_format = ctx.Attr("data_format"); const bool channel_last = (data_format == "NHWC" || data_format == "NDHWC"); @@ -495,7 +502,8 @@ class CUDNNConvGradOpKernel : public framework::OpKernel { &transformed_input); } break; default: - PADDLE_THROW("ConvOp only support tensors with 4 or 5 dimensions."); + PADDLE_THROW(platform::errors::InvalidArgument( + "ConvOp only support tensors with 4 or 5 dimensions.")); } } else { transformed_input.ShareDataWith(transformed_input_channel); @@ -701,8 +709,9 @@ class CUDNNConvDoubleGradOpKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { auto& dev_ctx = ctx.template device_context(); - PADDLE_ENFORCE_EQ(platform::is_gpu_place(ctx.GetPlace()), true, - "It must use CUDAPlace."); + PADDLE_ENFORCE_EQ( + platform::is_gpu_place(ctx.GetPlace()), true, + paddle::platform::errors::PreconditionNotMet("It must use CUDAPlace.")); auto X = ctx.Input("Input"); auto W = ctx.Input("Filter"); auto dO = ctx.Input("DOutput"); @@ -736,14 +745,16 @@ class CUDNNConvDoubleGradOpKernel : public framework::OpKernel { const std::vector& strides = ctx.Attr>("strides"); std::vector dilations = ctx.Attr>("dilations"); int groups = ctx.Attr("groups"); + bool exhaustive_search = FLAGS_cudnn_exhaustive_search || ctx.Attr("exhaustive_search"); bool deterministic = FLAGS_cudnn_deterministic; - if (exhaustive_search && deterministic) { - PADDLE_THROW( - "Can't set exhaustive_search True and " - "FLAGS_cudnn_deterministic True at same time."); - } + auto exhaustive_deterministic = exhaustive_search && deterministic; + PADDLE_ENFORCE_EQ(exhaustive_deterministic, false, + platform::errors::InvalidArgument( + "Cann't set exhaustive_search True and " + "FLAGS_cudnn_deterministic True at same time.")); + std::vector paddings = ctx.Attr>("paddings"); std::string padding_algorithm = ctx.Attr("padding_algorithm"); @@ -878,7 +889,8 @@ class CUDNNConvDoubleGradOpKernel : public framework::OpKernel { } } break; default: - PADDLE_THROW("ConvOp only support tensors with 4 or 5 dimensions."); + PADDLE_THROW(platform::errors::InvalidArgument( + "ConvOp only support tensors with 4 or 5 dimensions.")); } } else { diff --git a/paddle/fluid/operators/conv_op.h b/paddle/fluid/operators/conv_op.h index 8a5345e3cf8d9f1c657fe2996015af4dc038a1bf..662fac9e77e023d2e1b173caa5a9769b56eaf0c4 100644 --- a/paddle/fluid/operators/conv_op.h +++ b/paddle/fluid/operators/conv_op.h @@ -685,8 +685,9 @@ class GemmConvDoubleGradKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { auto& dev_ctx = ctx.template device_context(); - PADDLE_ENFORCE_EQ(platform::is_cpu_place(ctx.GetPlace()), true, - "It must use CPUPlace."); + PADDLE_ENFORCE_EQ( + platform::is_cpu_place(ctx.GetPlace()), true, + paddle::platform::errors::PreconditionNotMet("It must use CPUPlace.")); const Tensor* X = ctx.Input("Input"); const Tensor* dY = ctx.Input("DOutput"); const Tensor* ddX = ctx.Input("DDInput"); @@ -982,11 +983,20 @@ class DepthwiseConvKernel : public framework::OpKernel { PADDLE_ENFORCE_EQ( output->dims()[output->dims().size() - 1] % input->dims()[input->dims().size() - 1], - 0, "The output channels must be a multiple of the input channels"); + 0, platform::errors::InvalidArgument( + "ShapeError: The output channels must be a multiple of the " + "input channels. But receivced output channel number is %d " + "and input channel number is %d", + output->dims()[output->dims().size() - 1], + input->dims()[input->dims().size() - 1])); } else { PADDLE_ENFORCE_EQ( output->dims()[1] % input->dims()[1], 0, - "The output channels must be a multiple of the input channels"); + platform::errors::InvalidArgument( + "ShapeError: The output channels must be a multiple of the " + "input channels. But receivced output channel number is %d " + "and input channel number is %d", + output->dims()[1], input->dims()[1])); } // transform tensor Tensor transformed_input(input->type()); diff --git a/paddle/fluid/operators/conv_transpose_cudnn_op.cu b/paddle/fluid/operators/conv_transpose_cudnn_op.cu index 99ec1e048101b281e71005f6fde328c664ba66be..5249264b1c9bcf13c5ee8227828087659de5254b 100644 --- a/paddle/fluid/operators/conv_transpose_cudnn_op.cu +++ b/paddle/fluid/operators/conv_transpose_cudnn_op.cu @@ -51,8 +51,9 @@ template class CUDNNConvTransposeOpKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { - PADDLE_ENFORCE_EQ(platform::is_gpu_place(ctx.GetPlace()), true, - "It must use CUDAPlace."); + PADDLE_ENFORCE_EQ( + platform::is_gpu_place(ctx.GetPlace()), true, + paddle::platform::errors::PreconditionNotMet("It must use CUDAPlace.")); auto* input = ctx.Input("Input"); auto* filter = ctx.Input("Filter"); auto* output = ctx.Output("Output"); @@ -145,9 +146,8 @@ class CUDNNConvTransposeOpKernel : public framework::OpKernel { ctx, input_pad, input_transpose, pad_value, &transformed_input); } break; default: - PADDLE_ENFORCE_EQ( - rank == 4 || rank == 5, true, - "Op(ConvTranspose) only supports 4-D or 5-D input Tensor."); + PADDLE_THROW(platform::errors::InvalidArgument( + "Op(ConvTranspose) only supports 4-D or 5-D input Tensor.")); } } else { transformed_input = input_transpose; @@ -290,8 +290,9 @@ template class CUDNNConvTransposeGradOpKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { - PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()), - "It must use CUDAPlace."); + PADDLE_ENFORCE_EQ( + platform::is_gpu_place(ctx.GetPlace()), true, + paddle::platform::errors::PreconditionNotMet("It must use CUDAPlace.")); auto input = ctx.Input("Input"); auto filter = ctx.Input("Filter"); auto output_grad = ctx.Input(framework::GradVarName("Output")); @@ -393,9 +394,8 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel { &transformed_output_grad); } break; default: - PADDLE_ENFORCE_EQ( - rank == 4 || rank == 5, true, - "Op(ConvTranspose) only supports 4-D or 5-D input Tensor."); + PADDLE_THROW(platform::errors::InvalidArgument( + "Op(ConvTranspose) only supports 4-D or 5-D input Tensor.")); } } else { transformed_output_grad = output_grad_transpose; diff --git a/paddle/fluid/operators/conv_transpose_op.h b/paddle/fluid/operators/conv_transpose_op.h index 59b3677acc41658936dc678d9810c923a80bf6e1..1ea869e002af3ac8157321c66616b82517e4fabc 100644 --- a/paddle/fluid/operators/conv_transpose_op.h +++ b/paddle/fluid/operators/conv_transpose_op.h @@ -580,7 +580,12 @@ class DepthwiseConvTransposeKernel : public framework::OpKernel { output->mutable_data(context.GetPlace()); int groups = context.Attr("groups"); - PADDLE_ENFORCE_EQ(groups, filter.dims()[0]); + PADDLE_ENFORCE_EQ( + groups, filter.dims()[0], + platform::errors::InvalidArgument( + "groups should be error to the 1st dimension of filter. But " + "received groups is %d and filter dimension[0] is %d", + groups, filter.dims()[0])); std::vector strides = context.Attr>("strides"); std::vector paddings = context.Attr>("paddings"); @@ -588,7 +593,10 @@ class DepthwiseConvTransposeKernel : public framework::OpKernel { std::string padding_algorithm = context.Attr("padding_algorithm"); for (auto v : dilations) { - PADDLE_ENFORCE_EQ(v, 1); + PADDLE_ENFORCE_EQ(v, 1, platform::errors::InvalidArgument( + "dilations should be 1 in depthwise conv. " + "But received dilations is %d", + v)); } auto in_dims = input->dims(); diff --git a/paddle/fluid/operators/pixel_shuffle_op.cc b/paddle/fluid/operators/pixel_shuffle_op.cc index 70d232ad6a51e21b863974e70920eb2d9da895e6..111a82c6cce78b0b6fa462c332d9bd3dd6ce6e43 100644 --- a/paddle/fluid/operators/pixel_shuffle_op.cc +++ b/paddle/fluid/operators/pixel_shuffle_op.cc @@ -46,14 +46,14 @@ class PixelShuffleOp : public framework::OperatorWithKernel { platform::errors::InvalidArgument( "The square of upscale_factor[%u] should divide the " "number of channel[%u]", - input_dims[1], upscale_factor * upscale_factor)); + upscale_factor * upscale_factor, input_dims[1])); } else { PADDLE_ENFORCE_EQ( input_dims[3] % (upscale_factor * upscale_factor), 0, platform::errors::InvalidArgument( "The square of upscale_factor[%u] should divide the " "number of channel[%u]", - input_dims[3], upscale_factor * upscale_factor)); + upscale_factor * upscale_factor, input_dims[3])); } auto output_dims = input_dims; output_dims[0] = input_dims[0]; diff --git a/python/paddle/distributed/fleet/__init__.py b/python/paddle/distributed/fleet/__init__.py index f3ee09a6d9ec1b22171253a920b26bbf98afd36e..e89cb1f5ec497133985ef8069e77e864cef47f52 100644 --- a/python/paddle/distributed/fleet/__init__.py +++ b/python/paddle/distributed/fleet/__init__.py @@ -13,7 +13,7 @@ # limitations under the License. # TODO: define distributed api under this directory, -from .base.role_maker import UserDefinedRoleMaker, PaddleCloudRoleMaker +from .base.role_maker import Role, UserDefinedRoleMaker, PaddleCloudRoleMaker from .base.distributed_strategy import DistributedStrategy from .base.fleet_base import Fleet from .base.util_factory import UtilBase @@ -26,6 +26,7 @@ __all__ = [ "UserDefinedRoleMaker", "PaddleCloudRoleMaker", "Fleet", + "Role", ] fleet = Fleet() @@ -39,8 +40,7 @@ server_num = fleet.server_num server_index = fleet.server_index server_endpoints = fleet.server_endpoints is_server = fleet.is_server -set_util = fleet.set_util -util = fleet.util +util = UtilBase() barrier_worker = fleet.barrier_worker init_worker = fleet.init_worker init_server = fleet.init_server diff --git a/python/paddle/distributed/fleet/base/fleet_base.py b/python/paddle/distributed/fleet/base/fleet_base.py index d00faac838504f5d68e9d44d9ffa9f25c7bf2ee5..d0658efdca34ec5a10a0e2f07dabc357096bfad5 100644 --- a/python/paddle/distributed/fleet/base/fleet_base.py +++ b/python/paddle/distributed/fleet/base/fleet_base.py @@ -23,7 +23,6 @@ from .strategy_compiler import StrategyCompiler from .distributed_strategy import DistributedStrategy from .meta_optimizer_factory import MetaOptimizerFactory from .runtime_factory import RuntimeFactory -from .util_factory import UtilFactory from paddle.fluid.wrapped_decorator import wrap_decorator from paddle.fluid.dygraph import parallel_helper @@ -120,7 +119,6 @@ class Fleet(object): self.strategy_compiler = None self._is_collective = False self._runtime_handle = None - self._util = None def init(self, role_maker=None, is_collective=False): """ @@ -182,6 +180,9 @@ class Fleet(object): format(type(role_maker))) self._role_maker._generate_role() + import paddle.distributed.fleet as fleet + fleet.util._set_role_maker(self._role_maker) + self.strategy_compiler = StrategyCompiler() if paddle.fluid.framework.in_dygraph_mode(): if parallel_helper._is_parallel_ctx_initialized(): @@ -353,29 +354,6 @@ class Fleet(object): return self._role_maker._is_server( ) or self._role_maker._is_heter_worker() - def set_util(self, util): - self._util = util - - def util(self): - """ - Utility functions that can be used under certain runtime - return util - - Returns: - UtilBase: instance of UtilBase, can use distributed ops/tools easily. - - Examples: - - .. code-block:: python - import paddle.distributed.fleet as fleet - fleet.init() - util = fleet.util - files = ["1.log", "2.log", "3.log", "4.log"] - files = util.get_file_shard() - - """ - return self._util - def barrier_worker(self): """ barrier all workers @@ -1102,7 +1080,7 @@ class Fleet(object): if self._runtime_handle is None: self._runtime_handle = RuntimeFactory()._create_runtime(context) - if self._util is None: - self._util = UtilFactory()._create_util(context) + import paddle.distributed.fleet as fleet + fleet.util._set_strategy(context["valid_strategy"]) return optimize_ops, params_grads diff --git a/python/paddle/distributed/fleet/base/util_factory.py b/python/paddle/distributed/fleet/base/util_factory.py index efaa854c0879ddb57c7746cede68047ff82931a0..bbb7d60ed9c79f4caae5a0eb7427679bc5c7848e 100644 --- a/python/paddle/distributed/fleet/base/util_factory.py +++ b/python/paddle/distributed/fleet/base/util_factory.py @@ -73,11 +73,13 @@ class UtilBase(object): .. code-block:: python # Save the following code in `train.py` , and then execute the command `fleetrun --server_num 2 --worker_num 2 train.py` . - from paddle.distributed.fleet.base.util_factory import fleet_util import paddle.distributed.fleet as fleet from paddle.distributed.fleet import PaddleCloudRoleMaker import sys import numpy as np + import os + + os.environ["PADDLE_WITH_GLOO"] = "2" def train(): role = PaddleCloudRoleMaker( @@ -85,19 +87,18 @@ class UtilBase(object): init_gloo=True, path="./tmp_gloo") fleet.init(role) - fleet_util._set_role_maker(role) if fleet.is_server(): input = [1, 2] - output = fleet_util.all_reduce(input, "sum", "server") + output = fleet.util.all_reduce(input, "sum", "server") print(output) # [2, 4] elif fleet.is_worker(): input = np.array([3, 4]) - output = fleet_util.all_reduce(input, "sum", "worker") + output = fleet.util.all_reduce(input, "sum", "worker") print(output) # [6, 8] - output = fleet_util.all_reduce(input, "sum", "all") + output = fleet.util.all_reduce(input, "sum", "all") print(output) # [8, 12] if __name__ == "__main__": @@ -117,10 +118,12 @@ class UtilBase(object): .. code-block:: python # Save the following code in `train.py` , and then execute the command `fleetrun --server_num 2 --worker_num 2 train.py` . - from paddle.distributed.fleet.base.util_factory import fleet_util import paddle.distributed.fleet as fleet from paddle.distributed.fleet import PaddleCloudRoleMaker import sys + import os + + os.environ["PADDLE_WITH_GLOO"] = "2" def train(): role = PaddleCloudRoleMaker( @@ -128,15 +131,14 @@ class UtilBase(object): init_gloo=True, path="./tmp_gloo") fleet.init(role) - fleet_util._set_role_maker(role) if fleet.is_server(): - fleet_util.barrier("server") + fleet.util.barrier("server") print("all server arrive here") elif fleet.is_worker(): - fleet_util.barrier("worker") + fleet.util.barrier("worker") print("all server arrive here") - fleet_util.barrier("all") + fleet.util.barrier("all") print("all servers and workers arrive here") if __name__ == "__main__": @@ -160,10 +162,12 @@ class UtilBase(object): .. code-block:: python # Save the following code in `train.py` , and then execute the command `fleetrun --server_num 2 --worker_num 2 train.py` . - from paddle.distributed.fleet.base.util_factory import fleet_util import paddle.distributed.fleet as fleet from paddle.distributed.fleet import PaddleCloudRoleMaker import sys + import os + + os.environ["PADDLE_WITH_GLOO"] = "2" def train(): role = PaddleCloudRoleMaker( @@ -171,19 +175,18 @@ class UtilBase(object): init_gloo=True, path="./tmp_gloo") fleet.init(role) - fleet_util._set_role_maker(role) if fleet.is_server(): input = fleet.server_index() - output = fleet_util.all_gather(input, "server") + output = fleet.util.all_gather(input, "server") print(output) # output = [0, 1] elif fleet.is_worker(): input = fleet.worker_index() - output = fleet_util.all_gather(input, "worker") + output = fleet.util.all_gather(input, "worker") # output = [0, 1] print(output) - output = fleet_util.all_gather(input, "all") + output = fleet.util.all_gather(input, "all") print(output) # output = [0, 1, 0, 1] @@ -220,18 +223,20 @@ class UtilBase(object): .. code-block:: python - from paddle.distributed.fleet.base.util_factory import fleet_util - import paddle.distributed.fleet.base.role_maker as role_maker + import paddle.distributed.fleet as fleet + from paddle.distributed.fleet import UserDefinedRoleMaker - role = role_maker.UserDefinedRoleMaker( + role = UserDefinedRoleMaker( is_collective=False, init_gloo=False, current_id=0, - role=role_maker.Role.WORKER, + role=fleet.Role.WORKER, worker_endpoints=["127.0.0.1:6003", "127.0.0.1:6004"], server_endpoints=["127.0.0.1:6001", "127.0.0.1:6002"]) - fleet_util._set_role_maker(role) - files = fleet_util.get_file_shard(["file1", "file2", "file3"]) + fleet.init(role) + + files = fleet.util.get_file_shard(["file1", "file2", "file3"]) + print(files) # files = ["file1", "file2"] """ if not isinstance(files, list): @@ -267,18 +272,19 @@ class UtilBase(object): .. code-block:: python - from paddle.distributed.fleet.base.util_factory import fleet_util - import paddle.distributed.fleet.base.role_maker as role_maker + import paddle.distributed.fleet as fleet + from paddle.distributed.fleet import UserDefinedRoleMaker - role = role_maker.UserDefinedRoleMaker( + role = UserDefinedRoleMaker( is_collective=False, init_gloo=False, current_id=0, - role=role_maker.Role.WORKER, + role=fleet.Role.WORKER, worker_endpoints=["127.0.0.1:6003", "127.0.0.1:6004"], server_endpoints=["127.0.0.1:6001", "127.0.0.1:6002"]) - fleet_util._set_role_maker(role) - fleet_util.print_on_rank("I'm worker 0", 0) + fleet.init(role) + + fleet.util.print_on_rank("I'm worker 0", 0) """ if self.role_maker._worker_index() != rank_id: return @@ -577,6 +583,3 @@ class UtilBase(object): print("fetch_targets name: %s" % v.name) print("fetch_targets: {}".format(results[i])) return results - - -fleet_util = UtilFactory()._create_util(None) diff --git a/python/paddle/distributed/fleet/launch.py b/python/paddle/distributed/fleet/launch.py index d63c9f9184c0eb9aafec73df09b225d598f3413f..c589e4f26a04ca8478e762c424dfa795b94f1173 100644 --- a/python/paddle/distributed/fleet/launch.py +++ b/python/paddle/distributed/fleet/launch.py @@ -181,8 +181,8 @@ def get_gpus(gpus): cuda_visible_devices_list = cuda_visible_devices.split(',') for x in gpus.split(','): assert x in cuda_visible_devices_list, "Can't find "\ - "your gpus %s in CUDA_VISIBLE_DEVICES[%s]."\ - % (x, cuda_visible_devices) + "your gpus %s in CUDA_VISIBLE_DEVICES[%s]."\ + % (x, cuda_visible_devices) res_gpus = [ cuda_visible_devices_list.index(x.strip()) for x in gpus.split(',') @@ -348,8 +348,7 @@ def launch_ps(args): "PADDLE_PORT": cur_server.endpoint.split(":")[1], "TRAINING_ROLE": "PSERVER", "PADDLE_TRAINERS_NUM": str(worker_num), - "POD_IP": cur_server.endpoint.split(":")[0], - "PADDLE_WITH_GLOO": "1" + "POD_IP": cur_server.endpoint.split(":")[0] } current_env.update(proc_env) @@ -388,8 +387,7 @@ def launch_ps(args): "PADDLE_TRAINER_ENDPOINTS": worker_endpoints, "PADDLE_TRAINERS_NUM": str(worker_num), "TRAINING_ROLE": "TRAINER", - "PADDLE_TRAINER_ID": str(cur_worker.rank), - "PADDLE_WITH_GLOO": "1" + "PADDLE_TRAINER_ID": str(cur_worker.rank) } current_env.update(proc_env) diff --git a/python/paddle/distributed/fleet/utils/__init__.py b/python/paddle/distributed/fleet/utils/__init__.py index abf198b97e6e818e1fbe59006f98492640bcee54..a45e1682c3fad46ce62274ae14577cb7fd6b89b8 100644 --- a/python/paddle/distributed/fleet/utils/__init__.py +++ b/python/paddle/distributed/fleet/utils/__init__.py @@ -11,3 +11,5 @@ # 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. + +from .fs import LocalFS, HDFSClient diff --git a/python/paddle/distributed/fleet/utils/fs.py b/python/paddle/distributed/fleet/utils/fs.py index b7c50bda3eae0a004bd3d0169fc62260393b28b8..8d4f24fb116cccacbc88aa73ffbaeb559e1fdc06 100644 --- a/python/paddle/distributed/fleet/utils/fs.py +++ b/python/paddle/distributed/fleet/utils/fs.py @@ -120,7 +120,7 @@ class LocalFS(FS): Examples: .. code-block:: python - from paddle.distributed.fleet.utils.fs import LocalFS + from paddle.distributed.fleet.utils import LocalFS client = LocalFS() subdirs, files = client.ls_dir("./") @@ -140,7 +140,7 @@ class LocalFS(FS): Examples: .. code-block:: python - from paddle.distributed.fleet.utils.fs import LocalFS + from paddle.distributed.fleet.utils import LocalFS client = LocalFS() subdirs, files = client.ls_dir("./") @@ -160,7 +160,7 @@ class LocalFS(FS): def mkdirs(self, fs_path): """ - Create a remote HDFS directory. + Create a local directory. Args: fs_path(str): The local directory path. @@ -168,7 +168,7 @@ class LocalFS(FS): Examples: .. code-block:: python - from paddle.distributed.fleet.utils.fs import LocalFS + from paddle.distributed.fleet.utils import LocalFS client = LocalFS() client.mkdirs("test_mkdirs") @@ -189,7 +189,7 @@ class LocalFS(FS): Examples: .. code-block:: python - from paddle.distributed.fleet.utils.fs import LocalFS + from paddle.distributed.fleet.utils import LocalFS client = LocalFS() client.touch("test_rename_src") @@ -217,7 +217,7 @@ class LocalFS(FS): Examples: .. code-block:: python - from paddle.distributed.fleet.utils.fs import LocalFS + from paddle.distributed.fleet.utils import LocalFS client = LocalFS() client.mkdirs("test_localFS_mkdirs") @@ -247,7 +247,7 @@ class LocalFS(FS): Examples: .. code-block:: python - from paddle.distributed.fleet.utils.fs import LocalFS + from paddle.distributed.fleet.utils import LocalFS client = LocalFS() client.touch("test_is_file") @@ -269,7 +269,7 @@ class LocalFS(FS): Examples: .. code-block:: python - from paddle.distributed.fleet.utils.fs import LocalFS + from paddle.distributed.fleet.utils import LocalFS client = LocalFS() client.mkdirs("test_is_dir") @@ -292,7 +292,7 @@ class LocalFS(FS): Examples: .. code-block:: python - from paddle.distributed.fleet.utils.fs import LocalFS + from paddle.distributed.fleet.utils import LocalFS client = LocalFS() ret = local_fs.is_exist("test_is_exist") @@ -311,7 +311,7 @@ class LocalFS(FS): Examples: .. code-block:: python - from paddle.distributed.fleet.utils.fs import LocalFS + from paddle.distributed.fleet.utils import LocalFS client = LocalFS() client.touch("test_touch") @@ -332,13 +332,11 @@ class LocalFS(FS): src_path(str): Name of the file or directory, that's needed to be moved. dst_path(str): Name of the file or directory to which to move to. overwrite(bool): Whether to re-write `dst_path` if that exists. Default is False. - test_exists(bool): Check the existence of `src_path` and `dst_path` . - When `test_exists` is set true, if `src_path` doesn't exist or `dst_path` exists, program will throw an Excetption. Examples: .. code-block:: python - from paddle.distributed.fleet.utils.fs import LocalFS + from paddle.distributed.fleet.utils import LocalFS client = LocalFS() client.touch("test_mv_src") @@ -369,7 +367,7 @@ class LocalFS(FS): Examples: .. code-block:: python - from paddle.distributed.fleet.utils.fs import LocalFS + from paddle.distributed.fleet.utils import LocalFS client = LocalFS() subdirs = client.list_dirs("./") @@ -432,7 +430,7 @@ class HDFSClient(FS): .. code-block:: text - from paddle.distributed.fleet.utils.fs import HDFSClient + from paddle.distributed.fleet.utils import HDFSClient hadoop_home = "/home/client/hadoop-client/hadoop/" configs = { @@ -493,7 +491,7 @@ class HDFSClient(FS): .. code-block:: text - from paddle.distributed.fleet.utils.fs import HDFSClient + from paddle.distributed.fleet.utils import HDFSClient hadoop_home = "/home/client/hadoop-client/hadoop/" configs = { @@ -526,7 +524,7 @@ class HDFSClient(FS): .. code-block:: text - from paddle.distributed.fleet.utils.fs import HDFSClient + from paddle.distributed.fleet.utils import HDFSClient hadoop_home = "/home/client/hadoop-client/hadoop/" configs = { @@ -587,7 +585,7 @@ class HDFSClient(FS): .. code-block:: text - from paddle.distributed.fleet.utils.fs import HDFSClient + from paddle.distributed.fleet.utils import HDFSClient hadoop_home = "/home/client/hadoop-client/hadoop/" configs = { @@ -629,7 +627,7 @@ class HDFSClient(FS): .. code-block:: text - from paddle.distributed.fleet.utils.fs import HDFSClient + from paddle.distributed.fleet.utils import HDFSClient hadoop_home = "/home/client/hadoop-client/hadoop/" configs = { @@ -661,7 +659,7 @@ class HDFSClient(FS): .. code-block:: text - from paddle.distributed.fleet.utils.fs import HDFSClient + from paddle.distributed.fleet.utils import HDFSClient hadoop_home = "/home/client/hadoop-client/hadoop/" configs = { @@ -695,7 +693,7 @@ class HDFSClient(FS): .. code-block:: text - from paddle.distributed.fleet.utils.fs import HDFSClient + from paddle.distributed.fleet.utils import HDFSClient hadoop_home = "/home/client/hadoop-client/hadoop/" configs = { @@ -740,7 +738,7 @@ class HDFSClient(FS): .. code-block:: text - from paddle.distributed.fleet.utils.fs import HDFSClient + from paddle.distributed.fleet.utils import HDFSClient hadoop_home = "/home/client/hadoop-client/hadoop/" configs = { @@ -784,7 +782,7 @@ class HDFSClient(FS): .. code-block:: text - from paddle.distributed.fleet.utils.fs import HDFSClient + from paddle.distributed.fleet.utils import HDFSClient hadoop_home = "/home/client/hadoop-client/hadoop/" configs = { @@ -830,7 +828,7 @@ class HDFSClient(FS): .. code-block:: text - from paddle.distributed.fleet.utils.fs import HDFSClient + from paddle.distributed.fleet.utils import HDFSClient hadoop_home = "/home/client/hadoop-client/hadoop/" configs = { @@ -893,7 +891,7 @@ class HDFSClient(FS): .. code-block:: text - from paddle.distributed.fleet.utils.fs import HDFSClient + from paddle.distributed.fleet.utils import HDFSClient hadoop_home = "/home/client/hadoop-client/hadoop/" configs = { @@ -919,12 +917,14 @@ class HDFSClient(FS): Args: fs_path(str): The HDFS file path. + exist_ok(bool): When `fs_path` exists, if `exist_ok` is set false, + program will throw an Exception. Default is true. Examples: .. code-block:: text - from paddle.distributed.fleet.utils.fs import HDFSClient + from paddle.distributed.fleet.utils import HDFSClient hadoop_home = "/home/client/hadoop-client/hadoop/" configs = { diff --git a/python/paddle/fluid/tests/unittests/dist_fleet_ctr.py b/python/paddle/fluid/tests/unittests/dist_fleet_ctr.py index 5721445c414cf94379f44cab6bd01cca511938bf..f650dd0f7e9824ce2c2c495f1fb85fe1fc421d08 100644 --- a/python/paddle/fluid/tests/unittests/dist_fleet_ctr.py +++ b/python/paddle/fluid/tests/unittests/dist_fleet_ctr.py @@ -28,7 +28,6 @@ import numpy as np import ctr_dataset_reader from test_dist_fleet_base import runtime_main, FleetDistRunnerBase -from paddle.distributed.fleet.base.util_factory import fleet_util paddle.enable_static() @@ -180,13 +179,13 @@ class TestDistCTR2x2(FleetDistRunnerBase): fetch_list=[self.avg_cost.name]) loss_val = np.mean(loss_val) # TODO(randomly fail) - # reduce_output = fleet_util.all_reduce( + # reduce_output = fleet.util.all_reduce( # np.array(loss_val), mode="sum") - # loss_all_trainer = fleet_util.all_gather(float(loss_val)) + # loss_all_trainer = fleet.util.all_gather(float(loss_val)) # loss_val = float(reduce_output) / len(loss_all_trainer) message = "TRAIN ---> pass: {} loss: {}\n".format(epoch_id, loss_val) - fleet_util.print_on_rank(message, 0) + fleet.util.print_on_rank(message, 0) pass_time = time.time() - pass_start except fluid.core.EOFException: diff --git a/python/paddle/fluid/tests/unittests/dist_fleet_ctr_ps_gpu.py b/python/paddle/fluid/tests/unittests/dist_fleet_ctr_ps_gpu.py index 3852b225234ffacc2be749245fb1341331868272..7accc917f808e6f010e7f38de651a8a3965fcdf3 100644 --- a/python/paddle/fluid/tests/unittests/dist_fleet_ctr_ps_gpu.py +++ b/python/paddle/fluid/tests/unittests/dist_fleet_ctr_ps_gpu.py @@ -29,7 +29,6 @@ import numpy as np import ctr_dataset_reader from test_dist_fleet_base import runtime_main, FleetDistRunnerBase from dist_fleet_ctr import TestDistCTR2x2, fake_ctr_reader -from paddle.distributed.fleet.base.util_factory import fleet_util # Fix seed for test fluid.default_startup_program().random_seed = 1 @@ -76,13 +75,13 @@ class TestDistGpuPsCTR2x2(TestDistCTR2x2): loss_val = exe.run(program=fleet.main_program, fetch_list=[self.avg_cost.name]) loss_val = np.mean(loss_val) - reduce_output = fleet_util.all_reduce( + reduce_output = fleet.util.all_reduce( np.array(loss_val), mode="sum") - loss_all_trainer = fleet_util.all_gather(float(loss_val)) + loss_all_trainer = fleet.util.all_gather(float(loss_val)) loss_val = float(reduce_output) / len(loss_all_trainer) message = "TRAIN ---> pass: {} loss: {}\n".format(epoch_id, loss_val) - fleet_util.print_on_rank(message, 0) + fleet.util.print_on_rank(message, 0) pass_time = time.time() - pass_start except fluid.core.EOFException: diff --git a/python/paddle/fluid/tests/unittests/dist_fleet_heter_ctr.py b/python/paddle/fluid/tests/unittests/dist_fleet_heter_ctr.py index 470fb98d7991cf0cbffa47f6d5129b045f59ae97..f62ad66e462862f4c3f04bacc58ca7aac583ef1e 100644 --- a/python/paddle/fluid/tests/unittests/dist_fleet_heter_ctr.py +++ b/python/paddle/fluid/tests/unittests/dist_fleet_heter_ctr.py @@ -29,7 +29,6 @@ import numpy as np import ctr_dataset_reader from test_dist_fleet_heter_base import runtime_main, FleetDistHeterRunnerBase from dist_fleet_ctr import TestDistCTR2x2, fake_ctr_reader -from paddle.distributed.fleet.base.util_factory import fleet_util paddle.enable_static() @@ -182,7 +181,7 @@ class TestHeterPsCTR2x2(FleetDistHeterRunnerBase): thread_num = int(os.getenv("CPU_NUM", 2)) batch_size = 128 - filelist = fleet_util.get_file_shard(train_file_list) + filelist = fleet.util.get_file_shard(train_file_list) print("filelist: {}".format(filelist)) # config dataset diff --git a/python/paddle/fluid/tests/unittests/dist_fleet_simnet_bow.py b/python/paddle/fluid/tests/unittests/dist_fleet_simnet_bow.py index ff84848873924c52b0f7e8f5bc71ec2a266b73f1..fb7ddef862d0f0bd54f10e090f6072a3dd808057 100644 --- a/python/paddle/fluid/tests/unittests/dist_fleet_simnet_bow.py +++ b/python/paddle/fluid/tests/unittests/dist_fleet_simnet_bow.py @@ -32,7 +32,6 @@ import os import signal from functools import reduce from test_dist_fleet_base import runtime_main, FleetDistRunnerBase -from paddle.distributed.fleet.base.util_factory import fleet_util paddle.enable_static() @@ -198,7 +197,7 @@ class TestDistSimnetBow2x2(FleetDistRunnerBase): def net(self, args, batch_size=4, lr=0.01): avg_cost, _, predict, self.reader = \ train_network(batch_size=batch_size, is_distributed=False, - is_sparse=True, is_self_contained_lr=False, is_pyreader=(args.reader == "pyreader")) + is_sparse=True, is_self_contained_lr=False, is_pyreader=(args.reader == "pyreader")) self.avg_cost = avg_cost self.predict = predict @@ -238,7 +237,7 @@ class TestDistSimnetBow2x2(FleetDistRunnerBase): loss_val = np.mean(loss_val) message = "TRAIN ---> pass: {} loss: {}\n".format(epoch_id, loss_val) - fleet_util.print_on_rank(message, 0) + fleet.util.print_on_rank(message, 0) pass_time = time.time() - pass_start except fluid.core.EOFException: diff --git a/python/paddle/fluid/tests/unittests/test_dist_fleet_base.py b/python/paddle/fluid/tests/unittests/test_dist_fleet_base.py index 3a923dbf3f72e28c64c3f01d22d4d6f2d897f79b..c46d1dc5b0f87262aee8efd4722418be433c98ea 100644 --- a/python/paddle/fluid/tests/unittests/test_dist_fleet_base.py +++ b/python/paddle/fluid/tests/unittests/test_dist_fleet_base.py @@ -34,8 +34,7 @@ import unittest import paddle import paddle.fluid as fluid import paddle.distributed.fleet.base.role_maker as role_maker -from paddle.distributed.fleet.base.util_factory import fleet_util -from paddle.distributed.fleet import fleet +import paddle.distributed.fleet as fleet from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler.distributed_strategy import StrategyFactory __all__ = ['FleetDistRunnerBase', 'TestFleetBase', 'runtime_main'] @@ -97,7 +96,7 @@ class FleetDistRunnerBase(object): self.dump_fields_path = os.getenv("dump_fields_path", "") debug = int(os.getenv("Debug", "0")) # TODO(update strategy to support dump params) - if False: #debug: + if False: # debug: self.strategy.set_debug_opt({ "dump_param": self.dump_param, "dump_fields": self.dump_fields, @@ -372,8 +371,6 @@ def runtime_main(test_class): strategy = model.build_strategy(args) avg_cost = model.net(args) model.build_optimizer(avg_cost, strategy) - fleet_util._set_strategy(strategy) - fleet_util._set_role_maker(role) if args.role == "pserver": model.run_pserver(args) else: diff --git a/python/paddle/fluid/tests/unittests/test_dist_fleet_heter_base.py b/python/paddle/fluid/tests/unittests/test_dist_fleet_heter_base.py index 4d744c8299f484fd60a081adb1b3b9eb2834ddef..ba97c5079bde429b0b7145208926b570d04725bc 100644 --- a/python/paddle/fluid/tests/unittests/test_dist_fleet_heter_base.py +++ b/python/paddle/fluid/tests/unittests/test_dist_fleet_heter_base.py @@ -34,8 +34,7 @@ import unittest import paddle import paddle.fluid as fluid import paddle.distributed.fleet.base.role_maker as role_maker -from paddle.distributed.fleet.base.util_factory import fleet_util -from paddle.distributed.fleet import fleet +import paddle.distributed.fleet as fleet __all__ = ['FleetDistHeterRunnerBase', 'TestFleetHeterBase', 'runtime_main'] @@ -376,8 +375,6 @@ def runtime_main(test_class): strategy = model.build_strategy(args) avg_cost = model.net(args) model.build_optimizer(avg_cost, strategy) - fleet_util._set_strategy(strategy) - fleet_util._set_role_maker(role) if args.role == "pserver" or args.role == "heter_trainer": model.run_pserver(args) diff --git a/python/paddle/fluid/tests/unittests/test_dist_fleet_heter_program.py b/python/paddle/fluid/tests/unittests/test_dist_fleet_heter_program.py index 00301f9b1c61dd12dc993e0b4c735479fe16daed..7f4e5d99e02084f363b71dbb73b80e59d704aa15 100644 --- a/python/paddle/fluid/tests/unittests/test_dist_fleet_heter_program.py +++ b/python/paddle/fluid/tests/unittests/test_dist_fleet_heter_program.py @@ -19,7 +19,6 @@ import os import math import paddle.fluid as fluid import paddle.distributed.fleet.base.role_maker as role_maker -from paddle.distributed.fleet.base.util_factory import fleet_util from paddle.distributed.fleet import fleet import paddle diff --git a/python/paddle/fluid/tests/unittests/test_fleet_base.py b/python/paddle/fluid/tests/unittests/test_fleet_base.py index 45597e7253c4d5bab50aa58f5f58e13e89ce1c1e..ccd57c4d51529f2c178f6b7c6c8f98851b1e9e80 100644 --- a/python/paddle/fluid/tests/unittests/test_fleet_base.py +++ b/python/paddle/fluid/tests/unittests/test_fleet_base.py @@ -107,7 +107,7 @@ class TestFleetBase(unittest.TestCase): def test_util(self): role = role_maker.PaddleCloudRoleMaker(is_collective=True) fleet.init(role) - self.assertEqual(fleet.util(), None) + self.assertNotEqual(fleet.util, None) def test_barrier_worker(self): role = role_maker.PaddleCloudRoleMaker(is_collective=True) diff --git a/python/paddle/fluid/tests/unittests/test_fleet_rolemaker_new.py b/python/paddle/fluid/tests/unittests/test_fleet_rolemaker_new.py index 4dd254af251ae955878f9846e0f0e06f65c3ec90..992fbbbe2661d7059fc2acccb2b7fda5a357157a 100644 --- a/python/paddle/fluid/tests/unittests/test_fleet_rolemaker_new.py +++ b/python/paddle/fluid/tests/unittests/test_fleet_rolemaker_new.py @@ -436,12 +436,12 @@ class TestGlooWithCloudRoleMaker(unittest.TestCase): optimizer.minimize(avg_cost) comm_world = "server" - fleet.util().barrier(comm_world) + fleet.util.barrier(comm_world) - gather = fleet.util().all_gather(1, comm_world) + gather = fleet.util.all_gather(1, comm_world) self.assertEqual(gather[0], 1) - all_reduce = fleet.util().all_reduce(1, "sum", comm_world) + all_reduce = fleet.util.all_reduce(1, "sum", comm_world) self.assertEqual(1, all_reduce) self.clean(tmp) @@ -752,12 +752,12 @@ class TestGlooWithCloudRoleMaker(unittest.TestCase): optimizer.minimize(avg_cost) comm_world = "server" - fleet.util().barrier(comm_world) + fleet.util.barrier(comm_world) - gather = fleet.util().all_gather(1, comm_world) + gather = fleet.util.all_gather(1, comm_world) self.assertEqual(gather[0], 1) - all_reduce = fleet.util().all_reduce(1, "sum", comm_world) + all_reduce = fleet.util.all_reduce(1, "sum", comm_world) self.assertEqual(1, all_reduce) self.clean(tmp) diff --git a/python/paddle/fluid/tests/unittests/test_fleet_util.py b/python/paddle/fluid/tests/unittests/test_fleet_util.py index 1570912e7406f930212eead64305e1e35e1b8ac0..b5c22b192a16dec43c5e5243c34b970f489f5bb7 100644 --- a/python/paddle/fluid/tests/unittests/test_fleet_util.py +++ b/python/paddle/fluid/tests/unittests/test_fleet_util.py @@ -22,7 +22,6 @@ import tempfile import os import sys from paddle.dataset.common import download, DATA_HOME -from paddle.distributed.fleet.base.util_factory import fleet_util import paddle.distributed.fleet.base.role_maker as role_maker @@ -59,8 +58,7 @@ class TestFleetUtil(unittest.TestCase): import paddle.distributed.fleet.base.role_maker as role_maker role = role_maker.PaddleCloudRoleMaker(is_collective=True) fleet.init(role) - default_util = fleet.util() - self.assertEqual(default_util, None) + self.assertNotEqual(fleet.util, None) def test_set_user_defined_util(self): import paddle.distributed.fleet as fleet @@ -76,17 +74,19 @@ class TestFleetUtil(unittest.TestCase): role = role_maker.PaddleCloudRoleMaker(is_collective=True) fleet.init(role) my_util = UserDefinedUtil() - fleet.set_util(my_util) - user_id = fleet.util().get_user_id() + fleet.util = my_util + user_id = fleet.util.get_user_id() self.assertEqual(user_id, 10) def test_fs(self): - from paddle.distributed.fleet.utils.fs import LocalFS + import paddle.distributed.fleet as fleet + from paddle.distributed.fleet.utils import LocalFS + fs = LocalFS() dirs, files = fs.ls_dir("test_tmp") dirs, files = fs.ls_dir("./") self.assertFalse(fs.need_upload_download()) - fleet_util._set_file_system(fs) + fleet.util._set_file_system(fs) def download_files(self): path = download(self.proto_data_url, self.module_name, @@ -98,7 +98,8 @@ class TestFleetUtil(unittest.TestCase): return unzip_folder def test_get_file_shard(self): - self.assertRaises(Exception, fleet_util.get_file_shard, "files") + import paddle.distributed.fleet as fleet + self.assertRaises(Exception, fleet.util.get_file_shard, "files") try: import netifaces except: @@ -112,18 +113,20 @@ class TestFleetUtil(unittest.TestCase): role=role_maker.Role.WORKER, worker_endpoints=["127.0.0.1:6003", "127.0.0.1:6004"], server_endpoints=["127.0.0.1:6001", "127.0.0.1:6002"]) - fleet_util._set_role_maker(role) - files = fleet_util.get_file_shard(["1", "2", "3"]) + fleet.init(role) + + files = fleet.util.get_file_shard(["1", "2", "3"]) self.assertTrue(len(files) == 2 and "1" in files and "2" in files) def test_program_type_trans(self): + import paddle.distributed.fleet as fleet data_dir = self.download_files() program_dir = os.path.join(data_dir, self.pruned_dir) text_program = "pruned_main_program.pbtxt" binary_program = "pruned_main_program.bin" - text_to_binary = fleet_util._program_type_trans(program_dir, + text_to_binary = fleet.util._program_type_trans(program_dir, text_program, True) - binary_to_text = fleet_util._program_type_trans(program_dir, + binary_to_text = fleet.util._program_type_trans(program_dir, binary_program, False) self.assertTrue( os.path.exists(os.path.join(program_dir, text_to_binary))) @@ -131,6 +134,7 @@ class TestFleetUtil(unittest.TestCase): os.path.exists(os.path.join(program_dir, binary_to_text))) def test_prams_check(self): + import paddle.distributed.fleet as fleet data_dir = self.download_files() class config: @@ -160,11 +164,11 @@ class TestFleetUtil(unittest.TestCase): # test saved var's shape conf.dump_program_filename = "pruned_main_program.save_var_shape_not_match" - self.assertRaises(Exception, fleet_util._params_check) + self.assertRaises(Exception, fleet.util._params_check) # test program.proto without feed_op and fetch_op conf.dump_program_filename = "pruned_main_program.no_feed_fetch" - results = fleet_util._params_check(conf) + results = fleet.util._params_check(conf) self.assertTrue(len(results) == 1) np.testing.assert_array_almost_equal( results[0], np.array( @@ -172,11 +176,11 @@ class TestFleetUtil(unittest.TestCase): # test feed_var's shape conf.dump_program_filename = "pruned_main_program.feed_var_shape_not_match" - self.assertRaises(Exception, fleet_util._params_check) + self.assertRaises(Exception, fleet.util._params_check) # test correct case with feed_vars_filelist conf.dump_program_filename = "pruned_main_program.pbtxt" - results = fleet_util._params_check(conf) + results = fleet.util._params_check(conf) self.assertTrue(len(results) == 1) np.testing.assert_array_almost_equal( results[0], np.array( @@ -186,13 +190,14 @@ class TestFleetUtil(unittest.TestCase): conf.feed_config.feeded_vars_filelist = None # test feed var with lod_level >= 2 conf.dump_program_filename = "pruned_main_program.feed_lod2" - self.assertRaises(Exception, fleet_util._params_check) + self.assertRaises(Exception, fleet.util._params_check) conf.dump_program_filename = "pruned_main_program.pbtxt" - results = fleet_util._params_check(conf) + results = fleet.util._params_check(conf) self.assertTrue(len(results) == 1) def test_proto_check(self): + import paddle.distributed.fleet as fleet data_dir = self.download_files() class config: @@ -210,7 +215,7 @@ class TestFleetUtil(unittest.TestCase): "pruned_main_program.save_var_shape_not_match")) conf.is_text_pruned_program = True conf.draw = False - res = fleet_util._proto_check(conf) + res = fleet.util._proto_check(conf) self.assertFalse(res) # test match @@ -222,10 +227,11 @@ class TestFleetUtil(unittest.TestCase): else: conf.draw = True conf.draw_out_name = "pruned_check" - res = fleet_util._proto_check(conf) + res = fleet.util._proto_check(conf) self.assertTrue(res) def test_visualize(self): + import paddle.distributed.fleet as fleet if sys.platform == 'win32' or sys.platform == 'sys.platform': pass else: @@ -234,10 +240,10 @@ class TestFleetUtil(unittest.TestCase): data_dir, os.path.join(self.train_dir, "join_main_program.pbtxt")) is_text = True - program = fleet_util._load_program(program_path, is_text) + program = fleet.util._load_program(program_path, is_text) output_dir = os.path.join(data_dir, self.train_dir) output_filename = "draw_prog" - fleet_util._visualize_graphviz(program, output_dir, output_filename) + fleet.util._visualize_graphviz(program, output_dir, output_filename) self.assertTrue( os.path.exists( os.path.join(output_dir, output_filename + ".dot"))) diff --git a/python/paddle/fluid/tests/unittests/test_imperative_signal_handler.py b/python/paddle/fluid/tests/unittests/test_imperative_signal_handler.py index b47834ffab85e56b12c787ac57823ee02dd18df7..a0da4b0efee64e6a7aa26a213419764afa90bd22 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_signal_handler.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_signal_handler.py @@ -49,7 +49,7 @@ class TestDygraphDataLoaderSingalHandler(unittest.TestCase): test_process.start() set_child_signal_handler(id(self), test_process.pid) - time.sleep(3) + time.sleep(5) except core.EnforceNotMet as ex: self.assertIn("FatalError", cpt.get_exception_message(ex)) exception = ex diff --git a/python/paddle/fluid/tests/unittests/test_parallel_executor_mnist.py b/python/paddle/fluid/tests/unittests/test_parallel_executor_mnist.py index a2f1d77460819a933e8ab536d0ebc7dd32bedf3f..da7e30ff10643278eb355c65157a02a8fae6cff3 100644 --- a/python/paddle/fluid/tests/unittests/test_parallel_executor_mnist.py +++ b/python/paddle/fluid/tests/unittests/test_parallel_executor_mnist.py @@ -124,8 +124,10 @@ class TestMNIST(TestParallelExecutorBase): def test_simple_fc_with_new_strategy(self): # use_cuda, use_reduce - self._compare_reduce_and_allreduce(simple_fc_net, True) - self._compare_reduce_and_allreduce(simple_fc_net, False) + # NOTE: the computation result of nccl_reduce is non-deterministic, + # related issue: https://github.com/NVIDIA/nccl/issues/157 + self._compare_reduce_and_allreduce(simple_fc_net, True, 1e-5, 1e-2) + self._compare_reduce_and_allreduce(simple_fc_net, False, 1e-5, 1e-2) def check_simple_fc_parallel_accuracy(self, use_cuda): if use_cuda and not core.is_compiled_with_cuda(): @@ -179,7 +181,7 @@ class TestMNIST(TestParallelExecutorBase): # NOTE: the computation result of nccl_reduce is non-deterministic, # related issue: https://github.com/NVIDIA/nccl/issues/157 self._compare_reduce_and_allreduce(fc_with_batchnorm, True, 1e-5, 1e-2) - self._compare_reduce_and_allreduce(fc_with_batchnorm, False) + self._compare_reduce_and_allreduce(fc_with_batchnorm, False, 1e-5, 1e-2) if __name__ == '__main__':