diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index e718b32cb6c48d11e73600509a17db107f438708..d8112837dc9627bc2e501940b8e97c89e97c45ff 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -42,12 +42,6 @@ repos: entry: bash ./tools/codestyle/pylint_pre_commit.hook language: system files: \.(py)$ -- repo: https://github.com/PaddlePaddle/pre-commit-golang - sha: 8337620115c25ff8333f1b1a493bd031049bd7c0 - hooks: - - id: go-fmt - types: - - go - repo: local hooks: - id: copyright_checker diff --git a/cmake/external/mklml.cmake b/cmake/external/mklml.cmake index 54826cedb871690a82b535ae3ed102600277c622..ae2679db4aed7a77ad407f881c4482fd3914ac27 100644 --- a/cmake/external/mklml.cmake +++ b/cmake/external/mklml.cmake @@ -39,8 +39,10 @@ IF(WIN32) SET(MKLML_IOMP_LIB ${MKLML_LIB_DIR}/libiomp5md.lib) SET(MKLML_SHARED_LIB ${MKLML_LIB_DIR}/mklml.dll) SET(MKLML_SHARED_IOMP_LIB ${MKLML_LIB_DIR}/libiomp5md.dll) -ELSE() - SET(MKLML_VER "mklml_lnx_${TIME_VERSION}" CACHE STRING "" FORCE) +ELSE() + #TODO(intel-huying): + # Now enable Erf function in mklml library temporarily, it will be updated as offical version later. + SET(MKLML_VER "Glibc225_vsErf_mklml_lnx_${TIME_VERSION}" CACHE STRING "" FORCE) SET(MKLML_URL "http://paddlepaddledeps.cdn.bcebos.com/${MKLML_VER}.tgz" CACHE STRING "" FORCE) SET(MKLML_LIB ${MKLML_LIB_DIR}/libmklml_intel.so) SET(MKLML_IOMP_LIB ${MKLML_LIB_DIR}/libiomp5.so) diff --git a/cmake/external/ngraph.cmake b/cmake/external/ngraph.cmake index 5812a61f0ddc3a3233ff212710fc1b16aa140724..e7fb69dbbc872c813b2eba16a5b1098eebfeedd8 100644 --- a/cmake/external/ngraph.cmake +++ b/cmake/external/ngraph.cmake @@ -37,7 +37,7 @@ INCLUDE(GNUInstallDirs) INCLUDE(ExternalProject) SET(NGRAPH_PROJECT "extern_ngraph") -SET(NGRAPH_GIT_TAG "20bd8bbc79ae3a81c57313846a2be7313e5d1dab") +SET(NGRAPH_GIT_TAG "a444f7a959b7d87f2c117c9b57a4c387759e481e") SET(NGRAPH_SOURCES_DIR ${THIRD_PARTY_PATH}/ngraph) SET(NGRAPH_INSTALL_DIR ${THIRD_PARTY_PATH}/install/ngraph) SET(NGRAPH_INC_DIR ${NGRAPH_INSTALL_DIR}/include) @@ -69,7 +69,7 @@ ExternalProject_Add( CMAKE_ARGS -DNGRAPH_DEX_ONLY=TRUE CMAKE_ARGS -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE} CMAKE_ARGS -DMKLDNN_INCLUDE_DIR=${MKLDNN_INC_DIR} - CMAKE_ARGS -DMKLDNN_LIB_DIR=${MKLDNN_INSTALL_DIR}/lib + CMAKE_ARGS -DMKLDNN_LIB_DIR=${MKLDNN_INSTALL_DIR}/${CMAKE_INSTALL_LIBDIR} CMAKE_ARGS -DMKLML_LIB_DIR=${MKLML_INSTALL_DIR}/lib ) diff --git a/cmake/operators.cmake b/cmake/operators.cmake index c2d04828564e69d7ac965881057f185194aa0475..11a5b1b4554e7899c3ee7092a9295234743750d7 100644 --- a/cmake/operators.cmake +++ b/cmake/operators.cmake @@ -153,7 +153,11 @@ function(op_library TARGET) # pybind USE_OP_DEVICE_KERNEL for CUDNN list(LENGTH cudnn_cu_cc_srcs cudnn_cu_cc_srcs_len) if (WITH_GPU AND ${cudnn_cu_cc_srcs_len} GREATER 0) + if(${TARGET} STREQUAL "activation") + file(APPEND ${pybind_file} "USE_OP_DEVICE_KERNEL(relu, CUDNN);\n") + else() file(APPEND ${pybind_file} "USE_OP_DEVICE_KERNEL(${TARGET}, CUDNN);\n") + endif() endif() # pybind USE_OP_DEVICE_KERNEL for MIOPEN @@ -168,6 +172,9 @@ function(op_library TARGET) file(APPEND ${pybind_file} "USE_OP_DEVICE_KERNEL(relu, MKLDNN);\n") elseif(${MKLDNN_FILE} STREQUAL "conv_mkldnn_op") file(APPEND ${pybind_file} "USE_OP_DEVICE_KERNEL_WITH_CUSTOM_TYPE(conv2d, MKLDNN, FP32);\n") + file(APPEND ${pybind_file} "USE_OP_DEVICE_KERNEL_WITH_CUSTOM_TYPE(conv2d, MKLDNN, S8);\n") + file(APPEND ${pybind_file} "USE_OP_DEVICE_KERNEL_WITH_CUSTOM_TYPE(conv2d, MKLDNN, U8);\n") + else() file(APPEND ${pybind_file} "USE_OP_DEVICE_KERNEL(${TARGET}, MKLDNN);\n") endif() diff --git a/paddle/fluid/API.spec b/paddle/fluid/API.spec index 1c3192b0193b8ba2452e959c983b23df458a652a..0b5e83efef6efc60f9f0476747aa107994c64051 100644 --- a/paddle/fluid/API.spec +++ b/paddle/fluid/API.spec @@ -1,474 +1,475 @@ -paddle.fluid.Program.__init__ ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None) -paddle.fluid.Program.block ArgSpec(args=['self', 'index'], varargs=None, keywords=None, defaults=None) -paddle.fluid.Program.clone ArgSpec(args=['self', 'for_test'], varargs=None, keywords=None, defaults=(False,)) -paddle.fluid.Program.current_block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None) -paddle.fluid.Program.global_block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None) -paddle.fluid.Program.list_vars ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None) -paddle.fluid.Program.parse_from_string ArgSpec(args=['binary_str'], varargs=None, keywords=None, defaults=None) -paddle.fluid.Program.to_string ArgSpec(args=['self', 'throw_on_error', 'with_details'], varargs=None, keywords=None, defaults=(False,)) -paddle.fluid.default_startup_program ArgSpec(args=[], varargs=None, keywords=None, defaults=None) -paddle.fluid.default_main_program ArgSpec(args=[], varargs=None, keywords=None, defaults=None) -paddle.fluid.program_guard ArgSpec(args=['main_program', 'startup_program'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.name_scope ArgSpec(args=['prefix'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.Executor.__init__ ArgSpec(args=['self', 'place'], varargs=None, keywords=None, defaults=None) -paddle.fluid.Executor.close ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None) -paddle.fluid.Executor.run ArgSpec(args=['self', 'program', 'feed', 'fetch_list', 'feed_var_name', 'fetch_var_name', 'scope', 'return_numpy', 'use_program_cache'], varargs=None, keywords=None, defaults=(None, None, None, 'feed', 'fetch', None, True, False)) -paddle.fluid.global_scope ArgSpec(args=[], varargs=None, keywords=None, defaults=None) -paddle.fluid.scope_guard ArgSpec(args=['scope'], varargs=None, keywords=None, defaults=None) -paddle.fluid.DistributeTranspiler.__init__ ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.DistributeTranspiler.get_pserver_program ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None) -paddle.fluid.DistributeTranspiler.get_pserver_programs ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None) -paddle.fluid.DistributeTranspiler.get_startup_program ArgSpec(args=['self', 'endpoint', 'pserver_program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None)) -paddle.fluid.DistributeTranspiler.get_trainer_program ArgSpec(args=['self', 'wait_port'], varargs=None, keywords=None, defaults=(True,)) -paddle.fluid.DistributeTranspiler.transpile ArgSpec(args=['self', 'trainer_id', 'program', 'pservers', 'trainers', 'sync_mode', 'startup_program', 'current_endpoint'], varargs=None, keywords=None, defaults=(None, '127.0.0.1:6174', 1, True, None, '127.0.0.1:6174')) -paddle.fluid.memory_optimize ArgSpec(args=['input_program', 'skip_opt_set', 'print_log', 'level', 'skip_grads'], varargs=None, keywords=None, defaults=(None, False, 0, False)) -paddle.fluid.release_memory ArgSpec(args=['input_program', 'skip_opt_set'], varargs=None, keywords=None, defaults=(None,)) +paddle.fluid.Program.__init__ (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.Program.block (ArgSpec(args=['self', 'index'], varargs=None, keywords=None, defaults=None), ('document', 'af5346376065ff4cf6832a8ac0ae0945')) +paddle.fluid.Program.clone (ArgSpec(args=['self', 'for_test'], varargs=None, keywords=None, defaults=(False,)), ('document', 'ebb7765b2962bd2be041d19720e49d0f')) +paddle.fluid.Program.current_block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '5e162d3bf8dd625703463d9e4be36adb')) +paddle.fluid.Program.global_block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'cfb7e05a002b2e64650778cabde7301c')) +paddle.fluid.Program.list_vars (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '1c8647b14fe57c7824b1c9562394dd3c')) +paddle.fluid.Program.parse_from_string (ArgSpec(args=['binary_str'], varargs=None, keywords=None, defaults=None), ('document', 'b6a7ffb239a30bf2ce58cfaca8d8b8d5')) +paddle.fluid.Program.to_string (ArgSpec(args=['self', 'throw_on_error', 'with_details'], varargs=None, keywords=None, defaults=(False,)), ('document', 'faec17e5a04af28e3776160e34504d15')) +paddle.fluid.default_startup_program (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', '99e5d53d92d82797093332719c9e3ccd')) +paddle.fluid.default_main_program (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', '5430f54ab4895f9f47db6bebbaf71659')) +paddle.fluid.program_guard (ArgSpec(args=['main_program', 'startup_program'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b54f403e57825a1592aece03afe3afb6')) +paddle.fluid.name_scope (ArgSpec(args=['prefix'], varargs=None, keywords=None, defaults=(None,)), ('document', '0ef753f5cec69fef9ae6ad8b867b33a2')) +paddle.fluid.Executor.__init__ (ArgSpec(args=['self', 'place'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.Executor.close (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '78e512cabeda9c7f42cb7c7e88967ae7')) +paddle.fluid.Executor.run (ArgSpec(args=['self', 'program', 'feed', 'fetch_list', 'feed_var_name', 'fetch_var_name', 'scope', 'return_numpy', 'use_program_cache'], varargs=None, keywords=None, defaults=(None, None, None, 'feed', 'fetch', None, True, False)), ('document', 'aba8093edebf2d5c869b735b92811e45')) +paddle.fluid.global_scope (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', 'e148d3ab1ed8edf3e928212a375959c0')) +paddle.fluid.scope_guard (ArgSpec(args=['scope'], varargs=None, keywords=None, defaults=None), ('document', 'b94d1f6bcc29c4fb58fc0058561250c2')) +paddle.fluid.DistributeTranspiler.__init__ (ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.DistributeTranspiler.get_pserver_program (ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None), ('document', '292ab72977afbe58e6a3bde175452680')) +paddle.fluid.DistributeTranspiler.get_pserver_programs (ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None), ('document', '78f4949aedf317666a89ca74b3748ba8')) +paddle.fluid.DistributeTranspiler.get_startup_program (ArgSpec(args=['self', 'endpoint', 'pserver_program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'd796fc0c8d51503b556fcf6dc15c4f0c')) +paddle.fluid.DistributeTranspiler.get_trainer_program (ArgSpec(args=['self', 'wait_port'], varargs=None, keywords=None, defaults=(True,)), ('document', '736330e31a7a54abccc0c7fd9119d9ff')) +paddle.fluid.DistributeTranspiler.transpile (ArgSpec(args=['self', 'trainer_id', 'program', 'pservers', 'trainers', 'sync_mode', 'startup_program', 'current_endpoint'], varargs=None, keywords=None, defaults=(None, '127.0.0.1:6174', 1, True, None, '127.0.0.1:6174')), ('document', '06ce55338dfe96311ad1078235ab3bf4')) +paddle.fluid.memory_optimize (ArgSpec(args=['input_program', 'skip_opt_set', 'print_log', 'level', 'skip_grads'], varargs=None, keywords=None, defaults=(None, False, 0, False)), ('document', 'eda17d0f1639bc6ca215cecf87f588a4')) +paddle.fluid.release_memory (ArgSpec(args=['input_program', 'skip_opt_set'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ac4114d3df16264f1946deb3a8434a6f')) paddle.fluid.DistributeTranspilerConfig.__init__ -paddle.fluid.ParallelExecutor.__init__ ArgSpec(args=['self', 'use_cuda', 'loss_name', 'main_program', 'share_vars_from', 'exec_strategy', 'build_strategy', 'num_trainers', 'trainer_id', 'scope'], varargs=None, keywords=None, defaults=(None, None, None, None, None, 1, 0, None)) -paddle.fluid.ParallelExecutor.run ArgSpec(args=['self', 'fetch_list', 'feed', 'feed_dict', 'return_numpy'], varargs=None, keywords=None, defaults=(None, None, True)) -paddle.fluid.create_lod_tensor ArgSpec(args=['data', 'recursive_seq_lens', 'place'], varargs=None, keywords=None, defaults=None) -paddle.fluid.create_random_int_lodtensor ArgSpec(args=['recursive_seq_lens', 'base_shape', 'place', 'low', 'high'], varargs=None, keywords=None, defaults=None) -paddle.fluid.DataFeedDesc.__init__ ArgSpec(args=['self', 'proto_file'], varargs=None, keywords=None, defaults=None) -paddle.fluid.DataFeedDesc.desc ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None) -paddle.fluid.DataFeedDesc.set_batch_size ArgSpec(args=['self', 'batch_size'], varargs=None, keywords=None, defaults=None) -paddle.fluid.DataFeedDesc.set_dense_slots ArgSpec(args=['self', 'dense_slots_name'], varargs=None, keywords=None, defaults=None) -paddle.fluid.DataFeedDesc.set_use_slots ArgSpec(args=['self', 'use_slots_name'], varargs=None, keywords=None, defaults=None) -paddle.fluid.AsyncExecutor.__init__ ArgSpec(args=['self', 'place', 'run_mode'], varargs=None, keywords=None, defaults=(None, '')) -paddle.fluid.AsyncExecutor.config_distributed_nodes ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None) -paddle.fluid.AsyncExecutor.download_data ArgSpec(args=['self', 'afs_path', 'local_path', 'fs_default_name', 'ugi', 'file_cnt', 'hadoop_home', 'process_num'], varargs=None, keywords=None, defaults=('$HADOOP_HOME', 12)) -paddle.fluid.AsyncExecutor.get_instance ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None) -paddle.fluid.AsyncExecutor.init_model ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None) -paddle.fluid.AsyncExecutor.init_server ArgSpec(args=['self', 'dist_desc'], varargs=None, keywords=None, defaults=None) -paddle.fluid.AsyncExecutor.init_worker ArgSpec(args=['self', 'dist_desc', 'startup_program'], varargs=None, keywords=None, defaults=None) -paddle.fluid.AsyncExecutor.run ArgSpec(args=['self', 'program', 'data_feed', 'filelist', 'thread_num', 'fetch', 'mode', 'debug'], varargs=None, keywords=None, defaults=('', False)) -paddle.fluid.AsyncExecutor.save_model ArgSpec(args=['self', 'save_path'], varargs=None, keywords=None, defaults=None) -paddle.fluid.AsyncExecutor.stop ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None) -paddle.fluid.CompiledProgram.__init__ ArgSpec(args=['self', 'program_or_graph'], varargs=None, keywords=None, defaults=None) -paddle.fluid.CompiledProgram.with_data_parallel ArgSpec(args=['self', 'loss_name', 'build_strategy', 'exec_strategy', 'share_vars_from'], varargs=None, keywords=None, defaults=(None, None, None, None)) -paddle.fluid.CompiledProgram.with_inference_optimize ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=None) +paddle.fluid.ParallelExecutor.__init__ (ArgSpec(args=['self', 'use_cuda', 'loss_name', 'main_program', 'share_vars_from', 'exec_strategy', 'build_strategy', 'num_trainers', 'trainer_id', 'scope'], varargs=None, keywords=None, defaults=(None, None, None, None, None, 1, 0, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.ParallelExecutor.run (ArgSpec(args=['self', 'fetch_list', 'feed', 'feed_dict', 'return_numpy'], varargs=None, keywords=None, defaults=(None, None, True)), ('document', '2cb4bd74481861345c70228a0f57620c')) +paddle.fluid.create_lod_tensor (ArgSpec(args=['data', 'recursive_seq_lens', 'place'], varargs=None, keywords=None, defaults=None), ('document', '8e7bb21e83ff4604f5b379672e285b94')) +paddle.fluid.create_random_int_lodtensor (ArgSpec(args=['recursive_seq_lens', 'base_shape', 'place', 'low', 'high'], varargs=None, keywords=None, defaults=None), ('document', '368f638b99f1dfe59e9b02aa6f077752')) +paddle.fluid.DataFeedDesc.__init__ (ArgSpec(args=['self', 'proto_file'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.DataFeedDesc.desc (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '4294493e31c4bc9fc4bd48753044235f')) +paddle.fluid.DataFeedDesc.set_batch_size (ArgSpec(args=['self', 'batch_size'], varargs=None, keywords=None, defaults=None), ('document', '8d9f44601e0a99dd431f14fd9250cd21')) +paddle.fluid.DataFeedDesc.set_dense_slots (ArgSpec(args=['self', 'dense_slots_name'], varargs=None, keywords=None, defaults=None), ('document', 'eb894b464bbcd1b4bc8038398954f766')) +paddle.fluid.DataFeedDesc.set_use_slots (ArgSpec(args=['self', 'use_slots_name'], varargs=None, keywords=None, defaults=None), ('document', '415c56600ce4e198c071cad01409a690')) +paddle.fluid.AsyncExecutor.__init__ (ArgSpec(args=['self', 'place', 'run_mode'], varargs=None, keywords=None, defaults=(None, '')), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.AsyncExecutor.config_distributed_nodes (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '4810dbe1870452f16b3c60b6c5fd1459')) +paddle.fluid.AsyncExecutor.download_data (ArgSpec(args=['self', 'afs_path', 'local_path', 'fs_default_name', 'ugi', 'file_cnt', 'hadoop_home', 'process_num'], varargs=None, keywords=None, defaults=('$HADOOP_HOME', 12)), ('document', '799a2066cc26819f1ed31f47c15ad083')) +paddle.fluid.AsyncExecutor.get_instance (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'f8688f76a2db1243c7097a60c507b182')) +paddle.fluid.AsyncExecutor.init_model (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '504f39be2007404a17e5cabea1256c7d')) +paddle.fluid.AsyncExecutor.init_server (ArgSpec(args=['self', 'dist_desc'], varargs=None, keywords=None, defaults=None), ('document', 'c403ab46c5d3ef25c0f7e94ae75dcb68')) +paddle.fluid.AsyncExecutor.init_worker (ArgSpec(args=['self', 'dist_desc', 'startup_program'], varargs=None, keywords=None, defaults=None), ('document', 'dcf08f4bf2f3282acf11391f5d39c536')) +paddle.fluid.AsyncExecutor.run (ArgSpec(args=['self', 'program', 'data_feed', 'filelist', 'thread_num', 'fetch', 'mode', 'debug'], varargs=None, keywords=None, defaults=('', False)), ('document', '848fc53484e8326f6325feea87fe955c')) +paddle.fluid.AsyncExecutor.save_model (ArgSpec(args=['self', 'save_path'], varargs=None, keywords=None, defaults=None), ('document', 'c8ac0dfcb3b187aba25d03af7fea56b2')) +paddle.fluid.AsyncExecutor.stop (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '5f23d043607bb5d55e466ec3f578e093')) +paddle.fluid.CompiledProgram.__init__ (ArgSpec(args=['self', 'program_or_graph'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.CompiledProgram.with_data_parallel (ArgSpec(args=['self', 'loss_name', 'build_strategy', 'exec_strategy', 'share_vars_from'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'e1af7fd53cf868554f312779fc803864')) +paddle.fluid.CompiledProgram.with_inference_optimize (ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=None), ('document', '9e5b009d850191a010e859189c127fd8')) paddle.fluid.ExecutionStrategy.__init__ __init__(self: paddle.fluid.core.ParallelExecutor.ExecutionStrategy) -> None paddle.fluid.BuildStrategy.GradientScaleStrategy.__init__ __init__(self: paddle.fluid.core.ParallelExecutor.BuildStrategy.GradientScaleStrategy, arg0: int) -> None paddle.fluid.BuildStrategy.ReduceStrategy.__init__ __init__(self: paddle.fluid.core.ParallelExecutor.BuildStrategy.ReduceStrategy, arg0: int) -> None paddle.fluid.BuildStrategy.__init__ __init__(self: paddle.fluid.core.ParallelExecutor.BuildStrategy) -> None -paddle.fluid.io.save_vars ArgSpec(args=['executor', 'dirname', 'main_program', 'vars', 'predicate', 'filename'], varargs=None, keywords=None, defaults=(None, None, None, None)) -paddle.fluid.io.save_params ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None)) -paddle.fluid.io.save_persistables ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None)) -paddle.fluid.io.load_vars ArgSpec(args=['executor', 'dirname', 'main_program', 'vars', 'predicate', 'filename'], varargs=None, keywords=None, defaults=(None, None, None, None)) -paddle.fluid.io.load_params ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None)) -paddle.fluid.io.load_persistables ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None)) -paddle.fluid.io.save_inference_model ArgSpec(args=['dirname', 'feeded_var_names', 'target_vars', 'executor', 'main_program', 'model_filename', 'params_filename', 'export_for_deployment'], varargs=None, keywords=None, defaults=(None, None, None, True)) -paddle.fluid.io.load_inference_model ArgSpec(args=['dirname', 'executor', 'model_filename', 'params_filename', 'pserver_endpoints'], varargs=None, keywords=None, defaults=(None, None, None)) -paddle.fluid.initializer.ConstantInitializer.__init__ ArgSpec(args=['self', 'value', 'force_cpu'], varargs=None, keywords=None, defaults=(0.0, False)) -paddle.fluid.initializer.UniformInitializer.__init__ ArgSpec(args=['self', 'low', 'high', 'seed'], varargs=None, keywords=None, defaults=(-1.0, 1.0, 0)) -paddle.fluid.initializer.NormalInitializer.__init__ ArgSpec(args=['self', 'loc', 'scale', 'seed'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0)) -paddle.fluid.initializer.TruncatedNormalInitializer.__init__ ArgSpec(args=['self', 'loc', 'scale', 'seed'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0)) -paddle.fluid.initializer.XavierInitializer.__init__ ArgSpec(args=['self', 'uniform', 'fan_in', 'fan_out', 'seed'], varargs=None, keywords=None, defaults=(True, None, None, 0)) -paddle.fluid.initializer.BilinearInitializer.__init__ ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None) -paddle.fluid.initializer.MSRAInitializer.__init__ ArgSpec(args=['self', 'uniform', 'fan_in', 'seed'], varargs=None, keywords=None, defaults=(True, None, 0)) -paddle.fluid.initializer.force_init_on_cpu ArgSpec(args=[], varargs=None, keywords=None, defaults=None) -paddle.fluid.initializer.init_on_cpu ArgSpec(args=[], varargs=None, keywords=None, defaults=None) -paddle.fluid.initializer.NumpyArrayInitializer.__init__ ArgSpec(args=['self', 'value'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.fc ArgSpec(args=['input', 'size', 'num_flatten_dims', 'param_attr', 'bias_attr', 'act', 'is_test', 'name'], varargs=None, keywords=None, defaults=(1, None, None, None, False, None)) -paddle.fluid.layers.embedding ArgSpec(args=['input', 'size', 'is_sparse', 'is_distributed', 'padding_idx', 'param_attr', 'dtype'], varargs=None, keywords=None, defaults=(False, False, None, None, 'float32')) -paddle.fluid.layers.dynamic_lstm ArgSpec(args=['input', 'size', 'h_0', 'c_0', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'float32', None)) -paddle.fluid.layers.dynamic_lstmp ArgSpec(args=['input', 'size', 'proj_size', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'proj_activation', 'dtype', 'name', 'h_0', 'c_0', 'cell_clip', 'proj_clip'], varargs=None, keywords=None, defaults=(None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'tanh', 'float32', None, None, None, None, None)) -paddle.fluid.layers.dynamic_gru ArgSpec(args=['input', 'size', 'param_attr', 'bias_attr', 'is_reverse', 'gate_activation', 'candidate_activation', 'h_0', 'origin_mode'], varargs=None, keywords=None, defaults=(None, None, False, 'sigmoid', 'tanh', None, False)) -paddle.fluid.layers.gru_unit ArgSpec(args=['input', 'hidden', 'size', 'param_attr', 'bias_attr', 'activation', 'gate_activation', 'origin_mode'], varargs=None, keywords=None, defaults=(None, None, 'tanh', 'sigmoid', False)) -paddle.fluid.layers.linear_chain_crf ArgSpec(args=['input', 'label', 'param_attr'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.crf_decoding ArgSpec(args=['input', 'param_attr', 'label'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.cos_sim ArgSpec(args=['X', 'Y'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.cross_entropy ArgSpec(args=['input', 'label', 'soft_label', 'ignore_index'], varargs=None, keywords=None, defaults=(False, -100)) -paddle.fluid.layers.bpr_loss ArgSpec(args=['input', 'label', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.square_error_cost ArgSpec(args=['input', 'label'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.chunk_eval ArgSpec(args=['input', 'label', 'chunk_scheme', 'num_chunk_types', 'excluded_chunk_types'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.sequence_conv ArgSpec(args=['input', 'num_filters', 'filter_size', 'filter_stride', 'padding', 'bias_attr', 'param_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(3, 1, None, None, None, None, None)) -paddle.fluid.layers.conv2d ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None)) -paddle.fluid.layers.conv3d ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None)) -paddle.fluid.layers.sequence_pool ArgSpec(args=['input', 'pool_type', 'is_test'], varargs=None, keywords=None, defaults=(False,)) -paddle.fluid.layers.sequence_softmax ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(False, None)) -paddle.fluid.layers.softmax ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(True, None)) -paddle.fluid.layers.pool2d ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True)) -paddle.fluid.layers.pool3d ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True)) -paddle.fluid.layers.adaptive_pool2d ArgSpec(args=['input', 'pool_size', 'pool_type', 'require_index', 'name'], varargs=None, keywords=None, defaults=('max', False, None)) -paddle.fluid.layers.adaptive_pool3d ArgSpec(args=['input', 'pool_size', 'pool_type', 'require_index', 'name'], varargs=None, keywords=None, defaults=('max', False, None)) -paddle.fluid.layers.batch_norm ArgSpec(args=['input', 'act', 'is_test', 'momentum', 'epsilon', 'param_attr', 'bias_attr', 'data_layout', 'in_place', 'name', 'moving_mean_name', 'moving_variance_name', 'do_model_average_for_mean_and_var', 'fuse_with_relu', 'use_global_stats'], varargs=None, keywords=None, defaults=(None, False, 0.9, 1e-05, None, None, 'NCHW', False, None, None, None, False, False, False)) -paddle.fluid.layers.data_norm ArgSpec(args=['input', 'act', 'epsilon', 'param_attr', 'data_layout', 'in_place', 'use_mkldnn', 'name', 'moving_mean_name', 'moving_variance_name', 'do_model_average_for_mean_and_var'], varargs=None, keywords=None, defaults=(None, 1e-05, None, 'NCHW', False, False, None, None, None, False)) -paddle.fluid.layers.beam_search_decode ArgSpec(args=['ids', 'scores', 'beam_size', 'end_id', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.conv2d_transpose ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None)) -paddle.fluid.layers.conv3d_transpose ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None)) -paddle.fluid.layers.sequence_expand ArgSpec(args=['x', 'y', 'ref_level', 'name'], varargs=None, keywords=None, defaults=(-1, None)) -paddle.fluid.layers.sequence_expand_as ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.sequence_pad ArgSpec(args=['x', 'pad_value', 'maxlen', 'name'], varargs=None, keywords=None, defaults=(None, None)) -paddle.fluid.layers.sequence_unpad ArgSpec(args=['x', 'length', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.lstm_unit ArgSpec(args=['x_t', 'hidden_t_prev', 'cell_t_prev', 'forget_bias', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(0.0, None, None, None)) -paddle.fluid.layers.reduce_sum ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)) -paddle.fluid.layers.reduce_mean ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)) -paddle.fluid.layers.reduce_max ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)) -paddle.fluid.layers.reduce_min ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)) -paddle.fluid.layers.reduce_prod ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)) -paddle.fluid.layers.sequence_first_step ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.sequence_last_step ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.sequence_slice ArgSpec(args=['input', 'offset', 'length', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.dropout ArgSpec(args=['x', 'dropout_prob', 'is_test', 'seed', 'name', 'dropout_implementation'], varargs=None, keywords=None, defaults=(False, None, None, 'downgrade_in_infer')) -paddle.fluid.layers.split ArgSpec(args=['input', 'num_or_sections', 'dim', 'name'], varargs=None, keywords=None, defaults=(-1, None)) -paddle.fluid.layers.ctc_greedy_decoder ArgSpec(args=['input', 'blank', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.edit_distance ArgSpec(args=['input', 'label', 'normalized', 'ignored_tokens'], varargs=None, keywords=None, defaults=(True, None)) -paddle.fluid.layers.l2_normalize ArgSpec(args=['x', 'axis', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(1e-12, None)) -paddle.fluid.layers.matmul ArgSpec(args=['x', 'y', 'transpose_x', 'transpose_y', 'alpha', 'name'], varargs=None, keywords=None, defaults=(False, False, 1.0, None)) -paddle.fluid.layers.topk ArgSpec(args=['input', 'k', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.warpctc ArgSpec(args=['input', 'label', 'blank', 'norm_by_times', 'use_cudnn'], varargs=None, keywords=None, defaults=(0, False, False)) -paddle.fluid.layers.sequence_reshape ArgSpec(args=['input', 'new_dim'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.transpose ArgSpec(args=['x', 'perm', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.im2sequence ArgSpec(args=['input', 'filter_size', 'stride', 'padding', 'input_image_size', 'out_stride', 'name'], varargs=None, keywords=None, defaults=(1, 1, 0, None, 1, None)) -paddle.fluid.layers.nce ArgSpec(args=['input', 'label', 'num_total_classes', 'sample_weight', 'param_attr', 'bias_attr', 'num_neg_samples', 'name', 'sampler', 'custom_dist', 'seed', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, 'uniform', None, 0, False)) -paddle.fluid.layers.sampled_softmax_with_cross_entropy ArgSpec(args=['logits', 'label', 'num_samples', 'num_true', 'remove_accidental_hits', 'use_customized_samples', 'customized_samples', 'customized_probabilities', 'seed'], varargs=None, keywords=None, defaults=(1, True, False, None, None, 0)) -paddle.fluid.layers.hsigmoid ArgSpec(args=['input', 'label', 'num_classes', 'param_attr', 'bias_attr', 'name', 'path_table', 'path_code', 'is_custom', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, False, False)) -paddle.fluid.layers.beam_search ArgSpec(args=['pre_ids', 'pre_scores', 'ids', 'scores', 'beam_size', 'end_id', 'level', 'is_accumulated', 'name', 'return_parent_idx'], varargs=None, keywords=None, defaults=(0, True, None, False)) -paddle.fluid.layers.row_conv ArgSpec(args=['input', 'future_context_size', 'param_attr', 'act'], varargs=None, keywords=None, defaults=(None, None)) -paddle.fluid.layers.multiplex ArgSpec(args=['inputs', 'index'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.layer_norm ArgSpec(args=['input', 'scale', 'shift', 'begin_norm_axis', 'epsilon', 'param_attr', 'bias_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(True, True, 1, 1e-05, None, None, None, None)) -paddle.fluid.layers.group_norm ArgSpec(args=['input', 'groups', 'epsilon', 'param_attr', 'bias_attr', 'act', 'data_layout', 'name'], varargs=None, keywords=None, defaults=(1e-05, None, None, None, 'NCHW', None)) -paddle.fluid.layers.softmax_with_cross_entropy ArgSpec(args=['logits', 'label', 'soft_label', 'ignore_index', 'numeric_stable_mode', 'return_softmax'], varargs=None, keywords=None, defaults=(False, -100, False, False)) -paddle.fluid.layers.smooth_l1 ArgSpec(args=['x', 'y', 'inside_weight', 'outside_weight', 'sigma'], varargs=None, keywords=None, defaults=(None, None, None)) -paddle.fluid.layers.one_hot ArgSpec(args=['input', 'depth'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.autoincreased_step_counter ArgSpec(args=['counter_name', 'begin', 'step'], varargs=None, keywords=None, defaults=(None, 1, 1)) -paddle.fluid.layers.reshape ArgSpec(args=['x', 'shape', 'actual_shape', 'act', 'inplace', 'name'], varargs=None, keywords=None, defaults=(None, None, False, None)) -paddle.fluid.layers.squeeze ArgSpec(args=['input', 'axes', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.unsqueeze ArgSpec(args=['input', 'axes', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.lod_reset ArgSpec(args=['x', 'y', 'target_lod'], varargs=None, keywords=None, defaults=(None, None)) -paddle.fluid.layers.lrn ArgSpec(args=['input', 'n', 'k', 'alpha', 'beta', 'name'], varargs=None, keywords=None, defaults=(5, 1.0, 0.0001, 0.75, None)) -paddle.fluid.layers.pad ArgSpec(args=['x', 'paddings', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0.0, None)) -paddle.fluid.layers.pad_constant_like ArgSpec(args=['x', 'y', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0.0, None)) -paddle.fluid.layers.label_smooth ArgSpec(args=['label', 'prior_dist', 'epsilon', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, 0.1, 'float32', None)) -paddle.fluid.layers.roi_pool ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1, 1, 1.0)) -paddle.fluid.layers.roi_align ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale', 'sampling_ratio', 'name'], varargs=None, keywords=None, defaults=(1, 1, 1.0, -1, None)) -paddle.fluid.layers.dice_loss ArgSpec(args=['input', 'label', 'epsilon'], varargs=None, keywords=None, defaults=(1e-05,)) -paddle.fluid.layers.image_resize ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'resample', 'actual_shape', 'align_corners', 'align_mode'], varargs=None, keywords=None, defaults=(None, None, None, 'BILINEAR', None, True, 1)) -paddle.fluid.layers.image_resize_short ArgSpec(args=['input', 'out_short_len', 'resample'], varargs=None, keywords=None, defaults=('BILINEAR',)) -paddle.fluid.layers.resize_bilinear ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners', 'align_mode'], varargs=None, keywords=None, defaults=(None, None, None, None, True, 1)) -paddle.fluid.layers.resize_nearest ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners'], varargs=None, keywords=None, defaults=(None, None, None, None, True)) -paddle.fluid.layers.gather ArgSpec(args=['input', 'index'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.scatter ArgSpec(args=['input', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.sequence_scatter ArgSpec(args=['input', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.random_crop ArgSpec(args=['x', 'shape', 'seed'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.mean_iou ArgSpec(args=['input', 'label', 'num_classes'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.relu ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.selu ArgSpec(args=['x', 'scale', 'alpha', 'name'], varargs=None, keywords=None, defaults=(None, None, None)) -paddle.fluid.layers.log ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.crop ArgSpec(args=['x', 'shape', 'offsets', 'name'], varargs=None, keywords=None, defaults=(None, None, None)) -paddle.fluid.layers.rank_loss ArgSpec(args=['label', 'left', 'right', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.margin_rank_loss ArgSpec(args=['label', 'left', 'right', 'margin', 'name'], varargs=None, keywords=None, defaults=(0.1, None)) -paddle.fluid.layers.elu ArgSpec(args=['x', 'alpha', 'name'], varargs=None, keywords=None, defaults=(1.0, None)) -paddle.fluid.layers.relu6 ArgSpec(args=['x', 'threshold', 'name'], varargs=None, keywords=None, defaults=(6.0, None)) -paddle.fluid.layers.pow ArgSpec(args=['x', 'factor', 'name'], varargs=None, keywords=None, defaults=(1.0, None)) -paddle.fluid.layers.stanh ArgSpec(args=['x', 'scale_a', 'scale_b', 'name'], varargs=None, keywords=None, defaults=(0.6666666666666666, 1.7159, None)) -paddle.fluid.layers.hard_sigmoid ArgSpec(args=['x', 'slope', 'offset', 'name'], varargs=None, keywords=None, defaults=(0.2, 0.5, None)) -paddle.fluid.layers.swish ArgSpec(args=['x', 'beta', 'name'], varargs=None, keywords=None, defaults=(1.0, None)) -paddle.fluid.layers.prelu ArgSpec(args=['x', 'mode', 'param_attr', 'name'], varargs=None, keywords=None, defaults=(None, None)) -paddle.fluid.layers.brelu ArgSpec(args=['x', 't_min', 't_max', 'name'], varargs=None, keywords=None, defaults=(0.0, 24.0, None)) -paddle.fluid.layers.leaky_relu ArgSpec(args=['x', 'alpha', 'name'], varargs=None, keywords=None, defaults=(0.02, None)) -paddle.fluid.layers.soft_relu ArgSpec(args=['x', 'threshold', 'name'], varargs=None, keywords=None, defaults=(40.0, None)) -paddle.fluid.layers.flatten ArgSpec(args=['x', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None)) -paddle.fluid.layers.sequence_mask ArgSpec(args=['x', 'maxlen', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, 'int64', None)) -paddle.fluid.layers.stack ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=(0,)) -paddle.fluid.layers.pad2d ArgSpec(args=['input', 'paddings', 'mode', 'pad_value', 'data_format', 'name'], varargs=None, keywords=None, defaults=([0, 0, 0, 0], 'constant', 0.0, 'NCHW', None)) -paddle.fluid.layers.unstack ArgSpec(args=['x', 'axis', 'num'], varargs=None, keywords=None, defaults=(0, None)) -paddle.fluid.layers.sequence_enumerate ArgSpec(args=['input', 'win_size', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0, None)) -paddle.fluid.layers.expand ArgSpec(args=['x', 'expand_times', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.sequence_concat ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.scale ArgSpec(args=['x', 'scale', 'bias', 'bias_after_scale', 'act', 'name'], varargs=None, keywords=None, defaults=(1.0, 0.0, True, None, None)) -paddle.fluid.layers.elementwise_add ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)) -paddle.fluid.layers.elementwise_div ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)) -paddle.fluid.layers.elementwise_sub ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)) -paddle.fluid.layers.elementwise_mul ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)) -paddle.fluid.layers.elementwise_max ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)) -paddle.fluid.layers.elementwise_min ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)) -paddle.fluid.layers.elementwise_pow ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)) -paddle.fluid.layers.uniform_random_batch_size_like ArgSpec(args=['input', 'shape', 'dtype', 'input_dim_idx', 'output_dim_idx', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', 0, 0, -1.0, 1.0, 0)) -paddle.fluid.layers.gaussian_random ArgSpec(args=['shape', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')) -paddle.fluid.layers.sampling_id ArgSpec(args=['x', 'min', 'max', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')) -paddle.fluid.layers.gaussian_random_batch_size_like ArgSpec(args=['input', 'shape', 'input_dim_idx', 'output_dim_idx', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0, 0, 0.0, 1.0, 0, 'float32')) -paddle.fluid.layers.sum ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.slice ArgSpec(args=['input', 'axes', 'starts', 'ends'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.shape ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.logical_and ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None)) -paddle.fluid.layers.logical_or ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None)) -paddle.fluid.layers.logical_xor ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None)) -paddle.fluid.layers.logical_not ArgSpec(args=['x', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None)) -paddle.fluid.layers.clip ArgSpec(args=['x', 'min', 'max', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.clip_by_norm ArgSpec(args=['x', 'max_norm', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.mean ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.mul ArgSpec(args=['x', 'y', 'x_num_col_dims', 'y_num_col_dims', 'name'], varargs=None, keywords=None, defaults=(1, 1, None)) -paddle.fluid.layers.sigmoid_cross_entropy_with_logits ArgSpec(args=['x', 'label', 'ignore_index', 'name', 'normalize'], varargs=None, keywords=None, defaults=(-100, None, False)) -paddle.fluid.layers.maxout ArgSpec(args=['x', 'groups', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.space_to_depth ArgSpec(args=['x', 'blocksize', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.affine_grid ArgSpec(args=['theta', 'out_shape', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.sequence_reverse ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.affine_channel ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None)) -paddle.fluid.layers.similarity_focus ArgSpec(args=['input', 'axis', 'indexes', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.hash ArgSpec(args=['input', 'hash_size', 'num_hash', 'name'], varargs=None, keywords=None, defaults=(1, None)) -paddle.fluid.layers.grid_sampler ArgSpec(args=['x', 'grid', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.log_loss ArgSpec(args=['input', 'label', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(0.0001, None)) -paddle.fluid.layers.add_position_encoding ArgSpec(args=['input', 'alpha', 'beta', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.bilinear_tensor_product ArgSpec(args=['x', 'y', 'size', 'act', 'name', 'param_attr', 'bias_attr'], varargs=None, keywords=None, defaults=(None, None, None, None)) -paddle.fluid.layers.merge_selected_rows ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.get_tensor_from_selected_rows ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.lstm ArgSpec(args=['input', 'init_h', 'init_c', 'max_len', 'hidden_size', 'num_layers', 'dropout_prob', 'is_bidirec', 'is_test', 'name', 'default_initializer', 'seed'], varargs=None, keywords=None, defaults=(0.0, False, False, None, None, -1)) -paddle.fluid.layers.shuffle_channel ArgSpec(args=['x', 'group', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.py_func ArgSpec(args=['func', 'x', 'out', 'backward_func', 'skip_vars_in_backward_input'], varargs=None, keywords=None, defaults=(None, None)) -paddle.fluid.layers.psroi_pool ArgSpec(args=['input', 'rois', 'output_channels', 'spatial_scale', 'pooled_height', 'pooled_width', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.teacher_student_sigmoid_loss ArgSpec(args=['input', 'label', 'soft_max_up_bound', 'soft_max_lower_bound'], varargs=None, keywords=None, defaults=(15.0, -15.0)) -paddle.fluid.layers.huber_loss ArgSpec(args=['input', 'label', 'delta'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.tree_conv ArgSpec(args=['nodes_vector', 'edge_set', 'output_size', 'num_filters', 'max_depth', 'act', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(1, 2, 'tanh', None, None, None)) -paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True)) -paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None)) -paddle.fluid.layers.read_file ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.shuffle ArgSpec(args=['reader', 'buffer_size'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.batch ArgSpec(args=['reader', 'batch_size'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.double_buffer ArgSpec(args=['reader', 'place', 'name'], varargs=None, keywords=None, defaults=(None, None)) -paddle.fluid.layers.random_data_generator ArgSpec(args=['low', 'high', 'shapes', 'lod_levels', 'for_parallel'], varargs=None, keywords=None, defaults=(True,)) -paddle.fluid.layers.py_reader ArgSpec(args=['capacity', 'shapes', 'dtypes', 'lod_levels', 'name', 'use_double_buffer'], varargs=None, keywords=None, defaults=(None, None, True)) -paddle.fluid.layers.create_py_reader_by_data ArgSpec(args=['capacity', 'feed_list', 'name', 'use_double_buffer'], varargs=None, keywords=None, defaults=(None, True)) -paddle.fluid.layers.Preprocessor.__init__ ArgSpec(args=['self', 'reader', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.Preprocessor.block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.Preprocessor.inputs ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.Preprocessor.outputs ArgSpec(args=['self'], varargs='outs', keywords=None, defaults=None) -paddle.fluid.layers.load ArgSpec(args=['out', 'file_path', 'load_as_fp16'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.create_tensor ArgSpec(args=['dtype', 'name', 'persistable'], varargs=None, keywords=None, defaults=(None, False)) -paddle.fluid.layers.create_parameter ArgSpec(args=['shape', 'dtype', 'name', 'attr', 'is_bias', 'default_initializer'], varargs=None, keywords=None, defaults=(None, None, False, None)) -paddle.fluid.layers.create_global_var ArgSpec(args=['shape', 'value', 'dtype', 'persistable', 'force_cpu', 'name'], varargs=None, keywords=None, defaults=(False, False, None)) -paddle.fluid.layers.cast ArgSpec(args=['x', 'dtype'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.tensor_array_to_tensor ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None)) -paddle.fluid.layers.concat ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(0, None)) -paddle.fluid.layers.sums ArgSpec(args=['input', 'out'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.assign ArgSpec(args=['input', 'output'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.fill_constant_batch_size_like ArgSpec(args=['input', 'shape', 'dtype', 'value', 'input_dim_idx', 'output_dim_idx'], varargs=None, keywords=None, defaults=(0, 0)) -paddle.fluid.layers.fill_constant ArgSpec(args=['shape', 'dtype', 'value', 'force_cpu', 'out'], varargs=None, keywords=None, defaults=(False, None)) -paddle.fluid.layers.argmin ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=(0,)) -paddle.fluid.layers.argmax ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=(0,)) -paddle.fluid.layers.argsort ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(-1, None)) -paddle.fluid.layers.ones ArgSpec(args=['shape', 'dtype', 'force_cpu'], varargs=None, keywords=None, defaults=(False,)) -paddle.fluid.layers.zeros ArgSpec(args=['shape', 'dtype', 'force_cpu'], varargs=None, keywords=None, defaults=(False,)) -paddle.fluid.layers.reverse ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.has_inf ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.has_nan ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.isfinite ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.While.__init__ ArgSpec(args=['self', 'cond', 'is_test', 'name'], varargs=None, keywords=None, defaults=(False, None)) -paddle.fluid.layers.While.block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.Switch.__init__ ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.Switch.case ArgSpec(args=['self', 'condition'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.Switch.default ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.increment ArgSpec(args=['x', 'value', 'in_place'], varargs=None, keywords=None, defaults=(1.0, True)) -paddle.fluid.layers.array_write ArgSpec(args=['x', 'i', 'array'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.create_array ArgSpec(args=['dtype'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.less_than ArgSpec(args=['x', 'y', 'force_cpu', 'cond'], varargs=None, keywords='ignored', defaults=(None, None)) -paddle.fluid.layers.equal ArgSpec(args=['x', 'y', 'cond'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.array_read ArgSpec(args=['array', 'i'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.array_length ArgSpec(args=['array'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.IfElse.__init__ ArgSpec(args=['self', 'cond', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.IfElse.false_block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.IfElse.input ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.IfElse.output ArgSpec(args=['self'], varargs='outs', keywords=None, defaults=None) -paddle.fluid.layers.IfElse.true_block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.DynamicRNN.__init__ ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.DynamicRNN.block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.DynamicRNN.memory ArgSpec(args=['self', 'init', 'shape', 'value', 'need_reorder', 'dtype'], varargs=None, keywords=None, defaults=(None, None, 0.0, False, 'float32')) -paddle.fluid.layers.DynamicRNN.output ArgSpec(args=['self'], varargs='outputs', keywords=None, defaults=None) -paddle.fluid.layers.DynamicRNN.static_input ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.DynamicRNN.step_input ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.DynamicRNN.update_memory ArgSpec(args=['self', 'ex_mem', 'new_mem'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.StaticRNN.__init__ ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.StaticRNN.memory ArgSpec(args=['self', 'init', 'shape', 'batch_ref', 'init_value', 'init_batch_dim_idx', 'ref_batch_dim_idx'], varargs=None, keywords=None, defaults=(None, None, None, 0.0, 0, 1)) -paddle.fluid.layers.StaticRNN.output ArgSpec(args=['self'], varargs='outputs', keywords=None, defaults=None) -paddle.fluid.layers.StaticRNN.step ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.StaticRNN.step_input ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.StaticRNN.step_output ArgSpec(args=['self', 'o'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.StaticRNN.update_memory ArgSpec(args=['self', 'mem', 'var'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.reorder_lod_tensor_by_rank ArgSpec(args=['x', 'rank_table'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.Print ArgSpec(args=['input', 'first_n', 'message', 'summarize', 'print_tensor_name', 'print_tensor_type', 'print_tensor_shape', 'print_tensor_lod', 'print_phase'], varargs=None, keywords=None, defaults=(-1, None, -1, True, True, True, True, 'both')) -paddle.fluid.layers.is_empty ArgSpec(args=['x', 'cond'], varargs=None, keywords='ignored', defaults=(None,)) -paddle.fluid.layers.sigmoid ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.logsigmoid ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.exp ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.tanh ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.tanh_shrink ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.softshrink ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.sqrt ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.abs ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.ceil ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.floor ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.cos ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.sin ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.round ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.reciprocal ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.square ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.softplus ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.softsign ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.uniform_random ArgSpec(args=['shape', 'dtype', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', -1.0, 1.0, 0)) -paddle.fluid.layers.hard_shrink ArgSpec(args=['x', 'threshold'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.cumsum ArgSpec(args=['x', 'axis', 'exclusive', 'reverse'], varargs=None, keywords=None, defaults=(None, None, None)) -paddle.fluid.layers.thresholded_relu ArgSpec(args=['x', 'threshold'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.prior_box ArgSpec(args=['input', 'image', 'min_sizes', 'max_sizes', 'aspect_ratios', 'variance', 'flip', 'clip', 'steps', 'offset', 'name', 'min_max_aspect_ratios_order'], varargs=None, keywords=None, defaults=(None, [1.0], [0.1, 0.1, 0.2, 0.2], False, False, [0.0, 0.0], 0.5, None, False)) -paddle.fluid.layers.density_prior_box ArgSpec(args=['input', 'image', 'densities', 'fixed_sizes', 'fixed_ratios', 'variance', 'clip', 'steps', 'offset', 'flatten_to_2d', 'name'], varargs=None, keywords=None, defaults=(None, None, None, [0.1, 0.1, 0.2, 0.2], False, [0.0, 0.0], 0.5, False, None)) -paddle.fluid.layers.multi_box_head ArgSpec(args=['inputs', 'image', 'base_size', 'num_classes', 'aspect_ratios', 'min_ratio', 'max_ratio', 'min_sizes', 'max_sizes', 'steps', 'step_w', 'step_h', 'offset', 'variance', 'flip', 'clip', 'kernel_size', 'pad', 'stride', 'name', 'min_max_aspect_ratios_order'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None, 0.5, [0.1, 0.1, 0.2, 0.2], True, False, 1, 0, 1, None, False)) -paddle.fluid.layers.bipartite_match ArgSpec(args=['dist_matrix', 'match_type', 'dist_threshold', 'name'], varargs=None, keywords=None, defaults=(None, None, None)) -paddle.fluid.layers.target_assign ArgSpec(args=['input', 'matched_indices', 'negative_indices', 'mismatch_value', 'name'], varargs=None, keywords=None, defaults=(None, None, None)) -paddle.fluid.layers.detection_output ArgSpec(args=['loc', 'scores', 'prior_box', 'prior_box_var', 'background_label', 'nms_threshold', 'nms_top_k', 'keep_top_k', 'score_threshold', 'nms_eta'], varargs=None, keywords=None, defaults=(0, 0.3, 400, 200, 0.01, 1.0)) -paddle.fluid.layers.ssd_loss ArgSpec(args=['location', 'confidence', 'gt_box', 'gt_label', 'prior_box', 'prior_box_var', 'background_label', 'overlap_threshold', 'neg_pos_ratio', 'neg_overlap', 'loc_loss_weight', 'conf_loss_weight', 'match_type', 'mining_type', 'normalize', 'sample_size'], varargs=None, keywords=None, defaults=(None, 0, 0.5, 3.0, 0.5, 1.0, 1.0, 'per_prediction', 'max_negative', True, None)) -paddle.fluid.layers.detection_map ArgSpec(args=['detect_res', 'label', 'class_num', 'background_label', 'overlap_threshold', 'evaluate_difficult', 'has_state', 'input_states', 'out_states', 'ap_version'], varargs=None, keywords=None, defaults=(0, 0.3, True, None, None, None, 'integral')) -paddle.fluid.layers.rpn_target_assign ArgSpec(args=['bbox_pred', 'cls_logits', 'anchor_box', 'anchor_var', 'gt_boxes', 'is_crowd', 'im_info', 'rpn_batch_size_per_im', 'rpn_straddle_thresh', 'rpn_fg_fraction', 'rpn_positive_overlap', 'rpn_negative_overlap', 'use_random'], varargs=None, keywords=None, defaults=(256, 0.0, 0.5, 0.7, 0.3, True)) -paddle.fluid.layers.anchor_generator ArgSpec(args=['input', 'anchor_sizes', 'aspect_ratios', 'variance', 'stride', 'offset', 'name'], varargs=None, keywords=None, defaults=(None, None, [0.1, 0.1, 0.2, 0.2], None, 0.5, None)) -paddle.fluid.layers.roi_perspective_transform ArgSpec(args=['input', 'rois', 'transformed_height', 'transformed_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1.0,)) -paddle.fluid.layers.generate_proposal_labels ArgSpec(args=['rpn_rois', 'gt_classes', 'is_crowd', 'gt_boxes', 'im_info', 'batch_size_per_im', 'fg_fraction', 'fg_thresh', 'bg_thresh_hi', 'bg_thresh_lo', 'bbox_reg_weights', 'class_nums', 'use_random'], varargs=None, keywords=None, defaults=(256, 0.25, 0.25, 0.5, 0.0, [0.1, 0.1, 0.2, 0.2], None, True)) -paddle.fluid.layers.generate_proposals ArgSpec(args=['scores', 'bbox_deltas', 'im_info', 'anchors', 'variances', 'pre_nms_top_n', 'post_nms_top_n', 'nms_thresh', 'min_size', 'eta', 'name'], varargs=None, keywords=None, defaults=(6000, 1000, 0.5, 0.1, 1.0, None)) -paddle.fluid.layers.generate_mask_labels ArgSpec(args=['im_info', 'gt_classes', 'is_crowd', 'gt_segms', 'rois', 'labels_int32', 'num_classes', 'resolution'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.iou_similarity ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.box_coder ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'code_type', 'box_normalized', 'name', 'axis'], varargs=None, keywords=None, defaults=('encode_center_size', True, None, 0)) -paddle.fluid.layers.polygon_box_transform ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.yolov3_loss ArgSpec(args=['x', 'gtbox', 'gtlabel', 'anchors', 'anchor_mask', 'class_num', 'ignore_thresh', 'downsample_ratio', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.box_clip ArgSpec(args=['input', 'im_info', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.layers.multiclass_nms ArgSpec(args=['bboxes', 'scores', 'score_threshold', 'nms_top_k', 'keep_top_k', 'nms_threshold', 'normalized', 'nms_eta', 'background_label', 'name'], varargs=None, keywords=None, defaults=(0.3, True, 1.0, 0, None)) -paddle.fluid.layers.accuracy ArgSpec(args=['input', 'label', 'k', 'correct', 'total'], varargs=None, keywords=None, defaults=(1, None, None)) -paddle.fluid.layers.auc ArgSpec(args=['input', 'label', 'curve', 'num_thresholds', 'topk', 'slide_steps'], varargs=None, keywords=None, defaults=('ROC', 4095, 1, 1)) -paddle.fluid.layers.exponential_decay ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,)) -paddle.fluid.layers.natural_exp_decay ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,)) -paddle.fluid.layers.inverse_time_decay ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,)) -paddle.fluid.layers.polynomial_decay ArgSpec(args=['learning_rate', 'decay_steps', 'end_learning_rate', 'power', 'cycle'], varargs=None, keywords=None, defaults=(0.0001, 1.0, False)) -paddle.fluid.layers.piecewise_decay ArgSpec(args=['boundaries', 'values'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.noam_decay ArgSpec(args=['d_model', 'warmup_steps'], varargs=None, keywords=None, defaults=None) -paddle.fluid.layers.append_LARS ArgSpec(args=['params_grads', 'learning_rate', 'weight_decay'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.InitState.__init__ ArgSpec(args=['self', 'init', 'shape', 'value', 'init_boot', 'need_reorder', 'dtype'], varargs=None, keywords=None, defaults=(None, None, 0.0, None, False, 'float32')) -paddle.fluid.contrib.StateCell.__init__ ArgSpec(args=['self', 'inputs', 'states', 'out_state', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.contrib.StateCell.compute_state ArgSpec(args=['self', 'inputs'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.StateCell.get_input ArgSpec(args=['self', 'input_name'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.StateCell.get_state ArgSpec(args=['self', 'state_name'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.StateCell.out_state ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.StateCell.set_state ArgSpec(args=['self', 'state_name', 'state_value'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.StateCell.state_updater ArgSpec(args=['self', 'updater'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.StateCell.update_states ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.TrainingDecoder.__init__ ArgSpec(args=['self', 'state_cell', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.contrib.TrainingDecoder.block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.TrainingDecoder.output ArgSpec(args=['self'], varargs='outputs', keywords=None, defaults=None) -paddle.fluid.contrib.TrainingDecoder.static_input ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.TrainingDecoder.step_input ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.BeamSearchDecoder.__init__ ArgSpec(args=['self', 'state_cell', 'init_ids', 'init_scores', 'target_dict_dim', 'word_dim', 'input_var_dict', 'topk_size', 'sparse_emb', 'max_len', 'beam_size', 'end_id', 'name'], varargs=None, keywords=None, defaults=({}, 50, True, 100, 1, 1, None)) -paddle.fluid.contrib.BeamSearchDecoder.block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.BeamSearchDecoder.decode ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.BeamSearchDecoder.early_stop ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.BeamSearchDecoder.read_array ArgSpec(args=['self', 'init', 'is_ids', 'is_scores'], varargs=None, keywords=None, defaults=(False, False)) -paddle.fluid.contrib.BeamSearchDecoder.update_array ArgSpec(args=['self', 'array', 'value'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.memory_usage ArgSpec(args=['program', 'batch_size'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.op_freq_statistic ArgSpec(args=['program'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.QuantizeTranspiler.__init__ ArgSpec(args=['self', 'weight_bits', 'activation_bits', 'activation_quantize_type', 'weight_quantize_type', 'window_size'], varargs=None, keywords=None, defaults=(8, 8, 'abs_max', 'abs_max', 10000)) -paddle.fluid.contrib.QuantizeTranspiler.convert_to_int8 ArgSpec(args=['self', 'program', 'place', 'scope'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.contrib.QuantizeTranspiler.freeze_program ArgSpec(args=['self', 'program', 'place', 'fuse_bn', 'scope'], varargs=None, keywords=None, defaults=(False, None)) -paddle.fluid.contrib.QuantizeTranspiler.training_transpile ArgSpec(args=['self', 'program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None)) -paddle.fluid.contrib.Calibrator.__init__ ArgSpec(args=['self'], varargs='args', keywords='kwargs', defaults=None) -paddle.fluid.contrib.Calibrator.sample_data ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.Calibrator.save_int8_model ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.reader.ctr_reader.ctr_reader ArgSpec(args=['feed_dict', 'file_type', 'file_format', 'dense_slot_index', 'sparse_slot_index', 'capacity', 'thread_num', 'batch_size', 'file_list', 'slots', 'name'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.contrib.build_compressor ArgSpec(args=['place', 'data_reader', 'data_feeder', 'scope', 'metrics', 'epoch', 'config'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None)) -paddle.fluid.contrib.CompressPass.__init__ ArgSpec(args=['self', 'place', 'data_reader', 'data_feeder', 'scope', 'metrics', 'epoch', 'program_exe'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None)) -paddle.fluid.contrib.CompressPass.add_strategy ArgSpec(args=['self', 'strategy'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.CompressPass.apply ArgSpec(args=['self', 'graph'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.ImitationGraph.__init__ ArgSpec(args=['self', 'program'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.contrib.ImitationGraph.all_parameters ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.SensitivePruneStrategy.__init__ ArgSpec(args=['self', 'pruner', 'start_epoch', 'end_epoch', 'delta_rate', 'acc_loss_threshold', 'sensitivities'], varargs=None, keywords=None, defaults=(None, 0, 10, 0.2, 0.2, None)) -paddle.fluid.contrib.SensitivePruneStrategy.on_batch_begin ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.SensitivePruneStrategy.on_batch_end ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.SensitivePruneStrategy.on_compress_begin ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.SensitivePruneStrategy.on_compress_end ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.SensitivePruneStrategy.on_epoch_begin ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.SensitivePruneStrategy.on_epoch_end ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.MagnitudePruner.__init__ ArgSpec(args=['self', 'threshold'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.MagnitudePruner.prune ArgSpec(args=['self', 'param', 'threshold'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.contrib.RatioPruner.__init__ ArgSpec(args=['self', 'ratios'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.contrib.RatioPruner.prune ArgSpec(args=['self', 'param', 'ratio'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.contrib.load_persistables_for_increment ArgSpec(args=['dirname', 'executor', 'program', 'lookup_table_var', 'lookup_table_var_path'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.load_persistables_for_inference ArgSpec(args=['dirname', 'executor', 'program', 'lookup_table_var_name'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.convert_dist_to_sparse_program ArgSpec(args=['program'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.HDFSClient.__init__ ArgSpec(args=['self', 'hadoop_home', 'configs'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.HDFSClient.delete ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.HDFSClient.download ArgSpec(args=['self', 'hdfs_path', 'local_path', 'overwrite', 'unzip'], varargs=None, keywords=None, defaults=(False, False)) -paddle.fluid.contrib.HDFSClient.is_dir ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.contrib.HDFSClient.is_exist ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.contrib.HDFSClient.ls ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.HDFSClient.lsr ArgSpec(args=['self', 'hdfs_path', 'only_file', 'sort'], varargs=None, keywords=None, defaults=(True, True)) -paddle.fluid.contrib.HDFSClient.make_local_dirs ArgSpec(args=['local_path'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.HDFSClient.makedirs ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=None) -paddle.fluid.contrib.HDFSClient.rename ArgSpec(args=['self', 'hdfs_src_path', 'hdfs_dst_path', 'overwrite'], varargs=None, keywords=None, defaults=(False,)) -paddle.fluid.contrib.HDFSClient.upload ArgSpec(args=['self', 'hdfs_path', 'local_path', 'overwrite', 'retry_times'], varargs=None, keywords=None, defaults=(False, 5)) -paddle.fluid.contrib.multi_download ArgSpec(args=['client', 'hdfs_path', 'local_path', 'trainer_id', 'trainers', 'multi_processes'], varargs=None, keywords=None, defaults=(5,)) -paddle.fluid.contrib.multi_upload ArgSpec(args=['client', 'hdfs_path', 'local_path', 'multi_processes', 'overwrite', 'sync'], varargs=None, keywords=None, defaults=(5, False, True)) -paddle.fluid.transpiler.DistributeTranspiler.__init__ ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.transpiler.DistributeTranspiler.get_pserver_program ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None) -paddle.fluid.transpiler.DistributeTranspiler.get_pserver_programs ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None) -paddle.fluid.transpiler.DistributeTranspiler.get_startup_program ArgSpec(args=['self', 'endpoint', 'pserver_program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None)) -paddle.fluid.transpiler.DistributeTranspiler.get_trainer_program ArgSpec(args=['self', 'wait_port'], varargs=None, keywords=None, defaults=(True,)) -paddle.fluid.transpiler.DistributeTranspiler.transpile ArgSpec(args=['self', 'trainer_id', 'program', 'pservers', 'trainers', 'sync_mode', 'startup_program', 'current_endpoint'], varargs=None, keywords=None, defaults=(None, '127.0.0.1:6174', 1, True, None, '127.0.0.1:6174')) -paddle.fluid.transpiler.memory_optimize ArgSpec(args=['input_program', 'skip_opt_set', 'print_log', 'level', 'skip_grads'], varargs=None, keywords=None, defaults=(None, False, 0, False)) -paddle.fluid.transpiler.release_memory ArgSpec(args=['input_program', 'skip_opt_set'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.transpiler.HashName.__init__ ArgSpec(args=['self', 'pserver_endpoints'], varargs=None, keywords=None, defaults=None) -paddle.fluid.transpiler.HashName.dispatch ArgSpec(args=['self', 'varlist'], varargs=None, keywords=None, defaults=None) -paddle.fluid.transpiler.HashName.reset ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None) -paddle.fluid.transpiler.RoundRobin.__init__ ArgSpec(args=['self', 'pserver_endpoints'], varargs=None, keywords=None, defaults=None) -paddle.fluid.transpiler.RoundRobin.dispatch ArgSpec(args=['self', 'varlist'], varargs=None, keywords=None, defaults=None) -paddle.fluid.transpiler.RoundRobin.reset ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None) +paddle.fluid.io.save_vars (ArgSpec(args=['executor', 'dirname', 'main_program', 'vars', 'predicate', 'filename'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b55d6193a1d4198d45b013fc5779e1f2')) +paddle.fluid.io.save_params (ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None)), ('document', '3a7a99abac3e1bf898871fe609354218')) +paddle.fluid.io.save_persistables (ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None)), ('document', '9141bb5f32caf7975eb3fd88c8a1b2da')) +paddle.fluid.io.load_vars (ArgSpec(args=['executor', 'dirname', 'main_program', 'vars', 'predicate', 'filename'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '0a5308f496632ab1ec3ba1f1377e6f95')) +paddle.fluid.io.load_params (ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None)), ('document', '41779819cef32f2246e83aebc5a002e2')) +paddle.fluid.io.load_persistables (ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None)), ('document', '28df5bfe26ca7a077f91156abb0fe6d2')) +paddle.fluid.io.save_inference_model (ArgSpec(args=['dirname', 'feeded_var_names', 'target_vars', 'executor', 'main_program', 'model_filename', 'params_filename', 'export_for_deployment'], varargs=None, keywords=None, defaults=(None, None, None, True)), ('document', '582d87b8df75a5a639a107db8ff86f9c')) +paddle.fluid.io.load_inference_model (ArgSpec(args=['dirname', 'executor', 'model_filename', 'params_filename', 'pserver_endpoints'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '7a5255386075dac3c75b7058254fcdcb')) +paddle.fluid.initializer.ConstantInitializer.__init__ (ArgSpec(args=['self', 'value', 'force_cpu'], varargs=None, keywords=None, defaults=(0.0, False)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.initializer.UniformInitializer.__init__ (ArgSpec(args=['self', 'low', 'high', 'seed'], varargs=None, keywords=None, defaults=(-1.0, 1.0, 0)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.initializer.NormalInitializer.__init__ (ArgSpec(args=['self', 'loc', 'scale', 'seed'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.initializer.TruncatedNormalInitializer.__init__ (ArgSpec(args=['self', 'loc', 'scale', 'seed'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.initializer.XavierInitializer.__init__ (ArgSpec(args=['self', 'uniform', 'fan_in', 'fan_out', 'seed'], varargs=None, keywords=None, defaults=(True, None, None, 0)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.initializer.BilinearInitializer.__init__ (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'd389912dc079cbef432335a00017cec0')) +paddle.fluid.initializer.MSRAInitializer.__init__ (ArgSpec(args=['self', 'uniform', 'fan_in', 'seed'], varargs=None, keywords=None, defaults=(True, None, 0)), ('document', '53c757bed9345f2ad3361902531e7cf5')) +paddle.fluid.initializer.force_init_on_cpu (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', '6d0f3e22c90d9d500d36ff57daf056ee')) +paddle.fluid.initializer.init_on_cpu (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', 'a6d7011ca3d8c0d454dac3a56eae0c29')) +paddle.fluid.initializer.NumpyArrayInitializer.__init__ (ArgSpec(args=['self', 'value'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.layers.fc (ArgSpec(args=['input', 'size', 'num_flatten_dims', 'param_attr', 'bias_attr', 'act', 'is_test', 'name'], varargs=None, keywords=None, defaults=(1, None, None, None, False, None)), ('document', '1929058262994f212620599c63aea6bd')) +paddle.fluid.layers.embedding (ArgSpec(args=['input', 'size', 'is_sparse', 'is_distributed', 'padding_idx', 'param_attr', 'dtype'], varargs=None, keywords=None, defaults=(False, False, None, None, 'float32')), ('document', '89c2c55a0b0656b106064048e068e77a')) +paddle.fluid.layers.dynamic_lstm (ArgSpec(args=['input', 'size', 'h_0', 'c_0', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'float32', None)), ('document', 'dfbb624f85015df29e994ca6999e8ff6')) +paddle.fluid.layers.dynamic_lstmp (ArgSpec(args=['input', 'size', 'proj_size', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'proj_activation', 'dtype', 'name', 'h_0', 'c_0', 'cell_clip', 'proj_clip'], varargs=None, keywords=None, defaults=(None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'tanh', 'float32', None, None, None, None, None)), ('document', 'b4b608b986eb9617aa0525e1be21d32d')) +paddle.fluid.layers.dynamic_gru (ArgSpec(args=['input', 'size', 'param_attr', 'bias_attr', 'is_reverse', 'gate_activation', 'candidate_activation', 'h_0', 'origin_mode'], varargs=None, keywords=None, defaults=(None, None, False, 'sigmoid', 'tanh', None, False)), ('document', '4ec4845fd7d991bcac822f8b0dfc101f')) +paddle.fluid.layers.gru_unit (ArgSpec(args=['input', 'hidden', 'size', 'param_attr', 'bias_attr', 'activation', 'gate_activation', 'origin_mode'], varargs=None, keywords=None, defaults=(None, None, 'tanh', 'sigmoid', False)), ('document', 'e0e2439f7af069b57badca18a6ba60b8')) +paddle.fluid.layers.linear_chain_crf (ArgSpec(args=['input', 'label', 'param_attr'], varargs=None, keywords=None, defaults=(None,)), ('document', '7c49ef4bbf0adfd4b9a1d98e2e5f3fea')) +paddle.fluid.layers.crf_decoding (ArgSpec(args=['input', 'param_attr', 'label'], varargs=None, keywords=None, defaults=(None,)), ('document', '7642373ab65d3fc3b96d16d10fef1538')) +paddle.fluid.layers.cos_sim (ArgSpec(args=['X', 'Y'], varargs=None, keywords=None, defaults=None), ('document', 'd740824aa7316b807c4b4a3c6c8c0bbe')) +paddle.fluid.layers.cross_entropy (ArgSpec(args=['input', 'label', 'soft_label', 'ignore_index'], varargs=None, keywords=None, defaults=(False, -100)), ('document', '025b364dafb4b7975c801eb33e7831a1')) +paddle.fluid.layers.bpr_loss (ArgSpec(args=['input', 'label', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '30add751a0f99347a6257634c03ff254')) +paddle.fluid.layers.square_error_cost (ArgSpec(args=['input', 'label'], varargs=None, keywords=None, defaults=None), ('document', '44b6eef4a0f2bc15f7d9745782406736')) +paddle.fluid.layers.chunk_eval (ArgSpec(args=['input', 'label', 'chunk_scheme', 'num_chunk_types', 'excluded_chunk_types'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ee152a7ba3036e7b9ede9184545179b4')) +paddle.fluid.layers.sequence_conv (ArgSpec(args=['input', 'num_filters', 'filter_size', 'filter_stride', 'padding', 'bias_attr', 'param_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(3, 1, None, None, None, None, None)), ('document', 'b6543768e1afaa2ecb869709d6e9c7e2')) +paddle.fluid.layers.conv2d (ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None)), ('document', '8ca6121acd6d23cd8806a93f493c2e17')) +paddle.fluid.layers.conv3d (ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None)), ('document', '37042620f9bd3a2da6e5d3138b2f724b')) +paddle.fluid.layers.sequence_pool (ArgSpec(args=['input', 'pool_type', 'is_test'], varargs=None, keywords=None, defaults=(False,)), ('document', 'a194fb80614023f543df3949fbd0d0b8')) +paddle.fluid.layers.sequence_softmax (ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(False, None)), ('document', '19ef6f9cdd27feac8a1ae060f19c10b4')) +paddle.fluid.layers.softmax (ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(False, None)), ('document', 'f19dd380864e61134ce3814e4be0de4b')) +paddle.fluid.layers.pool2d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True)), ('document', 'bbd84e855e660cd1084bb71a2fd0cdaa')) +paddle.fluid.layers.pool3d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True)), ('document', '043de7333b79ee0ac55053c14ed81625')) +paddle.fluid.layers.adaptive_pool2d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'require_index', 'name'], varargs=None, keywords=None, defaults=('max', False, None)), ('document', '859b887174d06f361658f69cb7c06d95')) +paddle.fluid.layers.adaptive_pool3d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'require_index', 'name'], varargs=None, keywords=None, defaults=('max', False, None)), ('document', '120f4323a3d7ed9c0916f15a59f0e497')) +paddle.fluid.layers.batch_norm (ArgSpec(args=['input', 'act', 'is_test', 'momentum', 'epsilon', 'param_attr', 'bias_attr', 'data_layout', 'in_place', 'name', 'moving_mean_name', 'moving_variance_name', 'do_model_average_for_mean_and_var', 'fuse_with_relu', 'use_global_stats'], varargs=None, keywords=None, defaults=(None, False, 0.9, 1e-05, None, None, 'NCHW', False, None, None, None, False, False, False)), ('document', 'c527b71b8a4c60dca8df8a745c2b598d')) +paddle.fluid.layers.data_norm (ArgSpec(args=['input', 'act', 'epsilon', 'param_attr', 'data_layout', 'in_place', 'name', 'moving_mean_name', 'moving_variance_name', 'do_model_average_for_mean_and_var'], varargs=None, keywords=None, defaults=(None, 1e-05, None, 'NCHW', False, None, None, None, False)), ('document', 'e45e09e65a2658e07cad987222f0d9ab')) +paddle.fluid.layers.beam_search_decode (ArgSpec(args=['ids', 'scores', 'beam_size', 'end_id', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b0b8d53821716cd50c42e09b593f3feb')) +paddle.fluid.layers.conv2d_transpose (ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None)), ('document', '03993955ab1e6d3044c44e6f17fc85e9')) +paddle.fluid.layers.conv3d_transpose (ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None)), ('document', 'ec113c6a3686ac94f8fccd1a7953d445')) +paddle.fluid.layers.sequence_expand (ArgSpec(args=['x', 'y', 'ref_level', 'name'], varargs=None, keywords=None, defaults=(-1, None)), ('document', '79c375214fa427faac504043d162dae9')) +paddle.fluid.layers.sequence_expand_as (ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '9d2611f84ab364c5da545e6a82f1770a')) +paddle.fluid.layers.sequence_pad (ArgSpec(args=['x', 'pad_value', 'maxlen', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '6a1adf3067b20f6e4bcb354d71c19184')) +paddle.fluid.layers.sequence_unpad (ArgSpec(args=['x', 'length', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'd12803c903c99aa36ec03aaac5f0cc5b')) +paddle.fluid.layers.lstm_unit (ArgSpec(args=['x_t', 'hidden_t_prev', 'cell_t_prev', 'forget_bias', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(0.0, None, None, None)), ('document', '027723966f3ef0d7bc598f22287a96cc')) +paddle.fluid.layers.reduce_sum (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'b69998ce3ff4980fb21da0df05565f1b')) +paddle.fluid.layers.reduce_mean (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'd4d80dd98a1a5839f41eeb3a0f85f370')) +paddle.fluid.layers.reduce_max (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', '66a622db727551761ce4eb73eaa7f6a4')) +paddle.fluid.layers.reduce_min (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'd50ac552b5d131468ed466d08bb2d38c')) +paddle.fluid.layers.reduce_prod (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'fcd8301a0ce15f219c7a4bcd0c1e8eca')) +paddle.fluid.layers.sequence_first_step (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', '2b290d3d77882bfe9bb8d331cac8cdd3')) +paddle.fluid.layers.sequence_last_step (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', 'c16a892f44f7fe71bfa5afc32d3f34ce')) +paddle.fluid.layers.sequence_slice (ArgSpec(args=['input', 'offset', 'length', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'fdcea0e8b5bc7d8d4b1b072c521014e6')) +paddle.fluid.layers.dropout (ArgSpec(args=['x', 'dropout_prob', 'is_test', 'seed', 'name', 'dropout_implementation'], varargs=None, keywords=None, defaults=(False, None, None, 'downgrade_in_infer')), ('document', 'dc7042734c6d8b8ce97321f017f01d6f')) +paddle.fluid.layers.split (ArgSpec(args=['input', 'num_or_sections', 'dim', 'name'], varargs=None, keywords=None, defaults=(-1, None)), ('document', '652625345c2acb900029c78cc75f8aa6')) +paddle.fluid.layers.ctc_greedy_decoder (ArgSpec(args=['input', 'blank', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ebbf2adbd79683dc93db03454dfa18c2')) +paddle.fluid.layers.edit_distance (ArgSpec(args=['input', 'label', 'normalized', 'ignored_tokens'], varargs=None, keywords=None, defaults=(True, None)), ('document', '97f0262f97602644c83142789d784571')) +paddle.fluid.layers.l2_normalize (ArgSpec(args=['x', 'axis', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(1e-12, None)), ('document', '6e428384ce6a77207fa2c70d9f011990')) +paddle.fluid.layers.matmul (ArgSpec(args=['x', 'y', 'transpose_x', 'transpose_y', 'alpha', 'name'], varargs=None, keywords=None, defaults=(False, False, 1.0, None)), ('document', 'b4cbe1ac451005df6dad12e9ffdccca9')) +paddle.fluid.layers.topk (ArgSpec(args=['input', 'k', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'd3570c02f71bcd78e60b3f31dc8f5b32')) +paddle.fluid.layers.warpctc (ArgSpec(args=['input', 'label', 'blank', 'norm_by_times', 'use_cudnn'], varargs=None, keywords=None, defaults=(0, False, False)), ('document', 'aaba49c038ba927f0a8e45c0c9a686ab')) +paddle.fluid.layers.sequence_reshape (ArgSpec(args=['input', 'new_dim'], varargs=None, keywords=None, defaults=None), ('document', 'a10ab9bf88d4a7e328882d411abb6fd1')) +paddle.fluid.layers.transpose (ArgSpec(args=['x', 'perm', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'a1feac48b843d679db82312dc85885f4')) +paddle.fluid.layers.im2sequence (ArgSpec(args=['input', 'filter_size', 'stride', 'padding', 'input_image_size', 'out_stride', 'name'], varargs=None, keywords=None, defaults=(1, 1, 0, None, 1, None)), ('document', '3ce01160ede80b1c26f776f8fef9340f')) +paddle.fluid.layers.nce (ArgSpec(args=['input', 'label', 'num_total_classes', 'sample_weight', 'param_attr', 'bias_attr', 'num_neg_samples', 'name', 'sampler', 'custom_dist', 'seed', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, 'uniform', None, 0, False)), ('document', 'fddad4896dee5193e1cdf70882c2a347')) +paddle.fluid.layers.sampled_softmax_with_cross_entropy (ArgSpec(args=['logits', 'label', 'num_samples', 'num_true', 'remove_accidental_hits', 'use_customized_samples', 'customized_samples', 'customized_probabilities', 'seed'], varargs=None, keywords=None, defaults=(1, True, False, None, None, 0)), ('document', '5db30b8a74e8c93687943a3e8d221da0')) +paddle.fluid.layers.hsigmoid (ArgSpec(args=['input', 'label', 'num_classes', 'param_attr', 'bias_attr', 'name', 'path_table', 'path_code', 'is_custom', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, False, False)), ('document', '80641ee6810b1cdc3fd6e14fc89ecc9d')) +paddle.fluid.layers.beam_search (ArgSpec(args=['pre_ids', 'pre_scores', 'ids', 'scores', 'beam_size', 'end_id', 'level', 'is_accumulated', 'name', 'return_parent_idx'], varargs=None, keywords=None, defaults=(0, True, None, False)), ('document', 'b350b9a30a18e7efd7e1bb740eef6996')) +paddle.fluid.layers.row_conv (ArgSpec(args=['input', 'future_context_size', 'param_attr', 'act'], varargs=None, keywords=None, defaults=(None, None)), ('document', '17485788fffe4e2d36dc58c2ac8d174e')) +paddle.fluid.layers.multiplex (ArgSpec(args=['inputs', 'index'], varargs=None, keywords=None, defaults=None), ('document', '013795af319e2e86d3506741941078ee')) +paddle.fluid.layers.layer_norm (ArgSpec(args=['input', 'scale', 'shift', 'begin_norm_axis', 'epsilon', 'param_attr', 'bias_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(True, True, 1, 1e-05, None, None, None, None)), ('document', 'de6a906950bae9f3c245cb744d22b94e')) +paddle.fluid.layers.group_norm (ArgSpec(args=['input', 'groups', 'epsilon', 'param_attr', 'bias_attr', 'act', 'data_layout', 'name'], varargs=None, keywords=None, defaults=(1e-05, None, None, None, 'NCHW', None)), ('document', '419c3a24a83cc89219a029cf4092788b')) +paddle.fluid.layers.softmax_with_cross_entropy (ArgSpec(args=['logits', 'label', 'soft_label', 'ignore_index', 'numeric_stable_mode', 'return_softmax'], varargs=None, keywords=None, defaults=(False, -100, True, False)), ('document', 'bce1b75e3d95b75cacd1099655cbb3c3')) +paddle.fluid.layers.smooth_l1 (ArgSpec(args=['x', 'y', 'inside_weight', 'outside_weight', 'sigma'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', 'c6b175d253c55baf4b9c0eca9b1dda88')) +paddle.fluid.layers.one_hot (ArgSpec(args=['input', 'depth'], varargs=None, keywords=None, defaults=None), ('document', '6148b6a555cbfb62fdcd030d8982c18c')) +paddle.fluid.layers.autoincreased_step_counter (ArgSpec(args=['counter_name', 'begin', 'step'], varargs=None, keywords=None, defaults=(None, 1, 1)), ('document', '3f6c828594720c9b2da89c464be94478')) +paddle.fluid.layers.reshape (ArgSpec(args=['x', 'shape', 'actual_shape', 'act', 'inplace', 'name'], varargs=None, keywords=None, defaults=(None, None, False, None)), ('document', '323c019f257e55ddea4a824a362de62f')) +paddle.fluid.layers.squeeze (ArgSpec(args=['input', 'axes', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '3229d06517f794e86ca3da14c38b1465')) +paddle.fluid.layers.unsqueeze (ArgSpec(args=['input', 'axes', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'bbd62da391b1df984a1909d069a759b2')) +paddle.fluid.layers.lod_reset (ArgSpec(args=['x', 'y', 'target_lod'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'f122194c562bd674f6ecdccf33785f99')) +paddle.fluid.layers.lrn (ArgSpec(args=['input', 'n', 'k', 'alpha', 'beta', 'name'], varargs=None, keywords=None, defaults=(5, 1.0, 0.0001, 0.75, None)), ('document', '0795e9940e42dcd62953514ff7e09f77')) +paddle.fluid.layers.pad (ArgSpec(args=['x', 'paddings', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0.0, None)), ('document', '2f28153bdd2d5ea6f7bad5867bd03eeb')) +paddle.fluid.layers.pad_constant_like (ArgSpec(args=['x', 'y', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0.0, None)), ('document', 'd2e1f45fef51b2c214e3f2aa8976c46c')) +paddle.fluid.layers.label_smooth (ArgSpec(args=['label', 'prior_dist', 'epsilon', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, 0.1, 'float32', None)), ('document', '70c113658102a11cc5d8e3d45145737a')) +paddle.fluid.layers.roi_pool (ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1, 1, 1.0)), ('document', 'c317aa595deb31649083c8faa91cdb97')) +paddle.fluid.layers.roi_align (ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale', 'sampling_ratio', 'name'], varargs=None, keywords=None, defaults=(1, 1, 1.0, -1, None)), ('document', '12c5bbb8b38c42e623fbc47611d766e1')) +paddle.fluid.layers.dice_loss (ArgSpec(args=['input', 'label', 'epsilon'], varargs=None, keywords=None, defaults=(1e-05,)), ('document', '1ba0508d573f65feecf3564dce22aa1d')) +paddle.fluid.layers.image_resize (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'resample', 'actual_shape', 'align_corners', 'align_mode'], varargs=None, keywords=None, defaults=(None, None, None, 'BILINEAR', None, True, 1)), ('document', 'b3ecb819454832885c1f0f3ab9a5b938')) +paddle.fluid.layers.image_resize_short (ArgSpec(args=['input', 'out_short_len', 'resample'], varargs=None, keywords=None, defaults=('BILINEAR',)), ('document', '06211aefc50c5a3e940d7204d859cdf7')) +paddle.fluid.layers.resize_bilinear (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners', 'align_mode'], varargs=None, keywords=None, defaults=(None, None, None, None, True, 1)), ('document', 'e4fb4ed511b2293b8f04f7e872afbfd7')) +paddle.fluid.layers.resize_nearest (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners'], varargs=None, keywords=None, defaults=(None, None, None, None, True)), ('document', '735fa9758a6d7ff3b47d7b827f961c1d')) +paddle.fluid.layers.gather (ArgSpec(args=['input', 'index'], varargs=None, keywords=None, defaults=None), ('document', '98f1c86716b9b7f4dda83f20e2adeee2')) +paddle.fluid.layers.scatter (ArgSpec(args=['input', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '65f8e9d8ddfd0b412f940579c4faa342')) +paddle.fluid.layers.sequence_scatter (ArgSpec(args=['input', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '15b522457dfef103f0c20ca9d397678b')) +paddle.fluid.layers.random_crop (ArgSpec(args=['x', 'shape', 'seed'], varargs=None, keywords=None, defaults=(None,)), ('document', 'c9ab9e460ef0a1823249935a30e82c66')) +paddle.fluid.layers.mean_iou (ArgSpec(args=['input', 'label', 'num_classes'], varargs=None, keywords=None, defaults=None), ('document', '35cbbdfa585d027bb490707c95a176b9')) +paddle.fluid.layers.relu (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '866ffa1cc93f29e23662b526a7596537')) +paddle.fluid.layers.selu (ArgSpec(args=['x', 'scale', 'alpha', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '9044c7fe667b76cb2d9264f2db11f417')) +paddle.fluid.layers.log (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '98247c59d1c9b40af6730001b2aea73d')) +paddle.fluid.layers.crop (ArgSpec(args=['x', 'shape', 'offsets', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '883104791204d3127e24234bb630b2e7')) +paddle.fluid.layers.rank_loss (ArgSpec(args=['label', 'left', 'right', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'c542e39ac6add24a6bef6e79bf5617e2')) +paddle.fluid.layers.margin_rank_loss (ArgSpec(args=['label', 'left', 'right', 'margin', 'name'], varargs=None, keywords=None, defaults=(0.1, None)), ('document', '6d19dcc19917080b7ff3e03bde451bc8')) +paddle.fluid.layers.elu (ArgSpec(args=['x', 'alpha', 'name'], varargs=None, keywords=None, defaults=(1.0, None)), ('document', '463258ee9f8b60760eb1e26357cc9bfa')) +paddle.fluid.layers.relu6 (ArgSpec(args=['x', 'threshold', 'name'], varargs=None, keywords=None, defaults=(6.0, None)), ('document', '6f367339caf6c7124bc262fe1475df70')) +paddle.fluid.layers.pow (ArgSpec(args=['x', 'factor', 'name'], varargs=None, keywords=None, defaults=(1.0, None)), ('document', 'a5117c1eb84aca2ac0b0abab337a4799')) +paddle.fluid.layers.stanh (ArgSpec(args=['x', 'scale_a', 'scale_b', 'name'], varargs=None, keywords=None, defaults=(0.6666666666666666, 1.7159, None)), ('document', '959936a477efc6c1447a9c8bf8ce94bb')) +paddle.fluid.layers.hard_sigmoid (ArgSpec(args=['x', 'slope', 'offset', 'name'], varargs=None, keywords=None, defaults=(0.2, 0.5, None)), ('document', 'c82059b6fea1aa730f9aac911807b756')) +paddle.fluid.layers.swish (ArgSpec(args=['x', 'beta', 'name'], varargs=None, keywords=None, defaults=(1.0, None)), ('document', 'ef745e55a48763ee7b46b21a81dc7e84')) +paddle.fluid.layers.prelu (ArgSpec(args=['x', 'mode', 'param_attr', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'f6acef7ff7d887e49ff499fbb1dad4a9')) +paddle.fluid.layers.brelu (ArgSpec(args=['x', 't_min', 't_max', 'name'], varargs=None, keywords=None, defaults=(0.0, 24.0, None)), ('document', '3db337c195e156e6ef2b8b4a57113600')) +paddle.fluid.layers.leaky_relu (ArgSpec(args=['x', 'alpha', 'name'], varargs=None, keywords=None, defaults=(0.02, None)), ('document', 'f878486c82b576938151daad0de995a0')) +paddle.fluid.layers.soft_relu (ArgSpec(args=['x', 'threshold', 'name'], varargs=None, keywords=None, defaults=(40.0, None)), ('document', '869adce548c342d6cc1bd88a948d83c9')) +paddle.fluid.layers.flatten (ArgSpec(args=['x', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None)), ('document', 'cb295c13cb957db85cd9609269d7784d')) +paddle.fluid.layers.sequence_mask (ArgSpec(args=['x', 'maxlen', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, 'int64', None)), ('document', 'f0dd6eddd3bff015a3c05269d82fcbd8')) +paddle.fluid.layers.stack (ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=(0,)), ('document', '367cfbb642839beacb5d117e2d2b4041')) +paddle.fluid.layers.pad2d (ArgSpec(args=['input', 'paddings', 'mode', 'pad_value', 'data_format', 'name'], varargs=None, keywords=None, defaults=([0, 0, 0, 0], 'constant', 0.0, 'NCHW', None)), ('document', '7f4d46320cc077ca2e8db600c35f4030')) +paddle.fluid.layers.unstack (ArgSpec(args=['x', 'axis', 'num'], varargs=None, keywords=None, defaults=(0, None)), ('document', '98eb9d633116efcfc6f90c114bd44fd6')) +paddle.fluid.layers.sequence_enumerate (ArgSpec(args=['input', 'win_size', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0, None)), ('document', 'f6028537085dc296103bbbd85fa7763d')) +paddle.fluid.layers.expand (ArgSpec(args=['x', 'expand_times', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '117d3607d1ffa0571835bbaebc7857ff')) +paddle.fluid.layers.sequence_concat (ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '3a1d155dd1bf6e72a0a3e3e1519591d1')) +paddle.fluid.layers.scale (ArgSpec(args=['x', 'scale', 'bias', 'bias_after_scale', 'act', 'name'], varargs=None, keywords=None, defaults=(1.0, 0.0, True, None, None)), ('document', '30190413b2fa442e7466d6cf2ce5ea07')) +paddle.fluid.layers.elementwise_add (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '6bfbe72cbadc95ac7ab88c05ed5bf9f0')) +paddle.fluid.layers.elementwise_div (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', 'cc6e6cc1cb942a152dde3ef08d5f165c')) +paddle.fluid.layers.elementwise_sub (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', 'a12abdab09c3e57af5a6e1e9f138684a')) +paddle.fluid.layers.elementwise_mul (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '422c77dbfcff355a57b5fdd4ec876daa')) +paddle.fluid.layers.elementwise_max (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', 'f0bb0b2c454541cfafa761021a5cc776')) +paddle.fluid.layers.elementwise_min (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '8a9cdefefbccbf9f6b0991c0946a21e9')) +paddle.fluid.layers.elementwise_pow (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '1aea4e197c552a284f83888a3c67a32e')) +paddle.fluid.layers.uniform_random_batch_size_like (ArgSpec(args=['input', 'shape', 'dtype', 'input_dim_idx', 'output_dim_idx', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', 0, 0, -1.0, 1.0, 0)), ('document', '129e0a3257f1d532a948eedf9d5bf671')) +paddle.fluid.layers.gaussian_random (ArgSpec(args=['shape', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', '389dafe36e099841b6a7fb18d11f1b4c')) +paddle.fluid.layers.sampling_id (ArgSpec(args=['x', 'min', 'max', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', '840fdac643d1341c1cae218d4511dbb9')) +paddle.fluid.layers.gaussian_random_batch_size_like (ArgSpec(args=['input', 'shape', 'input_dim_idx', 'output_dim_idx', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0, 0, 0.0, 1.0, 0, 'float32')), ('document', '840026b4766613c5705e06563cd103b6')) +paddle.fluid.layers.sum (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', 'a418e3ccb5e2ac21bd60f5cc221d5860')) +paddle.fluid.layers.slice (ArgSpec(args=['input', 'axes', 'starts', 'ends'], varargs=None, keywords=None, defaults=None), ('document', '01dbb91e7c74cb11336cd531013de51a')) +paddle.fluid.layers.shape (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', '17db0f814eb7bb5a3fac1ca6e60e16d8')) +paddle.fluid.layers.logical_and (ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'cdcf20c494c92060d10feb9374532f42')) +paddle.fluid.layers.logical_or (ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '0eae3f726a4afe590757552fa3ced012')) +paddle.fluid.layers.logical_xor (ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'b0daaa3fa4a0aa62f9b58c43d959eb25')) +paddle.fluid.layers.logical_not (ArgSpec(args=['x', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'cd1c8cf31e040427d4e05711044caeb6')) +paddle.fluid.layers.clip (ArgSpec(args=['x', 'min', 'max', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b020b7aab59719be98a4ae229a76deba')) +paddle.fluid.layers.clip_by_norm (ArgSpec(args=['x', 'max_norm', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'a1ea0bc5a926f427458c4254ca022749')) +paddle.fluid.layers.mean (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'd638d915195ce86a8d7963b81110d4c8')) +paddle.fluid.layers.mul (ArgSpec(args=['x', 'y', 'x_num_col_dims', 'y_num_col_dims', 'name'], varargs=None, keywords=None, defaults=(1, 1, None)), ('document', 'ccd37fa6b53f074adbfb732d738c4c2d')) +paddle.fluid.layers.sigmoid_cross_entropy_with_logits (ArgSpec(args=['x', 'label', 'ignore_index', 'name', 'normalize'], varargs=None, keywords=None, defaults=(-100, None, False)), ('document', '180c284317ea45ef89a460d8d79c0b72')) +paddle.fluid.layers.maxout (ArgSpec(args=['x', 'groups', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '891870d069a6aea746d34cc53b61690c')) +paddle.fluid.layers.space_to_depth (ArgSpec(args=['x', 'blocksize', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '5f207ae10589ebe38a63575ef6ff8e1e')) +paddle.fluid.layers.affine_grid (ArgSpec(args=['theta', 'out_shape', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '51def402b8910e163cbace9d0c0526ed')) +paddle.fluid.layers.sequence_reverse (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '77a6d80aa5551ca70324fc975c44507f')) +paddle.fluid.layers.affine_channel (ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None)), ('document', '2f46f1ff39a13ab00857e7b9f44b2fa7')) +paddle.fluid.layers.similarity_focus (ArgSpec(args=['input', 'axis', 'indexes', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '70e3b5182a18b40b47ecabd7c8490a35')) +paddle.fluid.layers.hash (ArgSpec(args=['input', 'hash_size', 'num_hash', 'name'], varargs=None, keywords=None, defaults=(1, None)), ('document', '9bb77f8dc002dd2ce75d4769eaaf5007')) +paddle.fluid.layers.grid_sampler (ArgSpec(args=['x', 'grid', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'd256cba1c41a5ed92ce3f31e24a2ca6d')) +paddle.fluid.layers.log_loss (ArgSpec(args=['input', 'label', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(0.0001, None)), ('document', '4b5a2341023afe63157a066c14254f98')) +paddle.fluid.layers.add_position_encoding (ArgSpec(args=['input', 'alpha', 'beta', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '4b9c2e8af5817937d831820874b5aa77')) +paddle.fluid.layers.bilinear_tensor_product (ArgSpec(args=['x', 'y', 'size', 'act', 'name', 'param_attr', 'bias_attr'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'aa7540a0fa73ff69a02e11b4091aab75')) +paddle.fluid.layers.merge_selected_rows (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'dc63315b84f591ac79ecca0c3632027a')) +paddle.fluid.layers.get_tensor_from_selected_rows (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '7ffc849e71f31dfe29030ff94e662de6')) +paddle.fluid.layers.lstm (ArgSpec(args=['input', 'init_h', 'init_c', 'max_len', 'hidden_size', 'num_layers', 'dropout_prob', 'is_bidirec', 'is_test', 'name', 'default_initializer', 'seed'], varargs=None, keywords=None, defaults=(0.0, False, False, None, None, -1)), ('document', 'd5e6c494ac35100e2ed4d4bd9a1ed932')) +paddle.fluid.layers.shuffle_channel (ArgSpec(args=['x', 'group', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '2fa6782d43d02ae64482d21235a82949')) +paddle.fluid.layers.py_func (ArgSpec(args=['func', 'x', 'out', 'backward_func', 'skip_vars_in_backward_input'], varargs=None, keywords=None, defaults=(None, None)), ('document', '8404e472ac12b4a30a505d3d3a3e5fdb')) +paddle.fluid.layers.psroi_pool (ArgSpec(args=['input', 'rois', 'output_channels', 'spatial_scale', 'pooled_height', 'pooled_width', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '1546136806fef5c08f6918544bd9151d')) +paddle.fluid.layers.teacher_student_sigmoid_loss (ArgSpec(args=['input', 'label', 'soft_max_up_bound', 'soft_max_lower_bound'], varargs=None, keywords=None, defaults=(15.0, -15.0)), ('document', '2f6ff96864054a31aa4bb659c6722c99')) +paddle.fluid.layers.huber_loss (ArgSpec(args=['input', 'label', 'delta'], varargs=None, keywords=None, defaults=None), ('document', '431a4301c35032166ec029f7432c80a7')) +paddle.fluid.layers.tree_conv (ArgSpec(args=['nodes_vector', 'edge_set', 'output_size', 'num_filters', 'max_depth', 'act', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(1, 2, 'tanh', None, None, None)), ('document', '34ea12ac9f10a65dccbc50100d12e607')) +paddle.fluid.layers.data (ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True)), ('document', '33bbd42027d872b3818b3d64ec52e139')) +paddle.fluid.layers.open_files (ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None)), ('document', 'b1ae2e1cc0750e58726374061ea90ecc')) +paddle.fluid.layers.read_file (ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None), ('document', 'b0a1c2fc51c27a106da28f3308c41f5e')) +paddle.fluid.layers.shuffle (ArgSpec(args=['reader', 'buffer_size'], varargs=None, keywords=None, defaults=None), ('document', 'f967a73426db26f970bc70bfb03cffca')) +paddle.fluid.layers.batch (ArgSpec(args=['reader', 'batch_size'], varargs=None, keywords=None, defaults=None), ('document', 'f563d376d35e1a4c4db100fd11b381a0')) +paddle.fluid.layers.double_buffer (ArgSpec(args=['reader', 'place', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '07e5b796674796eb1ef3fee9c10d24e3')) +paddle.fluid.layers.random_data_generator (ArgSpec(args=['low', 'high', 'shapes', 'lod_levels', 'for_parallel'], varargs=None, keywords=None, defaults=(True,)), ('document', '9b7f0f86ec24bbc97643cadcb6499cff')) +paddle.fluid.layers.py_reader (ArgSpec(args=['capacity', 'shapes', 'dtypes', 'lod_levels', 'name', 'use_double_buffer'], varargs=None, keywords=None, defaults=(None, None, True)), ('document', '13dabc57863f62ab3141586784ee356b')) +paddle.fluid.layers.create_py_reader_by_data (ArgSpec(args=['capacity', 'feed_list', 'name', 'use_double_buffer'], varargs=None, keywords=None, defaults=(None, True)), ('document', '350f74d93fab9adb2ac4950f1c26416b')) +paddle.fluid.layers.Preprocessor.__init__ (ArgSpec(args=['self', 'reader', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.layers.Preprocessor.block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.layers.Preprocessor.inputs (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.layers.Preprocessor.outputs (ArgSpec(args=['self'], varargs='outs', keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.layers.load (ArgSpec(args=['out', 'file_path', 'load_as_fp16'], varargs=None, keywords=None, defaults=(None,)), ('document', '9d1a4bc97bbce9fa1d4f7a4200a771ff')) +paddle.fluid.layers.create_tensor (ArgSpec(args=['dtype', 'name', 'persistable'], varargs=None, keywords=None, defaults=(None, False)), ('document', 'c0c3d0194f83fff8ea99ce0820657dae')) +paddle.fluid.layers.create_parameter (ArgSpec(args=['shape', 'dtype', 'name', 'attr', 'is_bias', 'default_initializer'], varargs=None, keywords=None, defaults=(None, None, False, None)), ('document', 'd62b866c899bc1fedb5385f95b88e1f8')) +paddle.fluid.layers.create_global_var (ArgSpec(args=['shape', 'value', 'dtype', 'persistable', 'force_cpu', 'name'], varargs=None, keywords=None, defaults=(False, False, None)), ('document', 'ab914fac893607e29ac6e52bbdbea1a4')) +paddle.fluid.layers.cast (ArgSpec(args=['x', 'dtype'], varargs=None, keywords=None, defaults=None), ('document', '60cb8f843d625abf33f8bf12455b8f99')) +paddle.fluid.layers.tensor_array_to_tensor (ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None)), ('document', 'b12717d3d4567e6119589f7f655b0cbb')) +paddle.fluid.layers.concat (ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(0, None)), ('document', 'b19b79be4f05e85d1d6cec642c9fb535')) +paddle.fluid.layers.sums (ArgSpec(args=['input', 'out'], varargs=None, keywords=None, defaults=(None,)), ('document', '42912092418620b4be07f36af31e7816')) +paddle.fluid.layers.assign (ArgSpec(args=['input', 'output'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b690184f3537df5501e4d9d8f31152a5')) +paddle.fluid.layers.fill_constant_batch_size_like (ArgSpec(args=['input', 'shape', 'dtype', 'value', 'input_dim_idx', 'output_dim_idx'], varargs=None, keywords=None, defaults=(0, 0)), ('document', 'd4059a2f5763036b07018d76429f9acb')) +paddle.fluid.layers.fill_constant (ArgSpec(args=['shape', 'dtype', 'value', 'force_cpu', 'out'], varargs=None, keywords=None, defaults=(False, None)), ('document', '1d8b14729639fa38509c79b9784740fa')) +paddle.fluid.layers.argmin (ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=(0,)), ('document', '2778a1d34be49263a51211885599ea37')) +paddle.fluid.layers.argmax (ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=(0,)), ('document', '04114996cfb98994ba222804a1a6109f')) +paddle.fluid.layers.argsort (ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(-1, None)), ('document', '68ec45c6fb6b93e47de9c9a0945fb98e')) +paddle.fluid.layers.ones (ArgSpec(args=['shape', 'dtype', 'force_cpu'], varargs=None, keywords=None, defaults=(False,)), ('document', 'b402489c62e668df42e7daceb63c142b')) +paddle.fluid.layers.zeros (ArgSpec(args=['shape', 'dtype', 'force_cpu'], varargs=None, keywords=None, defaults=(False,)), ('document', 'c155e2efc56ffa5ed4658cca0272e491')) +paddle.fluid.layers.reverse (ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=None), ('document', '8ee7cb6ca639e7460e825f953b65d94d')) +paddle.fluid.layers.has_inf (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '8f8c0306117ea441f20dcbbdba1f0ecc')) +paddle.fluid.layers.has_nan (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '2e53e83127dbfd86e7098bdfe9a549e8')) +paddle.fluid.layers.isfinite (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '0a437011c3906079fd8947ed3e52d292')) +paddle.fluid.layers.While.__init__ (ArgSpec(args=['self', 'cond', 'is_test', 'name'], varargs=None, keywords=None, defaults=(False, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.layers.While.block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.layers.Switch.__init__ (ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.layers.Switch.case (ArgSpec(args=['self', 'condition'], varargs=None, keywords=None, defaults=None), ('document', 'f7c7160014c1b46cfeda9dd5808d1789')) +paddle.fluid.layers.Switch.default (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '50853ae884df03d9c36703bb46d9ef07')) +paddle.fluid.layers.increment (ArgSpec(args=['x', 'value', 'in_place'], varargs=None, keywords=None, defaults=(1.0, True)), ('document', '73bb96ec4783ec1a11e760e8851b0e77')) +paddle.fluid.layers.array_write (ArgSpec(args=['x', 'i', 'array'], varargs=None, keywords=None, defaults=(None,)), ('document', '40b6d15f4c86b2b09df340d7778ad713')) +paddle.fluid.layers.create_array (ArgSpec(args=['dtype'], varargs=None, keywords=None, defaults=None), ('document', '2d4f20087080ba5105b55205ad5c5b6a')) +paddle.fluid.layers.less_than (ArgSpec(args=['x', 'y', 'force_cpu', 'cond'], varargs=None, keywords='ignored', defaults=(None, None)), ('document', '067bbc799c66289ca8b8924c26b6673f')) +paddle.fluid.layers.equal (ArgSpec(args=['x', 'y', 'cond'], varargs=None, keywords=None, defaults=(None,)), ('document', '80c29b1dc64718f0116de90d1ac88a77')) +paddle.fluid.layers.array_read (ArgSpec(args=['array', 'i'], varargs=None, keywords=None, defaults=None), ('document', '0275133f1dde2aed528b4d3230edf823')) +paddle.fluid.layers.array_length (ArgSpec(args=['array'], varargs=None, keywords=None, defaults=None), ('document', 'ffb8b9578ec66db565b223d313aa82a2')) +paddle.fluid.layers.IfElse.__init__ (ArgSpec(args=['self', 'cond', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.layers.IfElse.false_block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.layers.IfElse.input (ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.layers.IfElse.output (ArgSpec(args=['self'], varargs='outs', keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.layers.IfElse.true_block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.layers.DynamicRNN.__init__ (ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.layers.DynamicRNN.block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6d3e0a5d9aa519a9773a36e1620ea9b7')) +paddle.fluid.layers.DynamicRNN.memory (ArgSpec(args=['self', 'init', 'shape', 'value', 'need_reorder', 'dtype'], varargs=None, keywords=None, defaults=(None, None, 0.0, False, 'float32')), ('document', 'b9174d4e91505b0c8ecc193eb51e248d')) +paddle.fluid.layers.DynamicRNN.output (ArgSpec(args=['self'], varargs='outputs', keywords=None, defaults=None), ('document', 'b439a176a3328de8a75bdc5c08eece4a')) +paddle.fluid.layers.DynamicRNN.static_input (ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None), ('document', 'f29ad2478b6b2ad4f413d2936a331ea0')) +paddle.fluid.layers.DynamicRNN.step_input (ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None), ('document', '169d694d2224f62b4f3afdc3dbc19e95')) +paddle.fluid.layers.DynamicRNN.update_memory (ArgSpec(args=['self', 'ex_mem', 'new_mem'], varargs=None, keywords=None, defaults=None), ('document', '5d83987da13b98363d6a807a52d8024f')) +paddle.fluid.layers.StaticRNN.__init__ (ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.layers.StaticRNN.memory (ArgSpec(args=['self', 'init', 'shape', 'batch_ref', 'init_value', 'init_batch_dim_idx', 'ref_batch_dim_idx'], varargs=None, keywords=None, defaults=(None, None, None, 0.0, 0, 1)), ('document', 'c24e368e23afac1ed91a78a639d7a9c7')) +paddle.fluid.layers.StaticRNN.output (ArgSpec(args=['self'], varargs='outputs', keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.layers.StaticRNN.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.layers.StaticRNN.step_input (ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.layers.StaticRNN.step_output (ArgSpec(args=['self', 'o'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.layers.StaticRNN.update_memory (ArgSpec(args=['self', 'mem', 'var'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.layers.reorder_lod_tensor_by_rank (ArgSpec(args=['x', 'rank_table'], varargs=None, keywords=None, defaults=None), ('document', '3545f529ef04e8f6ecb76b47fa3df01a')) +paddle.fluid.layers.Print (ArgSpec(args=['input', 'first_n', 'message', 'summarize', 'print_tensor_name', 'print_tensor_type', 'print_tensor_shape', 'print_tensor_lod', 'print_phase'], varargs=None, keywords=None, defaults=(-1, None, -1, True, True, True, True, 'both')), ('document', '5fef91b0e21c93610785f2b1f7161732')) +paddle.fluid.layers.is_empty (ArgSpec(args=['x', 'cond'], varargs=None, keywords='ignored', defaults=(None,)), ('document', 'bbe578dbb49ad13e15b014e98c22b519')) +paddle.fluid.layers.sigmoid (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '29a25ba78de79152076cacfc5443137d')) +paddle.fluid.layers.logsigmoid (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '81ccb7acafd06c7728e11581f5d342e3')) +paddle.fluid.layers.exp (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e6b3e769413d96aab4176f96db25984b')) +paddle.fluid.layers.tanh (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e9d586a0b5bd05f67ee78048f9d503b6')) +paddle.fluid.layers.tanh_shrink (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '1e521554b9fdda9061ec6d306f0709b7')) +paddle.fluid.layers.softshrink (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '9eef31597bbafa2bd49691e072296e13')) +paddle.fluid.layers.sqrt (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '072a8541e0f632366bba10f67cb0db27')) +paddle.fluid.layers.abs (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '64650ac42cf82e9920cb0b172b1d29fd')) +paddle.fluid.layers.ceil (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'c75d67dc5fe28f68e4cfffead4f698ad')) +paddle.fluid.layers.floor (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '647b16c5da5ef909649ae02abb434973')) +paddle.fluid.layers.cos (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '485f2686bcc2fe37a4bd893769c8a3e2')) +paddle.fluid.layers.sin (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '01f1766aa76eff1df30147505b59f7c4')) +paddle.fluid.layers.round (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b47f5da13913d3e56bdb1e612a73f3f2')) +paddle.fluid.layers.reciprocal (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'cc6ac2f14f03c52aaa83a59bf83b8d26')) +paddle.fluid.layers.square (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '48dfb45d773dbc30126c3a7f777de5ee')) +paddle.fluid.layers.softplus (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '459c5781e9d1dd88283b7c5769d7872a')) +paddle.fluid.layers.softsign (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '80846bcd4bd457207457a6d5411f4148')) +paddle.fluid.layers.uniform_random (ArgSpec(args=['shape', 'dtype', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', -1.0, 1.0, 0)), ('document', '308b619af849caa82bbc31e897f5e641')) +paddle.fluid.layers.hard_shrink (ArgSpec(args=['x', 'threshold'], varargs=None, keywords=None, defaults=(None,)), ('document', 'c142f5884f3255e0d6075c286bbd531e')) +paddle.fluid.layers.cumsum (ArgSpec(args=['x', 'axis', 'exclusive', 'reverse'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '944d7c03057f5fc88bc78acd4d82f926')) +paddle.fluid.layers.thresholded_relu (ArgSpec(args=['x', 'threshold'], varargs=None, keywords=None, defaults=(None,)), ('document', '90566ea449ea4c681435546e2f70610a')) +paddle.fluid.layers.prior_box (ArgSpec(args=['input', 'image', 'min_sizes', 'max_sizes', 'aspect_ratios', 'variance', 'flip', 'clip', 'steps', 'offset', 'name', 'min_max_aspect_ratios_order'], varargs=None, keywords=None, defaults=(None, [1.0], [0.1, 0.1, 0.2, 0.2], False, False, [0.0, 0.0], 0.5, None, False)), ('document', '14cac0ee643fa6e026ad82aeeee75bd8')) +paddle.fluid.layers.density_prior_box (ArgSpec(args=['input', 'image', 'densities', 'fixed_sizes', 'fixed_ratios', 'variance', 'clip', 'steps', 'offset', 'flatten_to_2d', 'name'], varargs=None, keywords=None, defaults=(None, None, None, [0.1, 0.1, 0.2, 0.2], False, [0.0, 0.0], 0.5, False, None)), ('document', 'a0d762bb08de9ce93bc780aa57cd5cd9')) +paddle.fluid.layers.multi_box_head (ArgSpec(args=['inputs', 'image', 'base_size', 'num_classes', 'aspect_ratios', 'min_ratio', 'max_ratio', 'min_sizes', 'max_sizes', 'steps', 'step_w', 'step_h', 'offset', 'variance', 'flip', 'clip', 'kernel_size', 'pad', 'stride', 'name', 'min_max_aspect_ratios_order'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None, 0.5, [0.1, 0.1, 0.2, 0.2], True, False, 1, 0, 1, None, False)), ('document', 'a6ab47a2fe681e52fabb7057ddf0efdd')) +paddle.fluid.layers.bipartite_match (ArgSpec(args=['dist_matrix', 'match_type', 'dist_threshold', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '3ddb9b966f193900193a95a3df77c3c1')) +paddle.fluid.layers.target_assign (ArgSpec(args=['input', 'matched_indices', 'negative_indices', 'mismatch_value', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', 'c0b334f917828f95056f6ebe10907b1c')) +paddle.fluid.layers.detection_output (ArgSpec(args=['loc', 'scores', 'prior_box', 'prior_box_var', 'background_label', 'nms_threshold', 'nms_top_k', 'keep_top_k', 'score_threshold', 'nms_eta'], varargs=None, keywords=None, defaults=(0, 0.3, 400, 200, 0.01, 1.0)), ('document', 'c33093a82a46e3091e789e5572588db1')) +paddle.fluid.layers.ssd_loss (ArgSpec(args=['location', 'confidence', 'gt_box', 'gt_label', 'prior_box', 'prior_box_var', 'background_label', 'overlap_threshold', 'neg_pos_ratio', 'neg_overlap', 'loc_loss_weight', 'conf_loss_weight', 'match_type', 'mining_type', 'normalize', 'sample_size'], varargs=None, keywords=None, defaults=(None, 0, 0.5, 3.0, 0.5, 1.0, 1.0, 'per_prediction', 'max_negative', True, None)), ('document', '6d5028fd09d01ab82d296adc0ea95aee')) +paddle.fluid.layers.detection_map (ArgSpec(args=['detect_res', 'label', 'class_num', 'background_label', 'overlap_threshold', 'evaluate_difficult', 'has_state', 'input_states', 'out_states', 'ap_version'], varargs=None, keywords=None, defaults=(0, 0.3, True, None, None, None, 'integral')), ('document', '1467d91b50c22cd52103b4aa1ee9d0a1')) +paddle.fluid.layers.rpn_target_assign (ArgSpec(args=['bbox_pred', 'cls_logits', 'anchor_box', 'anchor_var', 'gt_boxes', 'is_crowd', 'im_info', 'rpn_batch_size_per_im', 'rpn_straddle_thresh', 'rpn_fg_fraction', 'rpn_positive_overlap', 'rpn_negative_overlap', 'use_random'], varargs=None, keywords=None, defaults=(256, 0.0, 0.5, 0.7, 0.3, True)), ('document', '1dddef3eb4b3cbd4df8e03ac480dbf97')) +paddle.fluid.layers.anchor_generator (ArgSpec(args=['input', 'anchor_sizes', 'aspect_ratios', 'variance', 'stride', 'offset', 'name'], varargs=None, keywords=None, defaults=(None, None, [0.1, 0.1, 0.2, 0.2], None, 0.5, None)), ('document', '23337cc57bbf5be73884b6bd0f849603')) +paddle.fluid.layers.roi_perspective_transform (ArgSpec(args=['input', 'rois', 'transformed_height', 'transformed_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1.0,)), ('document', '5761f9ed83654314416e24372b33bb84')) +paddle.fluid.layers.generate_proposal_labels (ArgSpec(args=['rpn_rois', 'gt_classes', 'is_crowd', 'gt_boxes', 'im_info', 'batch_size_per_im', 'fg_fraction', 'fg_thresh', 'bg_thresh_hi', 'bg_thresh_lo', 'bbox_reg_weights', 'class_nums', 'use_random'], varargs=None, keywords=None, defaults=(256, 0.25, 0.25, 0.5, 0.0, [0.1, 0.1, 0.2, 0.2], None, True)), ('document', '87863717edeb7fe87a1268976cbc015d')) +paddle.fluid.layers.generate_proposals (ArgSpec(args=['scores', 'bbox_deltas', 'im_info', 'anchors', 'variances', 'pre_nms_top_n', 'post_nms_top_n', 'nms_thresh', 'min_size', 'eta', 'name'], varargs=None, keywords=None, defaults=(6000, 1000, 0.5, 0.1, 1.0, None)), ('document', '57ab49f3f324f310b7eed322e7c1057a')) +paddle.fluid.layers.generate_mask_labels (ArgSpec(args=['im_info', 'gt_classes', 'is_crowd', 'gt_segms', 'rois', 'labels_int32', 'num_classes', 'resolution'], varargs=None, keywords=None, defaults=None), ('document', 'f73706a65468e9ca3e0bee4a31521b0a')) +paddle.fluid.layers.iou_similarity (ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '587845f60c5d97ffdf2dfd21da52eca1')) +paddle.fluid.layers.box_coder (ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'code_type', 'box_normalized', 'name', 'axis'], varargs=None, keywords=None, defaults=('encode_center_size', True, None, 0)), ('document', '032d0f4b7d8f6235ee5d91e473344f0e')) +paddle.fluid.layers.polygon_box_transform (ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '0e5ac2507723a0b5adec473f9556799b')) +paddle.fluid.layers.yolov3_loss (ArgSpec(args=['x', 'gtbox', 'gtlabel', 'anchors', 'anchor_mask', 'class_num', 'ignore_thresh', 'downsample_ratio', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '991e934c3e09abf0edec7c9c978b4691')) +paddle.fluid.layers.box_clip (ArgSpec(args=['input', 'im_info', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '397e9e02b451d99c56e20f268fa03f2e')) +paddle.fluid.layers.multiclass_nms (ArgSpec(args=['bboxes', 'scores', 'score_threshold', 'nms_top_k', 'keep_top_k', 'nms_threshold', 'normalized', 'nms_eta', 'background_label', 'name'], varargs=None, keywords=None, defaults=(0.3, True, 1.0, 0, None)), ('document', 'ca7d1107b6c5d2d6d8221039a220fde0')) +paddle.fluid.layers.accuracy (ArgSpec(args=['input', 'label', 'k', 'correct', 'total'], varargs=None, keywords=None, defaults=(1, None, None)), ('document', '9808534c12c5e739a10f73ebb0b4eafd')) +paddle.fluid.layers.auc (ArgSpec(args=['input', 'label', 'curve', 'num_thresholds', 'topk', 'slide_steps'], varargs=None, keywords=None, defaults=('ROC', 4095, 1, 1)), ('document', 'e0e95334fce92d16c2d9db6e7caffc47')) +paddle.fluid.layers.exponential_decay (ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,)), ('document', '98a5050bee8522fcea81aa795adaba51')) +paddle.fluid.layers.natural_exp_decay (ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,)), ('document', '676a7bc2a218691db50bca233903d21e')) +paddle.fluid.layers.inverse_time_decay (ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,)), ('document', 'd07e767d59c4a5e6c930f3e6756d3f82')) +paddle.fluid.layers.polynomial_decay (ArgSpec(args=['learning_rate', 'decay_steps', 'end_learning_rate', 'power', 'cycle'], varargs=None, keywords=None, defaults=(0.0001, 1.0, False)), ('document', '882634f420f626642f0874481263da40')) +paddle.fluid.layers.piecewise_decay (ArgSpec(args=['boundaries', 'values'], varargs=None, keywords=None, defaults=None), ('document', 'c717d9d1d78a53c809d01b8bc56f3cae')) +paddle.fluid.layers.noam_decay (ArgSpec(args=['d_model', 'warmup_steps'], varargs=None, keywords=None, defaults=None), ('document', 'd9a95746353fd574be36dc28d8726c28')) +paddle.fluid.layers.append_LARS (ArgSpec(args=['params_grads', 'learning_rate', 'weight_decay'], varargs=None, keywords=None, defaults=None), ('document', 'd24fa1e7d62ac8a534fc6a86002f84f8')) +paddle.fluid.layers.cosine_decay (ArgSpec(args=['learning_rate', 'step_each_epoch', 'epochs'], varargs=None, keywords=None, defaults=None), ('document', '9588c64c26ffaef3c466e404a6af9d9b')) +paddle.fluid.contrib.InitState.__init__ (ArgSpec(args=['self', 'init', 'shape', 'value', 'init_boot', 'need_reorder', 'dtype'], varargs=None, keywords=None, defaults=(None, None, 0.0, None, False, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.contrib.StateCell.__init__ (ArgSpec(args=['self', 'inputs', 'states', 'out_state', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.contrib.StateCell.compute_state (ArgSpec(args=['self', 'inputs'], varargs=None, keywords=None, defaults=None), ('document', '92973b3f222081a1d17069c683cf4a99')) +paddle.fluid.contrib.StateCell.get_input (ArgSpec(args=['self', 'input_name'], varargs=None, keywords=None, defaults=None), ('document', '6f24a007cfa184e32f01a960703bfd70')) +paddle.fluid.contrib.StateCell.get_state (ArgSpec(args=['self', 'state_name'], varargs=None, keywords=None, defaults=None), ('document', '630a4945cfe659ea4f307598fbbce5d2')) +paddle.fluid.contrib.StateCell.out_state (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '7ad681dff0393ddf13a724194e720f28')) +paddle.fluid.contrib.StateCell.set_state (ArgSpec(args=['self', 'state_name', 'state_value'], varargs=None, keywords=None, defaults=None), ('document', 'd4e0e08cd5d9d9a571cbc52d114f5ae9')) +paddle.fluid.contrib.StateCell.state_updater (ArgSpec(args=['self', 'updater'], varargs=None, keywords=None, defaults=None), ('document', 'd5afe1b7665d94fb023b15cf913ca510')) +paddle.fluid.contrib.StateCell.update_states (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'fe0b0f1338723516a35a30247899c81b')) +paddle.fluid.contrib.TrainingDecoder.__init__ (ArgSpec(args=['self', 'state_cell', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.contrib.TrainingDecoder.block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '98d88fa1c989748410a12517c6a585bf')) +paddle.fluid.contrib.TrainingDecoder.output (ArgSpec(args=['self'], varargs='outputs', keywords=None, defaults=None), ('document', 'f0a457dee586559036202087ce2eff69')) +paddle.fluid.contrib.TrainingDecoder.static_input (ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None), ('document', 'a024c72664fe815068423ba630b7658a')) +paddle.fluid.contrib.TrainingDecoder.step_input (ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None), ('document', '4659db7a888a2495e71c1838a0483909')) +paddle.fluid.contrib.BeamSearchDecoder.__init__ (ArgSpec(args=['self', 'state_cell', 'init_ids', 'init_scores', 'target_dict_dim', 'word_dim', 'input_var_dict', 'topk_size', 'sparse_emb', 'max_len', 'beam_size', 'end_id', 'name'], varargs=None, keywords=None, defaults=({}, 50, True, 100, 1, 1, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.contrib.BeamSearchDecoder.block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '98d88fa1c989748410a12517c6a585bf')) +paddle.fluid.contrib.BeamSearchDecoder.decode (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '1e47c60f080c1343ebb6ceaef89656b2')) +paddle.fluid.contrib.BeamSearchDecoder.early_stop (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '3a84a7454ed6707f79b9e954d92a7575')) +paddle.fluid.contrib.BeamSearchDecoder.read_array (ArgSpec(args=['self', 'init', 'is_ids', 'is_scores'], varargs=None, keywords=None, defaults=(False, False)), ('document', 'aa89eb8fd5e4cabaf5cc1bcae14665a4')) +paddle.fluid.contrib.BeamSearchDecoder.update_array (ArgSpec(args=['self', 'array', 'value'], varargs=None, keywords=None, defaults=None), ('document', '5754e9b3212b7c09497151516a0de5a7')) +paddle.fluid.contrib.memory_usage (ArgSpec(args=['program', 'batch_size'], varargs=None, keywords=None, defaults=None), ('document', '8fcb2f93bb743693baa8d4860a5ccc47')) +paddle.fluid.contrib.op_freq_statistic (ArgSpec(args=['program'], varargs=None, keywords=None, defaults=None), ('document', '4d43687113c4bf5b29d15aee2f4e4afa')) +paddle.fluid.contrib.QuantizeTranspiler.__init__ (ArgSpec(args=['self', 'weight_bits', 'activation_bits', 'activation_quantize_type', 'weight_quantize_type', 'window_size'], varargs=None, keywords=None, defaults=(8, 8, 'abs_max', 'abs_max', 10000)), ('document', '14b39f1fcd5667ff556b1aad94357d1d')) +paddle.fluid.contrib.QuantizeTranspiler.convert_to_int8 (ArgSpec(args=['self', 'program', 'place', 'scope'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.contrib.QuantizeTranspiler.freeze_program (ArgSpec(args=['self', 'program', 'place', 'fuse_bn', 'scope'], varargs=None, keywords=None, defaults=(False, None)), ('document', '909675a1ab055c69b436a7893fcae4fd')) +paddle.fluid.contrib.QuantizeTranspiler.training_transpile (ArgSpec(args=['self', 'program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None)), ('document', '6dd9909f10b283ba2892a99058a72884')) +paddle.fluid.contrib.Calibrator.__init__ (ArgSpec(args=['self'], varargs='args', keywords='kwargs', defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.contrib.Calibrator.sample_data (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '3b8c85ca1e2cf753cc8c90a6c6992958')) +paddle.fluid.contrib.Calibrator.save_int8_model (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.contrib.reader.ctr_reader.ctr_reader (ArgSpec(args=['feed_dict', 'file_type', 'file_format', 'dense_slot_index', 'sparse_slot_index', 'capacity', 'thread_num', 'batch_size', 'file_list', 'slots', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b2ebf3de2a6ef1af2c3b88d2db7591ab')) +paddle.fluid.contrib.build_compressor (ArgSpec(args=['place', 'data_reader', 'data_feeder', 'scope', 'metrics', 'epoch', 'config'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.contrib.CompressPass.__init__ (ArgSpec(args=['self', 'place', 'data_reader', 'data_feeder', 'scope', 'metrics', 'epoch', 'program_exe'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.contrib.CompressPass.add_strategy (ArgSpec(args=['self', 'strategy'], varargs=None, keywords=None, defaults=None), ('document', '3bf6010b6f47d3c86df0ec8957be95e0')) +paddle.fluid.contrib.CompressPass.apply (ArgSpec(args=['self', 'graph'], varargs=None, keywords=None, defaults=None), ('document', 'a92bf85d4b59bd4f2ac1706d7c4899a6')) +paddle.fluid.contrib.ImitationGraph.__init__ (ArgSpec(args=['self', 'program'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.contrib.ImitationGraph.all_parameters (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.contrib.SensitivePruneStrategy.__init__ (ArgSpec(args=['self', 'pruner', 'start_epoch', 'end_epoch', 'delta_rate', 'acc_loss_threshold', 'sensitivities'], varargs=None, keywords=None, defaults=(None, 0, 10, 0.2, 0.2, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.contrib.SensitivePruneStrategy.on_batch_begin (ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.contrib.SensitivePruneStrategy.on_batch_end (ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.contrib.SensitivePruneStrategy.on_compress_begin (ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.contrib.SensitivePruneStrategy.on_compress_end (ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.contrib.SensitivePruneStrategy.on_epoch_begin (ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.contrib.SensitivePruneStrategy.on_epoch_end (ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.contrib.MagnitudePruner.__init__ (ArgSpec(args=['self', 'threshold'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.contrib.MagnitudePruner.prune (ArgSpec(args=['self', 'param', 'threshold'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.contrib.RatioPruner.__init__ (ArgSpec(args=['self', 'ratios'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e7a81a325b296a9ca502ee5adb4fc85d')) +paddle.fluid.contrib.RatioPruner.prune (ArgSpec(args=['self', 'param', 'ratio'], varargs=None, keywords=None, defaults=(None,)), ('document', '358cbf2978c91028fb96a195a9884645')) +paddle.fluid.contrib.load_persistables_for_increment (ArgSpec(args=['dirname', 'executor', 'program', 'lookup_table_var', 'lookup_table_var_path'], varargs=None, keywords=None, defaults=None), ('document', '11fbf7e8dd2289805de291b453a33ee7')) +paddle.fluid.contrib.load_persistables_for_inference (ArgSpec(args=['dirname', 'executor', 'program', 'lookup_table_var_name'], varargs=None, keywords=None, defaults=None), ('document', '5b5577bb3d24070da819674255d16196')) +paddle.fluid.contrib.convert_dist_to_sparse_program (ArgSpec(args=['program'], varargs=None, keywords=None, defaults=None), ('document', '4efbd93876832d4d35497cdbc7a1e6d8')) +paddle.fluid.contrib.HDFSClient.__init__ (ArgSpec(args=['self', 'hadoop_home', 'configs'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.contrib.HDFSClient.delete (ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=None), ('document', 'c3721aa2d4d9ef5a857dd47b2681c03e')) +paddle.fluid.contrib.HDFSClient.download (ArgSpec(args=['self', 'hdfs_path', 'local_path', 'overwrite', 'unzip'], varargs=None, keywords=None, defaults=(False, False)), ('document', 'ca55bde92184d3fd0f9f5c963b25e634')) +paddle.fluid.contrib.HDFSClient.is_dir (ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=(None,)), ('document', '45bde1bae02605a205c8245b58b9156d')) +paddle.fluid.contrib.HDFSClient.is_exist (ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=(None,)), ('document', 'be9c94bccff7ba0c1d95883ac62b5864')) +paddle.fluid.contrib.HDFSClient.ls (ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=None), ('document', '808acac504870c7e46594b95674f8a86')) +paddle.fluid.contrib.HDFSClient.lsr (ArgSpec(args=['self', 'hdfs_path', 'only_file', 'sort'], varargs=None, keywords=None, defaults=(True, True)), ('document', 'fae835aa3354eb6a0434c0f9ba3c2747')) +paddle.fluid.contrib.HDFSClient.make_local_dirs (ArgSpec(args=['local_path'], varargs=None, keywords=None, defaults=None), ('document', 'e76b89c8e7f019b5da576c0026fcf689')) +paddle.fluid.contrib.HDFSClient.makedirs (ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=None), ('document', '44d9972aae390aedf40aaea731a37e4b')) +paddle.fluid.contrib.HDFSClient.rename (ArgSpec(args=['self', 'hdfs_src_path', 'hdfs_dst_path', 'overwrite'], varargs=None, keywords=None, defaults=(False,)), ('document', '0eb133644d9a9f4da45bb39261ff0955')) +paddle.fluid.contrib.HDFSClient.upload (ArgSpec(args=['self', 'hdfs_path', 'local_path', 'overwrite', 'retry_times'], varargs=None, keywords=None, defaults=(False, 5)), ('document', '7d053b4bfd6dcfdd2c9dda0e0dbd9665')) +paddle.fluid.contrib.multi_download (ArgSpec(args=['client', 'hdfs_path', 'local_path', 'trainer_id', 'trainers', 'multi_processes'], varargs=None, keywords=None, defaults=(5,)), ('document', '100927be598ed8f9eaa1f3ef1b23568a')) +paddle.fluid.contrib.multi_upload (ArgSpec(args=['client', 'hdfs_path', 'local_path', 'multi_processes', 'overwrite', 'sync'], varargs=None, keywords=None, defaults=(5, False, True)), ('document', '183f34c83d30dbe16e09e8716c41958a')) +paddle.fluid.transpiler.DistributeTranspiler.__init__ (ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.transpiler.DistributeTranspiler.get_pserver_program (ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None), ('document', '292ab72977afbe58e6a3bde175452680')) +paddle.fluid.transpiler.DistributeTranspiler.get_pserver_programs (ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None), ('document', '78f4949aedf317666a89ca74b3748ba8')) +paddle.fluid.transpiler.DistributeTranspiler.get_startup_program (ArgSpec(args=['self', 'endpoint', 'pserver_program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'd796fc0c8d51503b556fcf6dc15c4f0c')) +paddle.fluid.transpiler.DistributeTranspiler.get_trainer_program (ArgSpec(args=['self', 'wait_port'], varargs=None, keywords=None, defaults=(True,)), ('document', '736330e31a7a54abccc0c7fd9119d9ff')) +paddle.fluid.transpiler.DistributeTranspiler.transpile (ArgSpec(args=['self', 'trainer_id', 'program', 'pservers', 'trainers', 'sync_mode', 'startup_program', 'current_endpoint'], varargs=None, keywords=None, defaults=(None, '127.0.0.1:6174', 1, True, None, '127.0.0.1:6174')), ('document', '06ce55338dfe96311ad1078235ab3bf4')) +paddle.fluid.transpiler.memory_optimize (ArgSpec(args=['input_program', 'skip_opt_set', 'print_log', 'level', 'skip_grads'], varargs=None, keywords=None, defaults=(None, False, 0, False)), ('document', 'eda17d0f1639bc6ca215cecf87f588a4')) +paddle.fluid.transpiler.release_memory (ArgSpec(args=['input_program', 'skip_opt_set'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ac4114d3df16264f1946deb3a8434a6f')) +paddle.fluid.transpiler.HashName.__init__ (ArgSpec(args=['self', 'pserver_endpoints'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.transpiler.HashName.dispatch (ArgSpec(args=['self', 'varlist'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.transpiler.HashName.reset (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.transpiler.RoundRobin.__init__ (ArgSpec(args=['self', 'pserver_endpoints'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.transpiler.RoundRobin.dispatch (ArgSpec(args=['self', 'varlist'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.transpiler.RoundRobin.reset (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.transpiler.DistributeTranspilerConfig.__init__ -paddle.fluid.nets.simple_img_conv_pool ArgSpec(args=['input', 'num_filters', 'filter_size', 'pool_size', 'pool_stride', 'pool_padding', 'pool_type', 'global_pooling', 'conv_stride', 'conv_padding', 'conv_dilation', 'conv_groups', 'param_attr', 'bias_attr', 'act', 'use_cudnn'], varargs=None, keywords=None, defaults=(0, 'max', False, 1, 0, 1, 1, None, None, None, True)) -paddle.fluid.nets.sequence_conv_pool ArgSpec(args=['input', 'num_filters', 'filter_size', 'param_attr', 'act', 'pool_type', 'bias_attr'], varargs=None, keywords=None, defaults=(None, 'sigmoid', 'max', None)) -paddle.fluid.nets.glu ArgSpec(args=['input', 'dim'], varargs=None, keywords=None, defaults=(-1,)) -paddle.fluid.nets.scaled_dot_product_attention ArgSpec(args=['queries', 'keys', 'values', 'num_heads', 'dropout_rate'], varargs=None, keywords=None, defaults=(1, 0.0)) -paddle.fluid.nets.img_conv_group ArgSpec(args=['input', 'conv_num_filter', 'pool_size', 'conv_padding', 'conv_filter_size', 'conv_act', 'param_attr', 'conv_with_batchnorm', 'conv_batchnorm_drop_rate', 'pool_stride', 'pool_type', 'use_cudnn'], varargs=None, keywords=None, defaults=(1, 3, None, None, False, 0.0, 1, 'max', True)) -paddle.fluid.optimizer.SGDOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'regularization', 'name'], varargs=None, keywords=None, defaults=(None, None)) -paddle.fluid.optimizer.SGDOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None) -paddle.fluid.optimizer.SGDOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)) -paddle.fluid.optimizer.SGDOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)) -paddle.fluid.optimizer.MomentumOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'momentum', 'use_nesterov', 'regularization', 'name'], varargs=None, keywords=None, defaults=(False, None, None)) -paddle.fluid.optimizer.MomentumOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None) -paddle.fluid.optimizer.MomentumOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)) -paddle.fluid.optimizer.MomentumOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)) -paddle.fluid.optimizer.AdagradOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'epsilon', 'regularization', 'name', 'initial_accumulator_value'], varargs=None, keywords=None, defaults=(1e-06, None, None, 0.0)) -paddle.fluid.optimizer.AdagradOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None) -paddle.fluid.optimizer.AdagradOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)) -paddle.fluid.optimizer.AdagradOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)) -paddle.fluid.optimizer.AdamOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name', 'lazy_mode'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None, False)) -paddle.fluid.optimizer.AdamOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None) -paddle.fluid.optimizer.AdamOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)) -paddle.fluid.optimizer.AdamOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)) -paddle.fluid.optimizer.AdamaxOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None)) -paddle.fluid.optimizer.AdamaxOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None) -paddle.fluid.optimizer.AdamaxOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)) -paddle.fluid.optimizer.AdamaxOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)) -paddle.fluid.optimizer.DecayedAdagradOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'decay', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.95, 1e-06, None, None)) -paddle.fluid.optimizer.DecayedAdagradOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None) -paddle.fluid.optimizer.DecayedAdagradOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)) -paddle.fluid.optimizer.DecayedAdagradOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)) -paddle.fluid.optimizer.FtrlOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'l1', 'l2', 'lr_power', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.0, 0.0, -0.5, None, None)) -paddle.fluid.optimizer.FtrlOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None) -paddle.fluid.optimizer.FtrlOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)) -paddle.fluid.optimizer.FtrlOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)) -paddle.fluid.optimizer.RMSPropOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'rho', 'epsilon', 'momentum', 'centered', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.95, 1e-06, 0.0, False, None, None)) -paddle.fluid.optimizer.RMSPropOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None) -paddle.fluid.optimizer.RMSPropOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)) -paddle.fluid.optimizer.RMSPropOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)) -paddle.fluid.optimizer.AdadeltaOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'epsilon', 'rho', 'regularization', 'name'], varargs=None, keywords=None, defaults=(1e-06, 0.95, None, None)) -paddle.fluid.optimizer.AdadeltaOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None) -paddle.fluid.optimizer.AdadeltaOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)) -paddle.fluid.optimizer.AdadeltaOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)) -paddle.fluid.optimizer.ModelAverage.__init__ ArgSpec(args=['self', 'average_window_rate', 'min_average_window', 'max_average_window', 'regularization', 'name'], varargs=None, keywords=None, defaults=(10000, 10000, None, None)) -paddle.fluid.optimizer.ModelAverage.apply ArgSpec(args=['self', 'executor', 'need_restore'], varargs=None, keywords=None, defaults=(True,)) -paddle.fluid.optimizer.ModelAverage.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None) -paddle.fluid.optimizer.ModelAverage.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)) -paddle.fluid.optimizer.ModelAverage.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)) -paddle.fluid.optimizer.ModelAverage.restore ArgSpec(args=['self', 'executor'], varargs=None, keywords=None, defaults=None) -paddle.fluid.optimizer.LarsMomentumOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'momentum', 'lars_coeff', 'lars_weight_decay', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.0005, None, None)) -paddle.fluid.optimizer.LarsMomentumOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None) -paddle.fluid.optimizer.LarsMomentumOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)) -paddle.fluid.optimizer.LarsMomentumOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)) -paddle.fluid.backward.append_backward ArgSpec(args=['loss', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None)) -paddle.fluid.regularizer.L1DecayRegularizer.__init__ ArgSpec(args=['self', 'regularization_coeff'], varargs=None, keywords=None, defaults=(0.0,)) -paddle.fluid.regularizer.L2DecayRegularizer.__init__ ArgSpec(args=['self', 'regularization_coeff'], varargs=None, keywords=None, defaults=(0.0,)) +paddle.fluid.nets.simple_img_conv_pool (ArgSpec(args=['input', 'num_filters', 'filter_size', 'pool_size', 'pool_stride', 'pool_padding', 'pool_type', 'global_pooling', 'conv_stride', 'conv_padding', 'conv_dilation', 'conv_groups', 'param_attr', 'bias_attr', 'act', 'use_cudnn'], varargs=None, keywords=None, defaults=(0, 'max', False, 1, 0, 1, 1, None, None, None, True)), ('document', 'e0f67f35abf27f666f81003113b90244')) +paddle.fluid.nets.sequence_conv_pool (ArgSpec(args=['input', 'num_filters', 'filter_size', 'param_attr', 'act', 'pool_type', 'bias_attr'], varargs=None, keywords=None, defaults=(None, 'sigmoid', 'max', None)), ('document', '48c434dd7bb827f69d90e5135d77470f')) +paddle.fluid.nets.glu (ArgSpec(args=['input', 'dim'], varargs=None, keywords=None, defaults=(-1,)), ('document', '08c1c57e1db6b20bf87b264cb7cf3ca8')) +paddle.fluid.nets.scaled_dot_product_attention (ArgSpec(args=['queries', 'keys', 'values', 'num_heads', 'dropout_rate'], varargs=None, keywords=None, defaults=(1, 0.0)), ('document', '921714c9bfb351b41403418265393203')) +paddle.fluid.nets.img_conv_group (ArgSpec(args=['input', 'conv_num_filter', 'pool_size', 'conv_padding', 'conv_filter_size', 'conv_act', 'param_attr', 'conv_with_batchnorm', 'conv_batchnorm_drop_rate', 'pool_stride', 'pool_type', 'use_cudnn'], varargs=None, keywords=None, defaults=(1, 3, None, None, False, 0.0, 1, 'max', True)), ('document', '3802be78fbfb206dae64a2d9f8480970')) +paddle.fluid.optimizer.SGDOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'regularization', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.optimizer.SGDOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871')) +paddle.fluid.optimizer.SGDOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) +paddle.fluid.optimizer.SGDOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea')) +paddle.fluid.optimizer.MomentumOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'momentum', 'use_nesterov', 'regularization', 'name'], varargs=None, keywords=None, defaults=(False, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.optimizer.MomentumOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871')) +paddle.fluid.optimizer.MomentumOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) +paddle.fluid.optimizer.MomentumOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea')) +paddle.fluid.optimizer.AdagradOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'epsilon', 'regularization', 'name', 'initial_accumulator_value'], varargs=None, keywords=None, defaults=(1e-06, None, None, 0.0)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.optimizer.AdagradOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871')) +paddle.fluid.optimizer.AdagradOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) +paddle.fluid.optimizer.AdagradOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea')) +paddle.fluid.optimizer.AdamOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name', 'lazy_mode'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None, False)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.optimizer.AdamOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871')) +paddle.fluid.optimizer.AdamOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) +paddle.fluid.optimizer.AdamOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea')) +paddle.fluid.optimizer.AdamaxOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.optimizer.AdamaxOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871')) +paddle.fluid.optimizer.AdamaxOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) +paddle.fluid.optimizer.AdamaxOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea')) +paddle.fluid.optimizer.DecayedAdagradOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'decay', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.95, 1e-06, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.optimizer.DecayedAdagradOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871')) +paddle.fluid.optimizer.DecayedAdagradOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) +paddle.fluid.optimizer.DecayedAdagradOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea')) +paddle.fluid.optimizer.FtrlOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'l1', 'l2', 'lr_power', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.0, 0.0, -0.5, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.optimizer.FtrlOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871')) +paddle.fluid.optimizer.FtrlOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) +paddle.fluid.optimizer.FtrlOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea')) +paddle.fluid.optimizer.RMSPropOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'rho', 'epsilon', 'momentum', 'centered', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.95, 1e-06, 0.0, False, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.optimizer.RMSPropOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871')) +paddle.fluid.optimizer.RMSPropOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) +paddle.fluid.optimizer.RMSPropOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea')) +paddle.fluid.optimizer.AdadeltaOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'epsilon', 'rho', 'regularization', 'name'], varargs=None, keywords=None, defaults=(1e-06, 0.95, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.optimizer.AdadeltaOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871')) +paddle.fluid.optimizer.AdadeltaOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) +paddle.fluid.optimizer.AdadeltaOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea')) +paddle.fluid.optimizer.ModelAverage.__init__ (ArgSpec(args=['self', 'average_window_rate', 'min_average_window', 'max_average_window', 'regularization', 'name'], varargs=None, keywords=None, defaults=(10000, 10000, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.optimizer.ModelAverage.apply (ArgSpec(args=['self', 'executor', 'need_restore'], varargs=None, keywords=None, defaults=(True,)), ('document', '46234a5470590feb336346f70a3db715')) +paddle.fluid.optimizer.ModelAverage.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871')) +paddle.fluid.optimizer.ModelAverage.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) +paddle.fluid.optimizer.ModelAverage.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea')) +paddle.fluid.optimizer.ModelAverage.restore (ArgSpec(args=['self', 'executor'], varargs=None, keywords=None, defaults=None), ('document', '18db9c70be9c4dd466f9844457b21bfe')) +paddle.fluid.optimizer.LarsMomentumOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'momentum', 'lars_coeff', 'lars_weight_decay', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.0005, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.optimizer.LarsMomentumOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871')) +paddle.fluid.optimizer.LarsMomentumOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) +paddle.fluid.optimizer.LarsMomentumOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea')) +paddle.fluid.backward.append_backward (ArgSpec(args=['loss', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '1a79bd7d10ae54ca763ec81bca36ba24')) +paddle.fluid.regularizer.L1DecayRegularizer.__init__ (ArgSpec(args=['self', 'regularization_coeff'], varargs=None, keywords=None, defaults=(0.0,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.regularizer.L2DecayRegularizer.__init__ (ArgSpec(args=['self', 'regularization_coeff'], varargs=None, keywords=None, defaults=(0.0,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.LoDTensor.__init__ 1. __init__(self: paddle.fluid.core.LoDTensor, arg0: List[List[int]]) -> None 2. __init__(self: paddle.fluid.core.LoDTensor) -> None paddle.fluid.LoDTensor.has_valid_recursive_sequence_lengths has_valid_recursive_sequence_lengths(self: paddle.fluid.core.LoDTensor) -> bool paddle.fluid.LoDTensor.lod lod(self: paddle.fluid.core.LoDTensor) -> List[List[int]] @@ -482,38 +483,38 @@ paddle.fluid.LoDTensorArray.append append(self: paddle.fluid.core.LoDTensorArray paddle.fluid.CPUPlace.__init__ __init__(self: paddle.fluid.core.CPUPlace) -> None paddle.fluid.CUDAPlace.__init__ __init__(self: paddle.fluid.core.CUDAPlace, arg0: int) -> None paddle.fluid.CUDAPinnedPlace.__init__ __init__(self: paddle.fluid.core.CUDAPinnedPlace) -> None -paddle.fluid.ParamAttr.__init__ ArgSpec(args=['self', 'name', 'initializer', 'learning_rate', 'regularizer', 'trainable', 'gradient_clip', 'do_model_average'], varargs=None, keywords=None, defaults=(None, None, 1.0, None, True, None, False)) -paddle.fluid.WeightNormParamAttr.__init__ ArgSpec(args=['self', 'dim', 'name', 'initializer', 'learning_rate', 'regularizer', 'trainable', 'gradient_clip', 'do_model_average'], varargs=None, keywords=None, defaults=(None, None, None, 1.0, None, True, None, False)) -paddle.fluid.DataFeeder.__init__ ArgSpec(args=['self', 'feed_list', 'place', 'program'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.DataFeeder.decorate_reader ArgSpec(args=['self', 'reader', 'multi_devices', 'num_places', 'drop_last'], varargs=None, keywords=None, defaults=(None, True)) -paddle.fluid.DataFeeder.feed ArgSpec(args=['self', 'iterable'], varargs=None, keywords=None, defaults=None) -paddle.fluid.DataFeeder.feed_parallel ArgSpec(args=['self', 'iterable', 'num_places'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.clip.ErrorClipByValue.__init__ ArgSpec(args=['self', 'max', 'min'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.clip.GradientClipByValue.__init__ ArgSpec(args=['self', 'max', 'min'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.clip.GradientClipByNorm.__init__ ArgSpec(args=['self', 'clip_norm'], varargs=None, keywords=None, defaults=None) -paddle.fluid.clip.GradientClipByGlobalNorm.__init__ ArgSpec(args=['self', 'clip_norm', 'group_name'], varargs=None, keywords=None, defaults=('default_group',)) -paddle.fluid.profiler.cuda_profiler ArgSpec(args=['output_file', 'output_mode', 'config'], varargs=None, keywords=None, defaults=(None, None)) -paddle.fluid.profiler.reset_profiler ArgSpec(args=[], varargs=None, keywords=None, defaults=None) -paddle.fluid.profiler.profiler ArgSpec(args=['state', 'sorted_key', 'profile_path'], varargs=None, keywords=None, defaults=(None, '/tmp/profile')) -paddle.fluid.profiler.start_profiler ArgSpec(args=['state'], varargs=None, keywords=None, defaults=None) -paddle.fluid.profiler.stop_profiler ArgSpec(args=['sorted_key', 'profile_path'], varargs=None, keywords=None, defaults=(None, '/tmp/profile')) -paddle.fluid.unique_name.generate ArgSpec(args=['key'], varargs=None, keywords=None, defaults=None) -paddle.fluid.unique_name.switch ArgSpec(args=['new_generator'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.unique_name.guard ArgSpec(args=['new_generator'], varargs=None, keywords=None, defaults=(None,)) -paddle.fluid.recordio_writer.convert_reader_to_recordio_file ArgSpec(args=['filename', 'reader_creator', 'feeder', 'compressor', 'max_num_records', 'feed_order'], varargs=None, keywords=None, defaults=(Compressor.Snappy, 1000, None)) -paddle.fluid.recordio_writer.convert_reader_to_recordio_files ArgSpec(args=['filename', 'batch_per_file', 'reader_creator', 'feeder', 'compressor', 'max_num_records', 'feed_order'], varargs=None, keywords=None, defaults=(Compressor.Snappy, 1000, None)) +paddle.fluid.ParamAttr.__init__ (ArgSpec(args=['self', 'name', 'initializer', 'learning_rate', 'regularizer', 'trainable', 'gradient_clip', 'do_model_average'], varargs=None, keywords=None, defaults=(None, None, 1.0, None, True, None, False)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.WeightNormParamAttr.__init__ (ArgSpec(args=['self', 'dim', 'name', 'initializer', 'learning_rate', 'regularizer', 'trainable', 'gradient_clip', 'do_model_average'], varargs=None, keywords=None, defaults=(None, None, None, 1.0, None, True, None, False)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.DataFeeder.__init__ (ArgSpec(args=['self', 'feed_list', 'place', 'program'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.DataFeeder.decorate_reader (ArgSpec(args=['self', 'reader', 'multi_devices', 'num_places', 'drop_last'], varargs=None, keywords=None, defaults=(None, True)), ('document', '0eed2f198dc73c08a41b61edbc755753')) +paddle.fluid.DataFeeder.feed (ArgSpec(args=['self', 'iterable'], varargs=None, keywords=None, defaults=None), ('document', '459e316301279dfd82001b46f0b8ffca')) +paddle.fluid.DataFeeder.feed_parallel (ArgSpec(args=['self', 'iterable', 'num_places'], varargs=None, keywords=None, defaults=(None,)), ('document', '543863d1f9d4853758adb613b8659e85')) +paddle.fluid.clip.ErrorClipByValue.__init__ (ArgSpec(args=['self', 'max', 'min'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.clip.GradientClipByValue.__init__ (ArgSpec(args=['self', 'max', 'min'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.clip.GradientClipByNorm.__init__ (ArgSpec(args=['self', 'clip_norm'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.clip.GradientClipByGlobalNorm.__init__ (ArgSpec(args=['self', 'clip_norm', 'group_name'], varargs=None, keywords=None, defaults=('default_group',)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.profiler.cuda_profiler (ArgSpec(args=['output_file', 'output_mode', 'config'], varargs=None, keywords=None, defaults=(None, None)), ('document', '2e2fb1cfc469a67f19fb578a2ed6be79')) +paddle.fluid.profiler.reset_profiler (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', '397ce757fabbe5c622e0c3458c41fcd0')) +paddle.fluid.profiler.profiler (ArgSpec(args=['state', 'sorted_key', 'profile_path'], varargs=None, keywords=None, defaults=(None, '/tmp/profile')), ('document', 'bd3a07eeb68e384f4d2d416cb2e28d86')) +paddle.fluid.profiler.start_profiler (ArgSpec(args=['state'], varargs=None, keywords=None, defaults=None), ('document', '88da8fb6dbebaee2f7520188a09574f9')) +paddle.fluid.profiler.stop_profiler (ArgSpec(args=['sorted_key', 'profile_path'], varargs=None, keywords=None, defaults=(None, '/tmp/profile')), ('document', 'a7500e39dd033f1e64f562e909333a8a')) +paddle.fluid.unique_name.generate (ArgSpec(args=['key'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.unique_name.switch (ArgSpec(args=['new_generator'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.unique_name.guard (ArgSpec(args=['new_generator'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.recordio_writer.convert_reader_to_recordio_file (ArgSpec(args=['filename', 'reader_creator', 'feeder', 'compressor', 'max_num_records', 'feed_order'], varargs=None, keywords=None, defaults=(Compressor.Snappy, 1000, None)), ('document', '65c7523e86f0c50bb729b01667f36310')) +paddle.fluid.recordio_writer.convert_reader_to_recordio_files (ArgSpec(args=['filename', 'batch_per_file', 'reader_creator', 'feeder', 'compressor', 'max_num_records', 'feed_order'], varargs=None, keywords=None, defaults=(Compressor.Snappy, 1000, None)), ('document', 'bc643f0f5f1b9db57ff0d8a57d379bd7')) paddle.fluid.Scope Scope() -> paddle.fluid.core._Scope -paddle.reader.map_readers ArgSpec(args=['func'], varargs='readers', keywords=None, defaults=None) -paddle.reader.buffered ArgSpec(args=['reader', 'size'], varargs=None, keywords=None, defaults=None) -paddle.reader.compose ArgSpec(args=[], varargs='readers', keywords='kwargs', defaults=None) -paddle.reader.chain ArgSpec(args=[], varargs='readers', keywords=None, defaults=None) -paddle.reader.shuffle ArgSpec(args=['reader', 'buf_size'], varargs=None, keywords=None, defaults=None) -paddle.reader.firstn ArgSpec(args=['reader', 'n'], varargs=None, keywords=None, defaults=None) -paddle.reader.xmap_readers ArgSpec(args=['mapper', 'reader', 'process_num', 'buffer_size', 'order'], varargs=None, keywords=None, defaults=(False,)) -paddle.reader.PipeReader.__init__ ArgSpec(args=['self', 'command', 'bufsize', 'file_type'], varargs=None, keywords=None, defaults=(8192, 'plain')) -paddle.reader.PipeReader.get_line ArgSpec(args=['self', 'cut_lines', 'line_break'], varargs=None, keywords=None, defaults=(True, '\n')) -paddle.reader.multiprocess_reader ArgSpec(args=['readers', 'use_pipe', 'queue_size'], varargs=None, keywords=None, defaults=(True, 1000)) -paddle.reader.Fake.__init__ ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None) -paddle.reader.creator.np_array ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None) -paddle.reader.creator.text_file ArgSpec(args=['path'], varargs=None, keywords=None, defaults=None) -paddle.reader.creator.recordio ArgSpec(args=['paths', 'buf_size'], varargs=None, keywords=None, defaults=(100,)) +paddle.reader.map_readers (ArgSpec(args=['func'], varargs='readers', keywords=None, defaults=None), ('document', '77cbadb09df588e21e5cc0819b69c87d')) +paddle.reader.buffered (ArgSpec(args=['reader', 'size'], varargs=None, keywords=None, defaults=None), ('document', '0d6186f109feceb99f60ec50a0a624cb')) +paddle.reader.compose (ArgSpec(args=[], varargs='readers', keywords='kwargs', defaults=None), ('document', '884291104e1c3f37f33aae44b7deeb0d')) +paddle.reader.chain (ArgSpec(args=[], varargs='readers', keywords=None, defaults=None), ('document', 'd22c34e379a53901ae67a6bca7f4def4')) +paddle.reader.shuffle (ArgSpec(args=['reader', 'buf_size'], varargs=None, keywords=None, defaults=None), ('document', 'e42ea6fee23ce26b23cb142cd1d6522d')) +paddle.reader.firstn (ArgSpec(args=['reader', 'n'], varargs=None, keywords=None, defaults=None), ('document', 'c5bb8f7dd4f917f1569a368aab5b8aad')) +paddle.reader.xmap_readers (ArgSpec(args=['mapper', 'reader', 'process_num', 'buffer_size', 'order'], varargs=None, keywords=None, defaults=(False,)), ('document', '283bc0b8a0e26ae186b8b9bee4aec560')) +paddle.reader.PipeReader.__init__ (ArgSpec(args=['self', 'command', 'bufsize', 'file_type'], varargs=None, keywords=None, defaults=(8192, 'plain')), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.reader.PipeReader.get_line (ArgSpec(args=['self', 'cut_lines', 'line_break'], varargs=None, keywords=None, defaults=(True, '\n')), ('document', '5f80a7ed70052f01665e4c74acccfa69')) +paddle.reader.multiprocess_reader (ArgSpec(args=['readers', 'use_pipe', 'queue_size'], varargs=None, keywords=None, defaults=(True, 1000)), ('document', '7d8b3a96e592107c893d5d51ce968ba0')) +paddle.reader.Fake.__init__ (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.reader.creator.np_array (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '28d457fbc9a71efa4ac91a3be179cada')) +paddle.reader.creator.text_file (ArgSpec(args=['path'], varargs=None, keywords=None, defaults=None), ('document', '44fe286ab6175a5464d3a961a68c266a')) +paddle.reader.creator.recordio (ArgSpec(args=['paths', 'buf_size'], varargs=None, keywords=None, defaults=(100,)), ('document', '11b3704ea42cfd537953387a7e58dae8')) diff --git a/paddle/fluid/framework/block_desc.cc b/paddle/fluid/framework/block_desc.cc index f4bb2f3e2fc2c8cf0376631d1996b395a8bc581a..0b7aaf11746d1931e10ad7e5368d9e053092500e 100644 --- a/paddle/fluid/framework/block_desc.cc +++ b/paddle/fluid/framework/block_desc.cc @@ -13,7 +13,11 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/fluid/framework/block_desc.h" + #include +#include +#include + #include "paddle/fluid/framework/operator.h" #include "paddle/fluid/framework/program_desc.h" @@ -155,6 +159,16 @@ void BlockDesc::RemoveOp(size_t s, size_t e) { ops_.erase(ops_.begin() + s, ops_.begin() + e); } +void BlockDesc::RemoveOpInternal(const OpDesc *op_desc) { + // TODO(minqiyang): make this faster + for (auto it = ops_.begin(); it != ops_.end(); ++it) { + if (it->get() == op_desc) { + ops_.erase(it); + break; + } + } +} + std::vector BlockDesc::AllOps() const { std::vector res; for (const auto &op : ops_) { @@ -163,20 +177,6 @@ std::vector BlockDesc::AllOps() const { return res; } -void BlockDesc::Clear() { - // clear all ops - ops_.clear(); - - // clear all vars which are not persistable - for (auto it = vars_.begin(); it != vars_.end();) { - if (it->second->Persistable()) { - ++it; - } else { - vars_.erase(it++); - } - } -} - void BlockDesc::Flush() { for (auto &op_desc : ops_) { op_desc->Flush(); diff --git a/paddle/fluid/framework/block_desc.h b/paddle/fluid/framework/block_desc.h index e192624a261e1291f1610e8e7e700d99a9d814d2..5c6e421516269a9b9865605400efa772f944a96f 100644 --- a/paddle/fluid/framework/block_desc.h +++ b/paddle/fluid/framework/block_desc.h @@ -93,12 +93,12 @@ class BlockDesc { */ void RemoveOp(size_t s, size_t e); + void RemoveOpInternal(const OpDesc *op_desc); + void RemoveVar(const std::string &name) { vars_.erase(name); } std::vector AllOps() const; - void Clear(); - size_t OpSize() const { return ops_.size(); } OpDesc *Op(int idx) const { return ops_.at(idx).get(); } diff --git a/paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.cc b/paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.cc index f03646705817b49d6d59e8beb3d91f625dc44bef..d4fbea9d95118666ababde811867e95c657c07de 100644 --- a/paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.cc +++ b/paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.cc @@ -12,7 +12,9 @@ // See the License for the specific language governing permissions and // limitations under the License. #include "paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.h" +#include #include +#include #include #include "paddle/fluid/framework/details/fetch_op_handle.h" #include "paddle/fluid/framework/details/multi_devices_helper.h" @@ -55,7 +57,7 @@ FeedFetchList FastThreadedSSAGraphExecutor::Run( std::vector fetch_ops; for (auto &fetch_var_name : fetch_tensors) { - for (auto &var_map : graph_->Get("vars")) { + for (auto &var_map : graph_->Get(details::kGraphVars)) { auto it = var_map.find(fetch_var_name); if (it != var_map.end()) { fetched_vars[fetch_var_name].push_back(*it->second.rbegin()); diff --git a/paddle/fluid/framework/executor.cc b/paddle/fluid/framework/executor.cc index 4323883fa5cc9b26a68c2980f3b7a49eca610543..c31d0beec306fe165164837cd15c95b4efd76af0 100644 --- a/paddle/fluid/framework/executor.cc +++ b/paddle/fluid/framework/executor.cc @@ -20,6 +20,7 @@ limitations under the License. */ #include "paddle/fluid/framework/lod_tensor_array.h" #include "paddle/fluid/framework/op_registry.h" #include "paddle/fluid/framework/reader.h" +#include "paddle/fluid/framework/threadpool.h" #include "paddle/fluid/framework/transfer_scope_cache.h" #include "paddle/fluid/framework/variable_helper.h" #include "paddle/fluid/operators/distributed/distributed.h" diff --git a/paddle/fluid/framework/ir/CMakeLists.txt b/paddle/fluid/framework/ir/CMakeLists.txt index 25d9afbcc8b2bc89ec47654f0dba4cb838be55b0..ca6b0229e906c0f8bfbf9ee6781013cb4ef7bbce 100644 --- a/paddle/fluid/framework/ir/CMakeLists.txt +++ b/paddle/fluid/framework/ir/CMakeLists.txt @@ -105,4 +105,5 @@ if (WITH_MKLDNN) cc_test(test_conv_bias_mkldnn_fuse_pass SRCS mkldnn/conv_bias_mkldnn_fuse_pass_tester.cc DEPS conv_bias_mkldnn_fuse_pass naive_executor) cc_test(test_conv_relu_mkldnn_fuse_pass SRCS mkldnn/conv_relu_mkldnn_fuse_pass_tester.cc DEPS conv_relu_mkldnn_fuse_pass) cc_test(test_conv_elementwise_add_mkldnn_fuse_pass SRCS mkldnn/conv_elementwise_add_mkldnn_fuse_pass_tester.cc DEPS conv_elementwise_add_mkldnn_fuse_pass) + cc_test(test_mkldnn_placement_pass SRCS mkldnn/mkldnn_placement_pass_tester.cc DEPS mkldnn_placement_pass) endif () diff --git a/paddle/fluid/framework/ir/mkldnn/conv_elementwise_add_mkldnn_fuse_pass_tester.cc b/paddle/fluid/framework/ir/mkldnn/conv_elementwise_add_mkldnn_fuse_pass_tester.cc index 9ef5c298b8cddfec094e9544dc6da9afdcaf0dab..433d89d8d3f20b3f87cd94901ebbf79cd99de813 100644 --- a/paddle/fluid/framework/ir/mkldnn/conv_elementwise_add_mkldnn_fuse_pass_tester.cc +++ b/paddle/fluid/framework/ir/mkldnn/conv_elementwise_add_mkldnn_fuse_pass_tester.cc @@ -44,10 +44,14 @@ struct TestIsReachable { using func = std::function; auto operator()(const std::unique_ptr& graph) -> func { - auto find_node = [](const std::unique_ptr& graph, - const std::string& name) -> Node* { + auto hash = [](const Node* node) -> std::string { + return node->Name() + std::to_string(node->id()); + }; + + auto find_node = [&](const std::unique_ptr& graph, + const std::string& name) -> Node* { for (auto& node : GraphTraits::DFS(*graph)) { - if (name == node.Name()) { + if (name == hash(&node)) { return &node; } } @@ -55,13 +59,17 @@ struct TestIsReachable { return nullptr; }; - return [&](std::string from, const std::string to) -> bool { + // update the from and to strings to hashed equivs in loop from graph traits + return [&](std::string from, std::string to) -> bool { if (from == to) return true; std::map visited; for (auto& node : GraphTraits::DFS(*graph)) { - visited[node.Name()] = false; + auto hashed = hash(&node); + if (node.Name() == from) from = hashed; + if (node.Name() == to) to = hashed; + visited[hashed] = false; } visited[from] = true; @@ -72,15 +80,15 @@ struct TestIsReachable { while (!queue.empty()) { auto cur = find_node(graph, queue.front()); queue.pop_front(); - if (cur == nullptr) return false; for (auto n : cur->outputs) { - if (n->Name() == to) return true; + auto hashed_name = hash(n); + if (hashed_name == to) return true; - if (!visited[n->Name()]) { - visited[n->Name()] = true; - queue.push_back(n->Name()); + if (!visited[hashed_name]) { + visited[hashed_name] = true; + queue.push_back(hashed_name); } } } @@ -166,6 +174,28 @@ TEST(ConvElementwiseAddMKLDNNFusePass, ConvolutionAsYWithElementwiseAddRelu) { RunPassAndAssert(&prog, "a", "relu", 1); } +TEST(ConvElementwiseAddMKLDNNFusePass, + ConvolutionProjectionAsYWithElementwiseAddRelu) { + auto prog = BuildProgramDesc({"a", "b", "c", "d", "e", "f"}, + {"bias", "weights", "bias2", "weights2"}); + + SetOp(&prog, "sigmoid", {{"X", "a"}}, {"Out", "b"}); + // right branch + SetOp(&prog, "conv2d", + {{"Input", "b"}, {"Bias", "bias"}, {"Filter", "weights"}}, + {"Output", "c"}); + + // left branch + SetOp(&prog, "conv2d", + {{"Input", "a"}, {"Bias", "bias2"}, {"Filter", "weights2"}}, + {"Output", "f"}); + + SetOp(&prog, "elementwise_add", {{"X", "f"}, {"Y", "c"}}, {"Out", "d"}); + SetOp(&prog, "relu", {{"X", "d"}}, {"Out", "e"}); + + RunPassAndAssert(&prog, "a", "relu", 2); +} + TEST(ConvElementwiseAddMKLDNNFusePass, ConvolutionAsYWithElementwiseAddReluNoBias) { auto prog = BuildProgramDesc({"a", "b", "c", "d", "e"}, {"weights"}); diff --git a/paddle/fluid/framework/ir/mkldnn/mkldnn_placement_pass.cc b/paddle/fluid/framework/ir/mkldnn/mkldnn_placement_pass.cc index 20e52410ffe3caa86450bc05bf3aabf5a5bce374..ccac65f3b3ad22d0f424ef9de9a7bd506e8ac862 100644 --- a/paddle/fluid/framework/ir/mkldnn/mkldnn_placement_pass.cc +++ b/paddle/fluid/framework/ir/mkldnn/mkldnn_placement_pass.cc @@ -21,7 +21,7 @@ namespace ir { std::unique_ptr MKLDNNPlacementPass::ApplyImpl( std::unique_ptr graph) const { - VLOG(3) << "Aplies MKL-DNN placement strategy."; + VLOG(3) << "Applies MKL-DNN placement strategy."; const auto& op_types_list = Get>("mkldnn_enabled_op_types"); for (const Node* n : graph->Nodes()) { diff --git a/paddle/fluid/framework/ir/mkldnn/mkldnn_placement_pass_tester.cc b/paddle/fluid/framework/ir/mkldnn/mkldnn_placement_pass_tester.cc new file mode 100644 index 0000000000000000000000000000000000000000..b6ec7e4d68b95125d630ce4a60635eb7b711e820 --- /dev/null +++ b/paddle/fluid/framework/ir/mkldnn/mkldnn_placement_pass_tester.cc @@ -0,0 +1,136 @@ +// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "paddle/fluid/framework/ir/mkldnn/mkldnn_placement_pass.h" + +#include +#include + +namespace paddle { +namespace framework { +namespace ir { + +void SetOp(ProgramDesc* prog, const std::string& type, const std::string& name, + const std::vector& inputs, + const std::vector& outputs, boost::tribool use_mkldnn) { + auto* op = prog->MutableBlock(0)->AppendOp(); + + op->SetType(type); + + if (!boost::indeterminate(use_mkldnn)) op->SetAttr("use_mkldnn", use_mkldnn); + + if (type == "conv2d") { + op->SetAttr("name", name); + op->SetInput("Input", {inputs[0]}); + op->SetInput("Filter", {inputs[1]}); + op->SetInput("Bias", {inputs[2]}); + } else if (type == "relu") { + op->SetInput("X", inputs); + } else if (type == "concat") { + op->SetAttr("axis", 1); + op->SetInput("X", {inputs[0], inputs[1]}); + } else if (type == "pool2d") { + op->SetInput("X", {inputs[0]}); + } else { + FAIL() << "Unexpected operator type."; + } + op->SetOutput("Out", {outputs[0]}); +} + +// operator use_mkldnn +// --------------------------------------- +// (a,b)->concat->c none +// (c,weights,bias)->conv->f none +// f->relu->g false +// g->pool->h false +// (h,weights2,bias2)->conv->k true +// k->relu->l true +ProgramDesc BuildProgramDesc() { + ProgramDesc prog; + + for (auto& v : + std::vector({"a", "b", "c", "weights", "bias", "f", "g", + "h", "weights2", "bias2", "k", "l"})) { + auto* var = prog.MutableBlock(0)->Var(v); + var->SetType(proto::VarType::SELECTED_ROWS); + if (v == "weights" || v == "bias") { + var->SetPersistable(true); + } + } + + SetOp(&prog, "concat", "concat1", std::vector({"a", "b"}), + std::vector({"c"}), boost::indeterminate); + SetOp(&prog, "conv2d", "conv1", + std::vector({"c", "weights", "bias"}), + std::vector({"f"}), boost::indeterminate); + SetOp(&prog, "relu", "relu1", std::vector({"f"}), + std::vector({"g"}), false); + SetOp(&prog, "pool2d", "pool1", std::vector({"g"}), + std::vector({"h"}), false); + SetOp(&prog, "conv2d", "conv2", + std::vector({"h", "weights2", "bias2"}), + std::vector({"k"}), true); + SetOp(&prog, "relu", "relu2", std::vector({"k"}), + std::vector({"l"}), true); + + return prog; +} + +void MainTest(std::initializer_list mkldnn_enabled_op_types, + unsigned expected_use_mkldnn_true_count) { + auto prog = BuildProgramDesc(); + + std::unique_ptr graph(new ir::Graph(prog)); + + auto pass = PassRegistry::Instance().Get("mkldnn_placement_pass"); + pass->Set("mkldnn_enabled_op_types", + new std::unordered_set(mkldnn_enabled_op_types)); + + graph = pass->Apply(std::move(graph)); + + unsigned use_mkldnn_true_count = 0; + + for (auto* node : graph->Nodes()) { + if (node->IsOp()) { + auto* op = node->Op(); + if (op->HasAttr("use_mkldnn") && + boost::get(op->GetAttr("use_mkldnn"))) { + ++use_mkldnn_true_count; + } + } + } + + EXPECT_EQ(use_mkldnn_true_count, expected_use_mkldnn_true_count); +} + +TEST(MKLDNNPlacementPass, enable_conv_relu) { + // 1 conv (1 conv is always true) + 2 relu (1 relu is always true) + 0 pool + MainTest({"conv2d", "relu"}, 3); +} + +TEST(MKLDNNPlacementPass, enable_relu_pool) { + // 1 conv (1 conv is always true) + 2 relu (1 relu is always true) + 1 pool + MainTest({"relu", "pool2d"}, 4); +} + +TEST(MKLDNNPlacementPass, enable_all) { + // 1 conv (1 conv is always true) + 2 relu (1 relu is always true) + 1 pool + MainTest({}, 4); +} + +} // namespace ir +} // namespace framework +} // namespace paddle + +USE_PASS(mkldnn_placement_pass); diff --git a/paddle/fluid/framework/op_registry.h b/paddle/fluid/framework/op_registry.h index 2c1648c81fc999c6306d5b08bc243f3ad21fec04..a53a81c270aeec1b6ee4ed30e77526f4ea2e7977 100644 --- a/paddle/fluid/framework/op_registry.h +++ b/paddle/fluid/framework/op_registry.h @@ -290,7 +290,7 @@ struct OpKernelRegistrarFunctorExIsType()) { - CheckTensorNANOrInf(vname, var->Get()); + CheckTensorNANOrInf(type_, vname, var->Get()); } else if (var->IsType()) { - CheckTensorNANOrInf(vname, var->Get().value()); + CheckTensorNANOrInf(type_, vname, + var->Get().value()); } } } diff --git a/paddle/fluid/framework/python_headers.h b/paddle/fluid/framework/python_headers.h index 422af19a13683dc9ae6377cac1b1ab2c2ac8f96b..8f9e3fad57f7bb87e78e334e741be23751417a78 100644 --- a/paddle/fluid/framework/python_headers.h +++ b/paddle/fluid/framework/python_headers.h @@ -24,3 +24,11 @@ limitations under the License. */ #pragma pop_macro("_XOPEN_SOURCE") #pragma pop_macro("_POSIX_C_SOURCE") + +#if !defined(PYBIND11_HIDDEN) +#ifdef _WIN32 +#define PYBIND11_HIDDEN __declspec(dllexport) +#else +#define PYBIND11_HIDDEN __attribute__((visibility("hidden"))) +#endif +#endif diff --git a/paddle/fluid/framework/tensor_util.cc b/paddle/fluid/framework/tensor_util.cc index 85d15c5d3faa5a3d021b12396f9f8ea7735f9148..89166bfd15f26e066d32a7191217a9b9a8977bda 100644 --- a/paddle/fluid/framework/tensor_util.cc +++ b/paddle/fluid/framework/tensor_util.cc @@ -14,6 +14,8 @@ #include "paddle/fluid/framework/tensor_util.h" #include #include +#include +#include #include #include "paddle/fluid/framework/data_type.h" diff --git a/paddle/fluid/imperative/layer.cc b/paddle/fluid/imperative/layer.cc index aff5cf24be7c41cf58929069768d4fdb34386ae6..012dfc1c7f66027bc5375794e0d70ed78e70e781 100644 --- a/paddle/fluid/imperative/layer.cc +++ b/paddle/fluid/imperative/layer.cc @@ -18,6 +18,7 @@ #include #include #include +#include #include #include "paddle/fluid/framework/lod_tensor.h" @@ -139,6 +140,8 @@ class Autograd { } } } + + ready_op->InvokeBackwardHooks(); } } @@ -156,8 +159,10 @@ class Autograd { for (auto it : candidate->pre_ops_) { for (OpBase* pre_op : it.second) { if (!pre_op) continue; - VLOG(5) << "op dep " << candidate->op_desc_->Type() << " <---- " - << it.first << " <---- " << pre_op->op_desc_->Type(); + VLOG(5) << "op dep " << candidate->op_desc_->Type() << " trace id " + << candidate->trace_id_ << " <---- " << it.first << " <---- " + << pre_op->op_desc_->Type() << " trace id " + << pre_op->trace_id_; if (visited.find(pre_op) == visited.end()) { visited.insert(pre_op); queue.push_back(pre_op); @@ -211,6 +216,7 @@ std::map> OpBase::ApplyGrad() { return {}; } + VLOG(3) << "apply op grad: " << op_desc_->Type(); std::vector grad_outputs; if (backward_id_ > 0) { VLOG(3) << "py_layer_grad"; @@ -272,6 +278,22 @@ std::map> OpBase::ApplyGrad() { return input_vars_; } +void OpBase::InvokeBackwardHooks() { + VLOG(3) << "call backward hooks, hooks num: " << backward_hooks_.size(); + + // call backward hooks + for (py::object& callable : backward_hooks_) { + callable(this); + } +} + +void OpBase::RegisterBackwardHooks(const py::object& callable) { + VLOG(3) << "Register backward hooks " << trace_id_; + + // TODO(minqiyang): check the callable format + backward_hooks_.push_back(callable); +} + void VarBase::RunBackward() { if (!pre_op_) return; diff --git a/paddle/fluid/imperative/layer.h b/paddle/fluid/imperative/layer.h index bbf614831ca817031b209fffec043495ea24d10f..7a9f33dc1e6cbc0c3ec1e649906fb0a8de047189 100644 --- a/paddle/fluid/imperative/layer.h +++ b/paddle/fluid/imperative/layer.h @@ -123,22 +123,32 @@ class VarBase { private: VarBase(framework::Variable* var, VarBase* grad, bool stop_gradient) - : var_desc_(nullptr), + : name_(), + var_desc_(nullptr), var_(var), grads_(grad), + block_(nullptr), + persistable_(false), stop_gradient_(stop_gradient), pre_op_(nullptr), + pre_op_out_name_(), pre_op_out_idx_(-1) {} public: virtual ~VarBase() { + // TODO(minqiyang): remove var desc from block desc if (var_) { delete var_; + var_ = nullptr; } if (grads_) { delete grads_; + grads_ = nullptr; } + + pre_op_ = nullptr; + pre_op_out_idx_ = -1; } inline OpBase* PreOp() const { return pre_op_; } @@ -151,6 +161,14 @@ class VarBase { void RunBackward(); + inline void ResetPreOp(OpBase* op) { + if (op == pre_op_) { + // clear pre_op info when op equals to var's pre_op + pre_op_ = nullptr; + pre_op_out_idx_ = -1; + } + } + void TrackPreOp(OpBase* pre_op, const std::string& pre_op_out_name, int pre_op_out_idx, bool pre_op_stop_gradient) { pre_op_ = pre_op; @@ -184,11 +202,15 @@ class VarBase { return string::Sprintf("%s@IGrad", var_desc_->Name()); } + std::string name_; framework::VarDesc* var_desc_; framework::Variable* var_; VarBase* grads_; + framework::BlockDesc* block_; + bool persistable_; + private: bool stop_gradient_; OpBase* pre_op_; @@ -199,15 +221,27 @@ class VarBase { /* The wrapper for OpDesc which holds a OpDesc and a OpDesc of its * gradient. This object should be managed totally by Python intepreter. */ -class OpBase { +class PYBIND11_HIDDEN OpBase { public: OpBase() : op_desc_(nullptr), forward_id_(-1), backward_id_(-1), - place_(platform::CPUPlace()) {} + trace_id_(-1), + place_(platform::CPUPlace()), + backward_hooks_() {} virtual ~OpBase() { + // TODO(minqiyang): remove op_desc from block_desc in tracer + // + // reset all output vars' pre op + for (auto iter : output_vars_) { + for (VarBase* var : iter.second) { + var->ResetPreOp(this); + } + } + + // release resource for (framework::OpDesc* desc : grad_op_descs_) { delete desc; } @@ -215,6 +249,10 @@ class OpBase { std::map> ApplyGrad(); + void RegisterBackwardHooks(const py::object& callable); + + void InvokeBackwardHooks(); + // One of `op_desc_` or `forward_id_` is set, not both. // For pure python PyLayer, use `forward_id_`, otherwise, use op_desc_. framework::OpDesc* op_desc_; @@ -225,6 +263,7 @@ class OpBase { // Note: each fwd op corresponds to a vector of bwd ops. std::vector grad_op_descs_; int backward_id_; + int trace_id_; platform::Place place_; @@ -239,6 +278,8 @@ class OpBase { std::vector grad_output_vars_; framework::BlockDesc* block_; + + std::vector backward_hooks_; }; class Layer { diff --git a/paddle/fluid/imperative/tracer.cc b/paddle/fluid/imperative/tracer.cc index 2993ab309027f9306c61023b55b1c061e0ebddc0..0cb1676372fdd35a762e897d269550f2d1e1ac36 100644 --- a/paddle/fluid/imperative/tracer.cc +++ b/paddle/fluid/imperative/tracer.cc @@ -14,15 +14,32 @@ #include "paddle/fluid/imperative/tracer.h" +#include #include +#include +#include #include "paddle/fluid/operators/math/math_function.h" #include "paddle/fluid/platform/device_context.h" #include "paddle/fluid/platform/enforce.h" +#ifdef WITH_GPERFTOOLS +#include "gperftools/profiler.h" +#endif + +DEFINE_string( + tracer_profile_fname, "", + "Profiler filename for imperative tracer, which generated by gperftools." + "Only valid when compiled `WITH_PROFILER=ON`. Empty if disable."); + namespace paddle { namespace imperative { +static std::once_flag gTracerProfileOnce; +#ifdef WITH_GPERFTOOLS +static bool gTracerProfilerStarted = false; +#endif + void CreateGradOp(const framework::OpDesc& op_desc, const std::unordered_set& no_grad_set, const std::vector& grad_sub_block, @@ -68,15 +85,36 @@ platform::Place GetExpectedPlace(platform::Place place, VarBasePtrMap inputs) { return result; } +Tracer::Tracer(framework::BlockDesc* root_block) : root_block_(root_block) { + if (!FLAGS_tracer_profile_fname.empty()) { + std::call_once(gTracerProfileOnce, [] { +#ifdef WITH_GPERFTOOLS + ProfilerStart(FLAGS_tracer_profile_fname.c_str()); + gTracerProfilerStarted = true; +#else + LOG(WARNING) << "Paddle is not compiled with gperftools. " + "FLAGS_tracer_profile_fname will be ignored"; +#endif + }); + } +} + std::set Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs, const VarBasePtrMap& outputs, framework::BlockDesc* block, const platform::Place expected_place, const bool stop_gradient) { +#ifdef WITH_GPERFTOOLS + if (gTracerProfilerStarted) { + ProfilerFlush(); + } +#endif + std::map vars; framework::OpDesc* op_desc = op->op_desc_; - VLOG(3) << "tracer tracing " << op_desc->Type(); + VLOG(3) << "tracer tracing " << op_desc->Type() << " trace id " + << op->trace_id_; op_desc->InferShape(*block); op_desc->InferVarType(block); @@ -99,11 +137,13 @@ std::set Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs, if (inp->PreOp() && !inp->IsStopGradient()) { op->pre_ops_[it.first].push_back(inp->PreOp()); op->pre_ops_out_idx_[it.first].push_back(inp->PreOpOutIdx()); + VLOG(3) << "add pre op " << inp->PreOp()->op_desc_->Type(); } else { op->pre_ops_[it.first].push_back(nullptr); } VLOG(3) << "input vname " << inp->var_desc_->Name() << " " - << inp->var_->IsInitialized(); + << inp->var_->IsInitialized() << " stop_gradient " + << inp->IsStopGradient(); } } @@ -155,6 +195,7 @@ std::set Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs, op->grad_input_vars_.resize(op->grad_op_descs_.size()); op->grad_output_vars_.resize(op->grad_op_descs_.size()); + for (size_t i = 0; i < op->grad_op_descs_.size(); ++i) { framework::OpDesc* grad_op_desc = op->grad_op_descs_[i]; for (auto it : grad_op_desc->Inputs()) { @@ -167,7 +208,6 @@ std::set Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs, PADDLE_ENFORCE(fwd_var_it != vars.end()); // Forward inputs or outputs. grad_in_vars.push_back(fwd_var_it->second->var_); - vars_saved_for_backward.insert(it.first); } else { VarBase* var = vars[var_it->second]; if (!var->grads_->var_->IsInitialized()) { @@ -177,6 +217,8 @@ std::set Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs, // Douts. grad_in_vars.push_back(var->grads_->var_); } + + vars_saved_for_backward.insert(it.first); } } diff --git a/paddle/fluid/imperative/tracer.h b/paddle/fluid/imperative/tracer.h index 98909e378f0e4188250fcb6efd9502dcc9740da4..8a0267c37f7c98a172fe0fa573955dc420952c0a 100644 --- a/paddle/fluid/imperative/tracer.h +++ b/paddle/fluid/imperative/tracer.h @@ -40,7 +40,7 @@ platform::Place GetExpectedPlace(platform::Place place, VarBasePtrMap inputs); class Tracer { public: - explicit Tracer(framework::BlockDesc* root_block) : root_block_(root_block) {} + explicit Tracer(framework::BlockDesc* root_block); virtual ~Tracer() {} diff --git a/paddle/fluid/inference/CMakeLists.txt b/paddle/fluid/inference/CMakeLists.txt index 157862016e3556902f6507e02417624363ed1029..762640d6d1ce12dff511fc7149e872efa834036c 100644 --- a/paddle/fluid/inference/CMakeLists.txt +++ b/paddle/fluid/inference/CMakeLists.txt @@ -16,6 +16,7 @@ add_subdirectory(utils) if (TENSORRT_FOUND) add_subdirectory(tensorrt) endif() +# add_subdirectory(anakin) get_property(fluid_modules GLOBAL PROPERTY FLUID_MODULES) get_property(cuda_modules GLOBAL PROPERTY CUDA_MODULES) diff --git a/paddle/fluid/inference/anakin/CMakeLists.txt b/paddle/fluid/inference/anakin/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..b418af62f8cae4513bcca24f057d1fe100bbea25 --- /dev/null +++ b/paddle/fluid/inference/anakin/CMakeLists.txt @@ -0,0 +1,4 @@ +cc_library(anakin_engine SRCS engine.cc) +target_link_libraries(anakin_engine anakin anakin_saber_common) +cc_test(test_anakin_engine SRCS test_anakin_engine.cc DEPS anakin_engine) +add_subdirectory(convert) diff --git a/paddle/fluid/inference/anakin/convert/CMakeLists.txt b/paddle/fluid/inference/anakin/convert/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..f5bfee861f14877b5a67bc48aeb14b8213a27370 --- /dev/null +++ b/paddle/fluid/inference/anakin/convert/CMakeLists.txt @@ -0,0 +1,2 @@ +cc_library(anakin_op_converter SRCS fc.cc registrar.cc DEPS anakin_engine framework_proto scope) +cc_test(test_anakin_fc SRCS test_fc_op.cc DEPS anakin_op_converter mul_op) diff --git a/paddle/fluid/inference/anakin/convert/fc.cc b/paddle/fluid/inference/anakin/convert/fc.cc new file mode 100644 index 0000000000000000000000000000000000000000..33a5aff1de2851ad55c2df83cc48ba86f8ded754 --- /dev/null +++ b/paddle/fluid/inference/anakin/convert/fc.cc @@ -0,0 +1,73 @@ +// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "paddle/fluid/inference/anakin/convert/fc.h" +#include + +using anakin::graph::GraphGlobalMem; +using anakin::AK_FLOAT; +using anakin::Precision; +using anakin::saber::NV; +using anakin::saber::X86; +using anakin::saber::Shape; +using anakin::PBlock; +using anakin::PTuple; + +namespace paddle { +namespace inference { +namespace anakin { + +void FcOpConverter::operator()(const framework::proto::OpDesc &op, + const framework::Scope &scope, bool test_mode) { + framework::OpDesc op_desc(op, nullptr); + PADDLE_ENFORCE_EQ(op_desc.Input("X").size(), 1); + PADDLE_ENFORCE_EQ(op_desc.Input("Y").size(), 1); + PADDLE_ENFORCE_EQ(op_desc.Output("Out").size(), 1); + + auto x_name = op_desc.Input("X").front(); + auto op_name = op_desc.Type() + ":" + op_desc.Output("Out").front(); + auto *y_v = scope.FindVar(op_desc.Input("Y").front()); + PADDLE_ENFORCE_NOT_NULL(y_v); + auto *y_t = y_v->GetMutable(); + + auto input_name = op_desc.Input("X").front(); + auto output_name = op_desc.Output("Out").front(); + + auto weight_shape = framework::vectorize2int(y_t->dims()); + engine_->AddOp(op_name, "Dense", {input_name}, {output_name}); + engine_->AddOpAttr(op_name, "bias_term", false); + engine_->AddOpAttr(op_name, "axis", 1); + int out_dim = weight_shape[1]; + engine_->AddOpAttr(op_name, "out_dim", out_dim); + + weight_shape.push_back(1); + weight_shape.push_back(1); + Shape anakin_shape(weight_shape); + + framework::LoDTensor weight_tensor; + weight_tensor.Resize(y_t->dims()); + TensorCopySync((*y_t), platform::CPUPlace(), &weight_tensor); + + auto *weight1 = + GraphGlobalMem::Global().template new_block(anakin_shape); + float *cpu_data = static_cast(weight1->h_tensor().mutable_data()); + std::copy_n(weight_tensor.data(), weight_tensor.numel(), cpu_data); + weight1->d_tensor().set_shape(anakin_shape); + weight1->d_tensor().copy_from(weight1->h_tensor()); + engine_->AddOpAttr(op_name, "weight_1", *weight1); +} + +} // namespace anakin +} // namespace inference +} // namespace paddle diff --git a/paddle/fluid/inference/anakin/convert/fc.h b/paddle/fluid/inference/anakin/convert/fc.h new file mode 100644 index 0000000000000000000000000000000000000000..b670486f12b36043a01ceb002da8756901ed01ce --- /dev/null +++ b/paddle/fluid/inference/anakin/convert/fc.h @@ -0,0 +1,38 @@ +// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include "paddle/fluid/inference/anakin/convert/op_converter.h" + +namespace paddle { +namespace inference { +namespace anakin { + +class FcOpConverter : public AnakinOpConverter { + public: + FcOpConverter() = default; + + virtual void operator()(const framework::proto::OpDesc &op, + const framework::Scope &scope, + bool test_mode) override; + virtual ~FcOpConverter() {} + + private: +}; + +static Registrar register_fc_op_converter("fc"); +} // namespace anakin +} // namespace inference +} // namespace paddle diff --git a/paddle/fluid/inference/anakin/convert/op_converter.h b/paddle/fluid/inference/anakin/convert/op_converter.h new file mode 100644 index 0000000000000000000000000000000000000000..b9a221079dcec78fc86ebed7dfac0c59ec0f8540 --- /dev/null +++ b/paddle/fluid/inference/anakin/convert/op_converter.h @@ -0,0 +1,112 @@ +// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include +#include +#include +#include +#include "framework/core/types.h" +#include "paddle/fluid/framework/block_desc.h" +#include "paddle/fluid/framework/op_registry.h" +#include "paddle/fluid/framework/scope.h" +#include "paddle/fluid/inference/anakin/convert/registrar.h" +#include "paddle/fluid/inference/anakin/engine.h" +#include "paddle/fluid/inference/utils/singleton.h" +#include "saber/saber_types.h" + +namespace paddle { +namespace inference { +namespace anakin { + +using AnakinNvEngine = + AnakinEngine<::anakin::saber::NV, ::anakin::Precision::FP32>; + +class AnakinOpConverter { + public: + AnakinOpConverter() = default; + + virtual void operator()(const framework::proto::OpDesc &op, + const framework::Scope &scope, bool test_mode) {} + void ConvertOp(const framework::proto::OpDesc &op, + const std::unordered_set ¶meters, + const framework::Scope &scope, AnakinNvEngine *engine, + bool test_mode = false) { + framework::OpDesc op_desc(op, nullptr); + std::string op_type = op_desc.Type(); + std::shared_ptr it{nullptr}; + + if (op_type == "mul") { + PADDLE_ENFORCE_EQ(op_desc.Input("Y").size(), 1UL); + std::string Y = op_desc.Input("Y")[0]; + std::cout << Y << parameters.count(Y) << std::endl; + if (parameters.count(Y)) { + it = OpRegister::instance()->Get("fc"); + } + } + + if (!it) { + it = OpRegister::instance()->Get(op_type); + } + PADDLE_ENFORCE_NOT_NULL(it, "no OpConverter for optype [%s]", op_type); + it->SetEngine(engine); + (*it)(op, scope, test_mode); + } + + void ConvertBlock(const framework::proto::BlockDesc &block, + const std::unordered_set ¶meters, + const framework::Scope &scope, AnakinNvEngine *engine) { + std::unique_lock lock(mutex_); + for (auto i = 0; i < block.ops_size(); i++) { + auto &op = block.ops(i); + ConvertOp(op, parameters, scope, engine); + } + } + void SetEngine(AnakinNvEngine *engine) { engine_ = engine; } + virtual ~AnakinOpConverter() {} + + protected: + bool test_mode_; + AnakinNvEngine *engine_{nullptr}; + + private: + std::unordered_map converters_; + framework::Scope *scope_{nullptr}; + std::mutex mutex_; +}; + +} // namespace anakin +} // namespace inference +} // namespace paddle + +#define REGISTER_ANAKIN_OP_CONVERTER(op_type__, Converter__) \ + struct anakin_##op_type__##_converter \ + : public ::paddle::framework::Registrar { \ + anakin_##op_type__##_converter() { \ + ::paddle::inference:: \ + Registry::Register< \ + ::paddle::inference::anakin::Converter__>(#op_type__); \ + } \ + }; \ + anakin_##op_type__##_converter anakin_##op_type__##_converter__; \ + int TouchConverterRegister_anakin_##op_type__() { \ + anakin_##op_type__##_converter__.Touch(); \ + return 0; \ + } + +#define USE_ANAKIN_CONVERTER(op_type__) \ + extern int TouchConverterRegister_anakin_##op_type__(); \ + static int use_op_converter_anakin_##op_type__ __attribute__((unused)) = \ + TouchConverterRegister_anakin_##op_type__(); diff --git a/paddle/fluid/inference/anakin/convert/registrar.cc b/paddle/fluid/inference/anakin/convert/registrar.cc new file mode 100644 index 0000000000000000000000000000000000000000..701ebdb2d43cf524330f946ac56d32dfa884f42a --- /dev/null +++ b/paddle/fluid/inference/anakin/convert/registrar.cc @@ -0,0 +1,34 @@ +// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "paddle/fluid/inference/anakin/convert/registrar.h" + +namespace paddle { +namespace inference { +namespace anakin { + +std::shared_ptr OpRegister::Get(const std::string &name) { + auto it = registry_.find(name); + if (it == registry_.end()) return nullptr; + return it->second(); +} + +OpRegister *OpRegister::instance() { + static OpRegister factory; + return &factory; +} + +} // namespace anakin +} // namespace inference +} // namespace paddle diff --git a/paddle/fluid/inference/anakin/convert/registrar.h b/paddle/fluid/inference/anakin/convert/registrar.h new file mode 100644 index 0000000000000000000000000000000000000000..afce66ca084143ae203af9a60089aa2f5d18a725 --- /dev/null +++ b/paddle/fluid/inference/anakin/convert/registrar.h @@ -0,0 +1,58 @@ +// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include +#include +#include +#include +#include + +namespace paddle { +namespace inference { +namespace anakin { + +class AnakinOpConverter; + +class OpRegister { + public: + OpRegister() = default; + std::shared_ptr Get(const std::string &name); + static OpRegister *instance(); + void OpRegisterFn(const std::string &name, + std::function()> fn) { + registry_[name] = fn; + } + + private: + using RegisterFnType = std::function()>; + std::map()>> + registry_; +}; + +template +class Registrar { + public: + Registrar(const std::string &name, Args... args) { + std::shared_ptr converter = + std::make_shared(std::move(args)...); + OpRegister::instance()->OpRegisterFn(name, + [converter]() { return converter; }); + } +}; + +} // namespace anakin +} // namespace inference +} // namespace paddle diff --git a/paddle/fluid/inference/anakin/convert/test_fc_op.cc b/paddle/fluid/inference/anakin/convert/test_fc_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..7b8ceefe28873f0ffb9cedbb04b832ba029b7de4 --- /dev/null +++ b/paddle/fluid/inference/anakin/convert/test_fc_op.cc @@ -0,0 +1,54 @@ +/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include +#include "paddle/fluid/inference/anakin/convert/fc.h" +#include "paddle/fluid/inference/anakin/convert/op_converter.h" +#include "paddle/fluid/inference/anakin/convert/ut_helper.h" + +namespace paddle { +namespace inference { +namespace anakin { + +TEST(fc_op, test) { + auto fc_converter = OpRegister::instance()->Get("fc"); + ASSERT_TRUE(fc_converter != nullptr); + // Registrar register_fc("fc"); + // auto fc = std::make_shared(); + + std::unordered_set parameters({"mul_y"}); + framework::Scope scope; + AnakinConvertValidation validator(parameters, scope); + validator.DeclInputVar("mul_x", {1, 1, 1, 1}); + validator.DeclParamVar("mul_y", {1, 2}); + validator.DeclOutputVar("mul_out", {1, 1, 1, 2}); + + // Prepare Op description + framework::OpDesc desc; + desc.SetType("mul"); + desc.SetInput("X", {"mul_x"}); + desc.SetInput("Y", {"mul_y"}); + desc.SetOutput("Out", {"mul_out"}); + int num_flatten_dims = 3; + desc.SetAttr("x_num_col_dims", num_flatten_dims); + validator.SetOp(*desc.Proto()); + + validator.Execute(10); +} + +} // namespace anakin +} // namespace inference +} // namespace paddle + +USE_OP(mul); diff --git a/paddle/fluid/inference/anakin/convert/ut_helper.h b/paddle/fluid/inference/anakin/convert/ut_helper.h new file mode 100644 index 0000000000000000000000000000000000000000..38d8e596a738ac98c9f9870473f72dcc72b0e7aa --- /dev/null +++ b/paddle/fluid/inference/anakin/convert/ut_helper.h @@ -0,0 +1,202 @@ +/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#pragma once + +#include +#include +#include +#include +#include +#include + +#include "paddle/fluid/framework/lod_tensor.h" +#include "paddle/fluid/framework/op_registry.h" +#include "paddle/fluid/framework/tensor_util.h" +#include "paddle/fluid/inference/anakin/engine.h" +#include "paddle/fluid/inference/analysis/helper.h" +#include "paddle/fluid/inference/utils/singleton.h" +#include "paddle/fluid/platform/enforce.h" + +using anakin::graph::GraphGlobalMem; +using anakin::AK_FLOAT; +using anakin::Precision; +using anakin::saber::NV; +using anakin::saber::X86; +using anakin::saber::Shape; +using anakin::PBlock; +using anakin::PTuple; + +namespace paddle { +namespace inference { +namespace anakin { + +/* + * Get a random float value between [low, high] + */ +float random(float low, float high) { + static std::random_device rd; + static std::mt19937 mt(rd()); + std::uniform_real_distribution dist(low, high); + return dist(mt); +} + +void RandomizeTensor(framework::LoDTensor* tensor, const platform::Place& place, + const platform::DeviceContext& ctx) { + auto dims = tensor->dims(); + size_t num_elements = analysis::AccuDims(dims, dims.size()); + PADDLE_ENFORCE_GT(num_elements, 0); + + platform::CPUPlace cpu_place; + framework::LoDTensor temp_tensor; + temp_tensor.Resize(dims); + auto* temp_data = temp_tensor.mutable_data(cpu_place); + + for (size_t i = 0; i < num_elements; i++) { + *(temp_data + i) = random(0., 1.); + } + + TensorCopySync(temp_tensor, place, tensor); +} + +/* + * Help to validate the correctness between Fluid Op and the corresponding + * anakin + * layer. + */ +class AnakinConvertValidation { + using AnakinNvEngineT = AnakinEngine; + + public: + AnakinConvertValidation() = delete; + + AnakinConvertValidation(const std::unordered_set& parameters, + const framework::Scope& scope) + : parameters_(parameters), scope_(scope), place_(0) { + PADDLE_ENFORCE_EQ(cudaStreamCreate(&stream_), 0); + engine_.reset(new AnakinEngine(true)); + } + + // Declare a Variable as input with random initialization. + void DeclInputVar(const std::string& name, + const std::vector tensor_dims) { + DeclVar(name, tensor_dims); + // should decalre anakin input here. + } + + void DeclParamVar(const std::string& name, const std::vector dim_vec) { + DeclVar(name, dim_vec); + } + + void DeclOutputVar(const std::string& name, const std::vector dim_vec) { + DeclVar(name, dim_vec); + // should declare anakin output here. + } + + void DeclVar(const std::string& name, const std::vector dim_vec) { + platform::CUDADeviceContext ctx(place_); + auto* x = scope_.Var(name); + auto* x_tensor = x->GetMutable(); + x_tensor->Resize(framework::make_ddim(dim_vec)); + RandomizeTensor(x_tensor, place_, ctx); + } + + void SetOp(const framework::proto::OpDesc& desc) { + op_ = framework::OpRegistry::CreateOp(desc); + op_desc_.reset(new framework::OpDesc(desc, nullptr)); + // should init anakin engine here. + + Singleton::Global().ConvertOp( + desc, parameters_, scope_, engine_.get(), true /*test_mode*/); + engine_->Freeze(); + for (const auto& input : op_desc_->InputArgumentNames()) { + if (parameters_.count(input)) continue; + auto& t = inference::analysis::GetFromScope(scope_, + input); + auto t_shape = framework::vectorize2int(t.dims()); + engine_->SetInputShape(input, t_shape); + } + engine_->Optimize(); + engine_->InitGraph(); + } + + // We use the set 'neglected_output' here, because some Ops like batch norm, + // the outputs specified in the op des are only used during training, + // so we should neglect those output during inference. + void Execute(int batch_size, + std::unordered_set neglected_output = {}) { + // Execute Fluid Op + platform::CUDADeviceContext ctx(place_); + op_->Run(scope_, place_); + + // std::vector input_vector; + // std::vector output_vector; + std::map inputs; + for (const auto& input : op_desc_->InputArgumentNames()) { + if (parameters_.count(input)) continue; + auto* var = scope_.FindVar(input); + auto tensor = var->GetMutable(); + inputs.insert({input, tensor}); + } + + std::map outputs; + std::vector> fluid_outputs; + for (const auto& output : op_desc_->OutputArgumentNames()) { + if (neglected_output.count(output)) continue; + std::vector fluid_out; + auto* var = scope_.FindVar(output); + auto tensor = var->GetMutable(); + framework::TensorToVector(*tensor, ctx, &fluid_out); + fluid_outputs.push_back(fluid_out); + + // size_t fluid_out_size = fluid_out.size(); + /*for (size_t i = 0; i < fluid_out_size; i++) { + std::cout << fluid_out[i] << std::endl; + }*/ + outputs.insert({output, tensor}); + } + + engine_->Execute(inputs, outputs); + int i_output = 0; + for (const auto& output : op_desc_->OutputArgumentNames()) { + if (neglected_output.count(output)) continue; + std::vector anakin_out; + auto* var = scope_.FindVar(output); + auto tensor = var->GetMutable(); + framework::TensorToVector(*tensor, ctx, &anakin_out); + + size_t anakin_out_size = anakin_out.size(); + auto fluid_out = fluid_outputs[i_output++]; + for (size_t i = 0; i < anakin_out_size; i++) { + LOG(INFO) << "Output[" << i << "]: anakin[" << anakin_out[i] << "], " + << "fluid[" << fluid_out[i] << "]"; + } + } + } + + framework::Scope& scope() { return scope_; } + + private: + std::unique_ptr engine_{nullptr}; + cudaStream_t stream_; + std::unique_ptr op_; + std::unique_ptr op_desc_; + const std::unordered_set& parameters_; + framework::Scope& scope_; + platform::CUDAPlace place_; +}; + +} // namespace anakin +} // namespace inference +} // namespace paddle diff --git a/paddle/fluid/inference/anakin/engine.cc b/paddle/fluid/inference/anakin/engine.cc new file mode 100644 index 0000000000000000000000000000000000000000..6549991474f4834f0c3ef74c60d294cca6bebc91 --- /dev/null +++ b/paddle/fluid/inference/anakin/engine.cc @@ -0,0 +1,112 @@ +// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "paddle/fluid/inference/anakin/engine.h" +#include +#include +#include +#include +#include "paddle/fluid/framework/ddim.h" + +using anakin::Precision; +using anakin::OpRunType; +using paddle::framework::LoDTensor; +template +using AnakinNetT = anakin::Net; + +template +using AnakinGraphT = anakin::graph::Graph; + +namespace paddle { +namespace inference { +namespace anakin { + +template +AnakinEngine::AnakinEngine(bool need_summary) + : graph_(new AnakinGraphT()), + net_(new AnakinNetT(need_summary)) {} + +template +AnakinEngine::~AnakinEngine() {} + +template +void AnakinEngine::SetInputShape( + const std::string &name, std::vector shape) { + graph_->AddOpAttr<::anakin::PTuple>(name, "input_shape", + std::move(shape)); +} + +template +void AnakinEngine::InitGraph() { + net_->init(*graph_); +} + +template +void AnakinEngine::AddOp( + const std::string &name, const std::string &type, + const std::vector &inputs, + const std::vector &outputs) { + PADDLE_ENFORCE(graph_->AddOp(name, type, inputs, outputs), "Add operation."); +} + +template +void AnakinEngine::Execute( + const std::map &inputs, + const std::map &outputs) { + for (const auto &input : inputs) { + auto *tensor = input.second; + auto *data = tensor->data(); + auto shape = framework::vectorize2int(tensor->dims()); + ::anakin::saber::Shape anakin_shape(shape); + auto *anakin_input = net_->get_in(input.first); + ::anakin::saber::Tensor tmp_anakin_tensor(data, TargetT(), 0, + anakin_shape); + anakin_input->share_from(tmp_anakin_tensor); + } + + for (const auto &output : outputs) { + auto *tensor = output.second; + auto *data = tensor->data(); + auto shape = framework::vectorize2int(tensor->dims()); + ::anakin::saber::Shape anakin_shape(shape); + auto *anakin_output = net_->get_out(output.first); + ::anakin::saber::Tensor tmp_anakin_tensor(data, TargetT(), 0, + anakin_shape); + anakin_output->share_from(tmp_anakin_tensor); + } + net_->prediction(); +} + +template +void AnakinEngine::Freeze() { + PADDLE_ENFORCE(graph_->Freeze(), "Freeze anakin subgraph."); +} + +template +void AnakinEngine::Optimize() { + PADDLE_ENFORCE(graph_->Optimize(), "Graph optimization."); +} + +template +std::unique_ptr> +AnakinEngine::Clone() { + auto *engine = new AnakinEngine(); + engine->net_ = std::move(net_->Clone()); + return std::unique_ptr(engine); +} + +template class AnakinEngine<::anakin::saber::NV, ::anakin::Precision::FP32>; +} // namespace anakin +} // namespace inference +} // namespace paddle diff --git a/paddle/fluid/inference/anakin/engine.h b/paddle/fluid/inference/anakin/engine.h new file mode 100644 index 0000000000000000000000000000000000000000..d8f32f57be5aabb91ba720c6457a03f15083db43 --- /dev/null +++ b/paddle/fluid/inference/anakin/engine.h @@ -0,0 +1,80 @@ +// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include +#include +#include +#include +#include +#include "paddle/fluid/framework/lod_tensor.h" +#include "paddle/fluid/inference/engine.h" +#include "paddle/fluid/inference/utils/singleton.h" + +#include "framework/core/net/net.h" +#include "framework/core/types.h" +#include "framework/graph/graph.h" +#include "saber/saber_types.h" + +namespace anakin { + +template +class Net; + +namespace graph { +template +class Graph; +} // namespace graph +} // namespace anakin + +namespace paddle { +namespace inference { +namespace anakin { + +template +class AnakinEngine { + public: + explicit AnakinEngine(bool need_summary = false); + ~AnakinEngine(); + void InitGraph(); + void SetInputShape(const std::string &name, std::vector shape); + void AddOp(const std::string &name, const std::string &type, + const std::vector &inputs, + const std::vector &outputs); + + template + void AddOpAttr(const std::string &op_name, const std::string &attr_name, + const T &attr_value) { + PADDLE_ENFORCE(graph_->AddOpAttr(op_name, attr_name, attr_value), + "Add operation's attribution."); + } + + std::unique_ptr Clone(); + void Freeze(); + void Optimize(); + void Execute(const std::map &inputs, + const std::map &outputs); + + private: + using NetT = ::anakin::Net; + using GraphT = ::anakin::graph::Graph; + std::unique_ptr graph_; + std::unique_ptr net_; +}; + +} // namespace anakin +} // namespace inference +} // namespace paddle diff --git a/paddle/fluid/inference/anakin/test_anakin_engine.cc b/paddle/fluid/inference/anakin/test_anakin_engine.cc new file mode 100644 index 0000000000000000000000000000000000000000..571294d3e22fb9489686bfcb2f3a64198099f970 --- /dev/null +++ b/paddle/fluid/inference/anakin/test_anakin_engine.cc @@ -0,0 +1,96 @@ +/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include +#include + +#include + +#include "framework/core/net/net.h" +#include "framework/graph/graph.h" +#include "framework/graph/graph_global_mem.h" +#include "paddle/fluid/inference/anakin/engine.h" + +using anakin::graph::GraphGlobalMem; +using anakin::AK_FLOAT; +using anakin::Precision; +using anakin::saber::NV; +using anakin::saber::X86; +using anakin::saber::Shape; +using anakin::PBlock; +using anakin::PTuple; +namespace paddle { +namespace inference { +namespace anakin { + +class TestAnakinEngine : public ::testing::Test { + protected: + void SetUp() override; + void TearDown() override {} + + protected: + using AnakinNvEngineT = AnakinEngine; + std::unique_ptr engine_{nullptr}; +}; + +void TestAnakinEngine::SetUp() { + engine_.reset(new AnakinEngine(true)); +} + +TEST_F(TestAnakinEngine, Execute) { + engine_->AddOp("op1", "Dense", {"x"}, {"y"}); + engine_->AddOpAttr("op1", "out_dim", 2); + engine_->AddOpAttr("op1", "bias_term", false); + engine_->AddOpAttr("op1", "axis", 1); + std::vector shape = {1, 1, 1, 2}; + Shape tmp_shape(shape); + // PBlock weight1(tmp_shape); + auto *weight1 = + GraphGlobalMem::Global().template new_block(tmp_shape); + // auto *weight1 = new PBlock(tmp_shape, AK_FLOAT); + + float *cpu_data = static_cast(weight1->h_tensor().mutable_data()); + cpu_data[0] = 2.; + weight1->d_tensor().set_shape(tmp_shape); + weight1->d_tensor().copy_from(weight1->h_tensor()); + engine_->AddOpAttr("op1", "weight_1", *weight1); + + engine_->Freeze(); + // PTuple input_shape = {1}; + // engine_->AddOpAttr("x", "input_shape", input_shape); + engine_->SetInputShape("x", {1, 1, 1, 1}); + engine_->Optimize(); + engine_->InitGraph(); + framework::LoDTensor x; + framework::LoDTensor y; + x.Resize({1, 1, 1, 1}); + y.Resize({1, 1, 1, 2}); + auto *x_data = x.mutable_data(platform::CUDAPlace()); + float x_data_cpu[] = {1.}; + cudaMemcpy(x_data, x_data_cpu, sizeof(float), cudaMemcpyHostToDevice); + + std::map inputs = {{"x", &x}}; + auto *y_data = y.mutable_data(platform::CUDAPlace()); + std::map outputs = {{"y", &y}}; + + engine_->Execute(inputs, outputs); + auto *y_data_gpu = y_data; + float y_data_cpu[2]; + cudaMemcpy(y_data_cpu, y_data_gpu, sizeof(float) * 2, cudaMemcpyDeviceToHost); + LOG(INFO) << "output value: " << y_data_cpu[0] << ", " << y_data_cpu[1]; +} + +} // namespace anakin +} // namespace inference +} // namespace paddle diff --git a/paddle/fluid/inference/api/paddle_pass_builder.cc b/paddle/fluid/inference/api/paddle_pass_builder.cc index f9c13c2fa84b3b5d629297d3f44a6f5889a734f4..92c24647e87a096e7cfbbf69876b678fe48842a4 100644 --- a/paddle/fluid/inference/api/paddle_pass_builder.cc +++ b/paddle/fluid/inference/api/paddle_pass_builder.cc @@ -13,7 +13,9 @@ // limitations under the License. #include "paddle/fluid/inference/api/paddle_pass_builder.h" - +#ifdef PADDLE_WITH_CUDA +#include +#endif #include namespace paddle { diff --git a/paddle/fluid/operators/activation_cudnn.cu.cc b/paddle/fluid/operators/activation_cudnn.cu.cc new file mode 100644 index 0000000000000000000000000000000000000000..494c02374a9faa22486644c9b9c7d586c86d41b0 --- /dev/null +++ b/paddle/fluid/operators/activation_cudnn.cu.cc @@ -0,0 +1,40 @@ +// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "paddle/fluid/framework/op_registry.h" +#include "paddle/fluid/operators/activation_op.h" +#include "paddle/fluid/platform/cudnn_desc.h" + +namespace paddle { +namespace operators { +using framework::Tensor; +using platform::ActivationDescriptor; +using platform::TensorDescriptor; + +template +class CudnnActivationKernel + : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& context) const override { + framework::Tensor *X, *Out; + ExtractActivationTensor(context, X, Out); + ActivationDescriptor act_desc; + TensorDescriptor x_desc, out_desc; + x_desc.set(detail::Ref(X)); + out_desc.set(detail::Ref(Out)); + } +}; + +} // namespace operators +} // namespace paddle diff --git a/paddle/fluid/operators/activation_cudnn_op.cu.cc b/paddle/fluid/operators/activation_cudnn_op.cu.cc new file mode 100644 index 0000000000000000000000000000000000000000..a382414d5c473a9c36f92a9af56837da819e96a4 --- /dev/null +++ b/paddle/fluid/operators/activation_cudnn_op.cu.cc @@ -0,0 +1,175 @@ +// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "paddle/fluid/framework/op_registry.h" +#include "paddle/fluid/operators/activation_op.h" +#include "paddle/fluid/platform/cudnn_desc.h" + +namespace paddle { +namespace operators { +using framework::Tensor; +using platform::ActivationDescriptor; +using platform::TensorDescriptor; +using platform::CUDADeviceContext; + +template +struct CudnnActivationFunctor { + using ELEMENT_TYPE = T; + CudnnActivationFunctor(const CUDADeviceContext& ctx, const T& c, + const cudnnActivationMode_t& m) + : ctx_(ctx), coef_(c), mode_(m) {} + void operator()(const Tensor& x, Tensor* out) { + ActivationDescriptor act_desc; + act_desc.set(mode_, coef_); + TensorDescriptor x_desc, out_desc; + x_desc.set(x); + out_desc.set(detail::Ref(out)); + PADDLE_ENFORCE(platform::dynload::cudnnActivationForward( + ctx_.cudnn_handle(), act_desc.desc(), + platform::CudnnDataType::kOne(), x_desc.desc(), x.data(), + platform::CudnnDataType::kZero(), out_desc.desc(), + out->mutable_data(ctx_.GetPlace()))); + } + const CUDADeviceContext& ctx_; + const T coef_; + const cudnnActivationMode_t mode_; +}; + +template +struct CudnnActivationGradFunctor { + using ELEMENT_TYPE = T; + CudnnActivationGradFunctor(const CUDADeviceContext& ctx, const T& c, + const cudnnActivationMode_t& m) + : ctx_(ctx), coef_(c), mode_(m) {} + void operator()(const Tensor& x, const Tensor& out, const Tensor dout, + Tensor* dx) { + ActivationDescriptor act_desc; + act_desc.set(mode_, coef_); + TensorDescriptor x_desc, out_desc, dout_desc, dx_desc; + x_desc.set(x); + out_desc.set(out); + dout_desc.set(dout); + dx_desc.set(detail::Ref(dx)); + PADDLE_ENFORCE(platform::dynload::cudnnActivationBackward( + ctx_.cudnn_handle(), act_desc.desc(), + platform::CudnnDataType::kOne(), out_desc.desc(), out.data(), + dout_desc.desc(), dout.data(), x_desc.desc(), x.data(), + platform::CudnnDataType::kZero(), dx_desc.desc(), + dx->mutable_data(ctx_.GetPlace()))); + } + const CUDADeviceContext& ctx_; + const T coef_; + const cudnnActivationMode_t mode_; +}; + +template +struct CudnnReluFunctor : public CudnnActivationFunctor { + explicit CudnnReluFunctor(const CUDADeviceContext& ctx) + : CudnnActivationFunctor(ctx, 0.0, CUDNN_ACTIVATION_RELU) {} +}; +template +struct CudnnReluGradFunctor : public CudnnActivationGradFunctor { + explicit CudnnReluGradFunctor(const CUDADeviceContext& ctx) + : CudnnActivationGradFunctor(ctx, 0.0, CUDNN_ACTIVATION_RELU) {} +}; + +template +struct CudnnRelu6Functor : public CudnnActivationFunctor { + explicit CudnnRelu6Functor(const CUDADeviceContext& ctx) + : CudnnActivationFunctor(ctx, 6.0, CUDNN_ACTIVATION_CLIPPED_RELU) {} +}; +template +struct CudnnRelu6GradFunctor : public CudnnActivationGradFunctor { + explicit CudnnRelu6GradFunctor(const CUDADeviceContext& ctx) + : CudnnActivationGradFunctor(ctx, 6.0, CUDNN_ACTIVATION_CLIPPED_RELU) { + } +}; + +template +struct CudnnSigmoidFunctor : public CudnnActivationFunctor { + explicit CudnnSigmoidFunctor(const CUDADeviceContext& ctx) + : CudnnActivationFunctor(ctx, 0.0, CUDNN_ACTIVATION_SIGMOID) {} +}; +template +struct CudnnSigmoidGradFunctor : public CudnnActivationGradFunctor { + explicit CudnnSigmoidGradFunctor(const CUDADeviceContext& ctx) + : CudnnActivationGradFunctor(ctx, 0.0, CUDNN_ACTIVATION_SIGMOID) {} +}; + +template +struct CudnnTanhFunctor : public CudnnActivationFunctor { + explicit CudnnTanhFunctor(const CUDADeviceContext& ctx) + : CudnnActivationFunctor(ctx, 0.0, CUDNN_ACTIVATION_TANH) {} +}; +template +struct CudnnTanhGradFunctor : public CudnnActivationGradFunctor { + explicit CudnnTanhGradFunctor(const CUDADeviceContext& ctx) + : CudnnActivationGradFunctor(ctx, 0.0, CUDNN_ACTIVATION_TANH) {} +}; + +template +class CudnnActivationKernel + : public framework::OpKernel { + public: + using T = typename Functor::ELEMENT_TYPE; + void Compute(const framework::ExecutionContext& context) const override { + const framework::Tensor* X = nullptr; + framework::Tensor* Out = nullptr; + ExtractActivationTensor(context, &X, &Out); + Out->mutable_data(context.GetPlace()); + auto& dev_ctx = context.template device_context(); + Functor functor(dev_ctx); + functor(detail::Ref(X), Out); + } +}; + +template +class CudnnActivationGradKernel + : public framework::OpKernel { + public: + using T = typename Functor::ELEMENT_TYPE; + void Compute(const framework::ExecutionContext& context) const override { + const framework::Tensor *X, *Out, *dOut; + X = Out = dOut = nullptr; + framework::Tensor* dX = nullptr; + ExtractActivationGradTensor(context, &X, &Out, &dOut, &dX); + dX->mutable_data(context.GetPlace()); + auto& dev_ctx = context.template device_context(); + Functor functor(dev_ctx); + functor(detail::Ref(X), detail::Ref(Out), detail::Ref(dOut), dX); + } +}; + +} // namespace operators +} // namespace paddle + +namespace plat = paddle::platform; +namespace ops = paddle::operators; + +#define FOR_EACH_CUDNN_OP_FUNCTOR(__macro) \ + __macro(relu, CudnnReluFunctor, CudnnReluGradFunctor); \ + __macro(relu6, CudnnRelu6Functor, CudnnRelu6GradFunctor); \ + __macro(sigmoid, CudnnTanhFunctor, CudnnTanhGradFunctor); \ + __macro(tanh, CudnnTanhFunctor, CudnnTanhGradFunctor) + +#define REGISTER_ACTIVATION_CUDNN_KERNEL(act_type, functor, grad_functor) \ + REGISTER_OP_KERNEL(act_type, CUDNN, plat::CUDAPlace, \ + ops::CudnnActivationKernel>, \ + ops::CudnnActivationKernel>); \ + REGISTER_OP_KERNEL( \ + act_type##_grad, CUDNN, plat::CUDAPlace, \ + ops::CudnnActivationGradKernel>, \ + ops::CudnnActivationGradKernel>); + +FOR_EACH_CUDNN_OP_FUNCTOR(REGISTER_ACTIVATION_CUDNN_KERNEL); diff --git a/paddle/fluid/operators/activation_op.cc b/paddle/fluid/operators/activation_op.cc index 65efe2966ce12e86ba7f4944eb57ae72cdf9796f..2feb8e4c4787440fd086c597fa2a7f97204e34ac 100644 --- a/paddle/fluid/operators/activation_op.cc +++ b/paddle/fluid/operators/activation_op.cc @@ -16,29 +16,36 @@ limitations under the License. */ #include #include "paddle/fluid/operators/mkldnn/mkldnn_activation_op.h" #include "paddle/fluid/platform/port.h" +#ifdef PADDLE_WITH_CUDA +#include "paddle/fluid/platform/cudnn_helper.h" +#endif namespace paddle { namespace operators { using paddle::framework::Tensor; -#define REGISTER_ACTIVATION_OP_MAKER(OP_NAME, OP_COMMENT) \ - class OP_NAME##OpMaker \ - : public ::paddle::framework::OpProtoAndCheckerMaker { \ - public: \ - void Make() override { \ - AddInput("X", "Input of " #OP_NAME " operator"); \ - AddOutput("Out", "Output of " #OP_NAME " operator"); \ - AddAttr("use_mkldnn", \ - "(bool, default false) Only used in mkldnn kernel") \ - .SetDefault(false); \ - AddAttr( \ - "is_test", \ - "(bool, default false) Set to true for inference only, false " \ - "for training. Some layers may run faster when this is true.") \ - .SetDefault(false); \ - AddComment(OP_COMMENT); \ - } \ +#define REGISTER_ACTIVATION_OP_MAKER(OP_NAME, OP_COMMENT) \ + class OP_NAME##OpMaker \ + : public ::paddle::framework::OpProtoAndCheckerMaker { \ + public: \ + void Make() override { \ + AddInput("X", "Input of " #OP_NAME " operator"); \ + AddOutput("Out", "Output of " #OP_NAME " operator"); \ + AddAttr("use_mkldnn", \ + "(bool, default false) Only used in mkldnn kernel") \ + .SetDefault(false); \ + AddAttr("use_cudnn", \ + "(bool, default false) Only used in cudnn kernel, need " \ + "install cudnn") \ + .SetDefault(false); \ + AddAttr( \ + "is_test", \ + "(bool, default false) Set to true for inference only, false " \ + "for training. Some layers may run faster when this is true.") \ + .SetDefault(false); \ + AddComment(OP_COMMENT); \ + } \ } #define REGISTER_ACTIVATION_OP_GRAD_MAKER(OP_NAME, KERNEL_TYPE) \ @@ -67,6 +74,12 @@ framework::OpKernelType GetKernelType(const framework::ExecutionContext& ctx, const std::string& name) { framework::LibraryType library{framework::LibraryType::kPlain}; framework::DataLayout layout = framework::DataLayout::kAnyLayout; +#ifdef PADDLE_WITH_CUDA + auto it1 = oper.Attrs().find("use_cudnn"); + if (it1 != oper.Attrs().end() && platform::CanCUDNNBeUsed(ctx)) { + library = framework::LibraryType::kCUDNN; + } +#endif #ifdef PADDLE_WITH_MKLDNN auto it = oper.Attrs().find("use_mkldnn"); if (library == framework::LibraryType::kPlain && it != oper.Attrs().end() && diff --git a/paddle/fluid/operators/activation_op.h b/paddle/fluid/operators/activation_op.h index c7df3ea58a91579e35ff0d486516271a6daf054f..1f5ae7fb5cd2e1c14190602d2c35e6c3755cfd70 100644 --- a/paddle/fluid/operators/activation_op.h +++ b/paddle/fluid/operators/activation_op.h @@ -11,6 +11,7 @@ limitations under the License. */ #pragma once #include +#include #include #include #include @@ -24,6 +25,7 @@ limitations under the License. */ #include "paddle/fluid/framework/eigen.h" #include "paddle/fluid/framework/op_registry.h" #include "paddle/fluid/operators/detail/safe_ref.h" +#include "paddle/fluid/operators/math/blas.h" #include "paddle/fluid/platform/float16.h" #ifdef PADDLE_WITH_MKLDNN @@ -41,53 +43,115 @@ static std::unordered_set InplaceOpSet = { "floor", "reciprocal", "relu6", "soft_relu", "hard_sigmoid", }; +static bool IsInplace(const std::string& op) { + bool inplace = InplaceOpSet.count(op); + // for op_grad + const int kGradSuffixLen = 4; + if (op.size() > kGradSuffixLen && + op.compare(op.size() - kGradSuffixLen - 1, kGradSuffixLen, "grad")) { + inplace = + InplaceOpSet.count(op.substr(0, op.size() - (kGradSuffixLen + 1))); + } + return inplace; +} + /* The following operator can be used to process SelectedRows, because the * output of those operator for zero is zero too. */ static std::unordered_set CanBeUsedBySelectedRows = { "abs", "abs_grad", "square", "square_grad", "sqrt", "sqrt_grad"}; -static bool IsInplace(std::string op) { return InplaceOpSet.count(op); } - -template -class ActivationKernel - : public framework::OpKernel { - public: - using T = typename Functor::ELEMENT_TYPE; - - void Compute(const framework::ExecutionContext& context) const override { +inline void ExtractActivationTensor(const framework::ExecutionContext& context, + const framework::Tensor** X, + framework::Tensor** Out) { + auto x_var = context.InputVar("X"); + auto out_var = context.OutputVar("Out"); + PADDLE_ENFORCE(x_var != nullptr, + "Cannot get input Variable X, variable name = %s", + context.op().Input("X")); + PADDLE_ENFORCE(out_var != nullptr, + "Cannot get output Variable Out, variable name = %s", + context.op().Output("Out")); + if (CanBeUsedBySelectedRows.count(context.op().Type())) { + *X = paddle::framework::GetLoDTensorOrSelectedRowsValueFromVar(*x_var); + *Out = paddle::framework::GetMutableLoDTensorOrSelectedRowsValueFromVar( + out_var); + } else { + *X = context.Input("X"); + *Out = context.Output("Out"); + } + + PADDLE_ENFORCE(*Out != nullptr, + "Cannot get output tensor Out, variable name = %s", + context.op().Output("Out")); +} + +inline void ExtractActivationGradTensor( + const framework::ExecutionContext& context, const framework::Tensor** X, + const framework::Tensor** Out, const framework::Tensor** dOut, + framework::Tensor** dX) { + auto out_var = context.InputVar("Out"); + auto out_grad_var = context.InputVar(framework::GradVarName("Out")); + auto x_grad_var = context.OutputVar(framework::GradVarName("X")); + PADDLE_ENFORCE(out_var != nullptr, + "Cannot get input Variable Out, variable name = %s", + context.op().Input("Out")); + PADDLE_ENFORCE(out_grad_var != nullptr, + "Cannot get input Variable %s, variable name = %s", + framework::GradVarName("Out"), + context.op().Input(framework::GradVarName("Out"))); + PADDLE_ENFORCE(x_grad_var != nullptr, + "Cannot get output Variable %s, variable name = %s", + framework::GradVarName("X"), + context.op().Output(framework::GradVarName("X"))); + + if (CanBeUsedBySelectedRows.count(context.op().Type())) { + *Out = paddle::framework::GetLoDTensorOrSelectedRowsValueFromVar(*out_var); + *dOut = paddle::framework::GetLoDTensorOrSelectedRowsValueFromVar( + *out_grad_var); + *dX = paddle::framework::GetMutableLoDTensorOrSelectedRowsValueFromVar( + x_grad_var); + } else { + *Out = context.Input("Out"); + *dOut = context.Input(framework::GradVarName("Out")); + *dX = context.Output(framework::GradVarName("X")); + } + PADDLE_ENFORCE(*dX != nullptr, + "Cannot get output tensor %s, variable name = %s", + framework::GradVarName("X"), + context.op().Output(framework::GradVarName("X"))); + + bool inplace = IsInplace(context.op().Type()); + if (!inplace) { auto x_var = context.InputVar("X"); - auto out_var = context.OutputVar("Out"); PADDLE_ENFORCE(x_var != nullptr, - "Cannot get input Variable X, variable name = %s", + "Cannot get input tensor X, variable name = %s", context.op().Input("X")); - PADDLE_ENFORCE(out_var != nullptr, - "Cannot get output Variable Out, variable name = %s", - context.op().Output("Out")); - - framework::Tensor X, *Out; - if (CanBeUsedBySelectedRows.count(context.op().Type())) { - X = detail::Ref( - paddle::framework::GetLoDTensorOrSelectedRowsValueFromVar(*x_var), - "Cannot get input Tensor X, variable name = %s", - context.op().Input("X")); - Out = paddle::framework::GetMutableLoDTensorOrSelectedRowsValueFromVar( - out_var); + *X = paddle::framework::GetLoDTensorOrSelectedRowsValueFromVar(*x_var); } else { - X = detail::Ref(context.Input("X"), - "Cannot get input Tensor X, variable name = %s", - context.op().Input("X")); - Out = context.Output("Out"); + *X = context.Input("X"); } + } else { + VLOG(10) << " Inplace activation of Op : " << context.op().Type(); + *X = *dX; + } +} - PADDLE_ENFORCE(Out != nullptr, - "Cannot get output tensor Out, variable name = %s", - context.op().Output("Out")); +template +class ActivationKernel + : public framework::OpKernel { + public: + using T = typename Functor::ELEMENT_TYPE; + void Compute(const framework::ExecutionContext& context) const override { + const framework::Tensor* X = nullptr; + framework::Tensor* Out = nullptr; + ExtractActivationTensor(context, &X, &Out); Out->mutable_data(context.GetPlace()); - auto x = framework::EigenVector::Flatten(X); - auto out = framework::EigenVector::Flatten(*Out); + + auto x = framework::EigenVector::Flatten(detail::Ref(X)); + auto out = framework::EigenVector::Flatten(detail::Ref(Out)); auto* place = context.template device_context().eigen_device(); Functor functor; @@ -106,55 +170,15 @@ class ActivationGradKernel public: using T = typename Functor::ELEMENT_TYPE; void Compute(const framework::ExecutionContext& context) const override { - auto out_var = context.InputVar("Out"); - auto out_grad_var = context.InputVar(framework::GradVarName("Out")); - auto x_grad_var = context.OutputVar(framework::GradVarName("X")); - PADDLE_ENFORCE(out_var != nullptr, - "Cannot get input Variable Out, variable name = %s", - context.op().Input("Out")); - PADDLE_ENFORCE(out_grad_var != nullptr, - "Cannot get input Variable %s, variable name = %s", - framework::GradVarName("Out"), - context.op().Input(framework::GradVarName("Out"))); - PADDLE_ENFORCE(x_grad_var != nullptr, - "Cannot get output Variable %s, variable name = %s", - framework::GradVarName("X"), - context.op().Output(framework::GradVarName("X"))); - - framework::Tensor Out, dOut, *dX; - if (CanBeUsedBySelectedRows.count(context.op().Type())) { - Out = detail::Ref( - paddle::framework::GetLoDTensorOrSelectedRowsValueFromVar(*out_var), - "Cannot get input Tensor Out, variable name = %s", - context.op().Input("Out")); - dOut = - detail::Ref(paddle::framework::GetLoDTensorOrSelectedRowsValueFromVar( - *out_grad_var), - "Cannot get input Tensor %s, variable name = %s", - framework::GradVarName("Out"), - context.op().Input(framework::GradVarName("Out"))); - dX = paddle::framework::GetMutableLoDTensorOrSelectedRowsValueFromVar( - x_grad_var); - } else { - Out = detail::Ref(context.Input("Out"), - "Cannot get input Tensor Out, variable name = %s", - context.op().Input("Out")); - dOut = detail::Ref( - context.Input(framework::GradVarName("Out")), - "Cannot get input Tensor %s, variable name = %s", - framework::GradVarName("Out"), - context.op().Input(framework::GradVarName("Out"))); - dX = context.Output(framework::GradVarName("X")); - } - PADDLE_ENFORCE(dX != nullptr, - "Cannot get output tensor %s, variable name = %s", - framework::GradVarName("X"), - context.op().Output(framework::GradVarName("X"))); + const framework::Tensor *X, *Out, *dOut; + framework::Tensor* dX = nullptr; + X = Out = dOut = nullptr; + ExtractActivationGradTensor(context, &X, &Out, &dOut, &dX); dX->mutable_data(context.GetPlace()); - - auto dout = framework::EigenVector::Flatten(dOut); - auto out = framework::EigenVector::Flatten(Out); - auto dx = framework::EigenVector::Flatten(*dX); + auto dout = framework::EigenVector::Flatten(detail::Ref(dOut)); + auto out = framework::EigenVector::Flatten(detail::Ref(Out)); + auto dx = framework::EigenVector::Flatten(detail::Ref(dX)); + auto x = framework::EigenVector::Flatten(detail::Ref(X)); auto* place = context.template device_context().eigen_device(); Functor functor; @@ -162,27 +186,7 @@ class ActivationGradKernel for (auto& attr : attrs) { *attr.second = context.Attr(attr.first); } - bool inplace = functor.Inplace(); - if (!inplace) { - auto x_var = context.InputVar("X"); - PADDLE_ENFORCE(x_var != nullptr, - "Cannot get input tensor X, variable name = %s", - context.op().Input("X")); - framework::Tensor X; - if (CanBeUsedBySelectedRows.count(context.op().Type())) { - X = detail::Ref( - paddle::framework::GetLoDTensorOrSelectedRowsValueFromVar(*x_var)); - } else { - X = detail::Ref(context.Input("X")); - } - - auto x = framework::EigenVector::Flatten(X); - functor(*place, x, out, dout, dx); - } else { - VLOG(10) << " Inplace activation "; - auto x = framework::EigenVector::Flatten(*dX); - functor(*place, x, out, dout, dx); - } + functor(*place, x, out, dout, dx); } }; @@ -214,7 +218,6 @@ struct SigmoidFunctor : public BaseActivationFunctor { template struct SigmoidGradFunctor : public BaseActivationFunctor { - bool Inplace() const { return IsInplace("sigmoid"); } template void operator()(Device d, X x, Out out, dOut dout, dX dx) const { @@ -269,7 +272,6 @@ struct ExpFunctor : public BaseActivationFunctor { template struct ExpGradFunctor : public BaseActivationFunctor { - bool Inplace() const { return IsInplace("exp"); } template void operator()(Device d, X x, Out out, dOut dout, dX dx) const { @@ -288,7 +290,6 @@ struct ReluFunctor : public BaseActivationFunctor { template struct ReluGradFunctor : public BaseActivationFunctor { - bool Inplace() const { return IsInplace("relu"); } template void operator()(Device d, X x, Out out, dOut dout, dX dx) const { @@ -301,8 +302,28 @@ template struct GeluFunctor : public BaseActivationFunctor { template void operator()(Device d, X x, Out out) const { +// Because the execute or device context can not be deliver here, it keep the +// marco for NVCC. +#if defined(PADDLE_WITH_MKLML) && !defined(_WIN32) && !defined(__APPLE__) && \ + !defined(__OSX__) && !defined(PADDLE_WITH_CUDA) + auto x_data = x.data(); + auto out_data = out.data(); + int n = std::min(x.size(), out.size()); + + std::memset(out_data, 0, n * sizeof(T)); + math::CBlas::AXPY(n, static_cast(M_SQRT1_2), x_data, 1, out_data, 1); + math::CBlas::VMERF(n, out_data, out_data, VML_LA); + for (int i = 0; i < n; i++) { + out_data[i] += static_cast(1); + } + math::CBlas::VMUL(n, x_data, out_data, out_data); + for (int i = 0; i < n; i++) { + out_data[i] *= static_cast(0.5); + } +#else auto temp = (x * static_cast(M_SQRT1_2)).erf(); out.device(d) = x * static_cast(0.5) * (static_cast(1) + temp); +#endif } }; @@ -331,7 +352,6 @@ struct TanhFunctor : public BaseActivationFunctor { template struct TanhGradFunctor : public BaseActivationFunctor { - bool Inplace() const { return IsInplace("tanh"); } template void operator()(Device d, X x, Out out, dOut dout, dX dx) const { @@ -437,7 +457,6 @@ struct SqrtFunctor : public BaseActivationFunctor { template struct SqrtGradFunctor : public BaseActivationFunctor { - bool Inplace() const { return IsInplace("sqrt"); } template void operator()(Device d, X x, Out out, dOut dout, dX dx) const { @@ -456,7 +475,6 @@ struct CeilFunctor : public BaseActivationFunctor { template struct ZeroGradFunctor : public BaseActivationFunctor { - bool Inplace() const { return IsInplace("ceil"); } template void operator()(Device d, X x, Out out, dOut dout, dX dx) const { @@ -573,7 +591,6 @@ struct ReciprocalFunctor : public BaseActivationFunctor { template struct ReciprocalGradFunctor : public BaseActivationFunctor { - bool Inplace() const { return IsInplace("reciprocal"); } template void operator()(Device d, X x, Out out, dOut dout, dX dx) const { @@ -673,7 +690,6 @@ struct Relu6GradFunctor : public BaseActivationFunctor { typename BaseActivationFunctor::AttrPair GetAttrs() { return {{"threshold", &threshold}}; } - bool Inplace() const { return IsInplace("relu6"); } template void operator()(Device d, X x, Out out, dOut dout, dX dx) const { @@ -755,7 +771,6 @@ struct SoftReluGradFunctor : public BaseActivationFunctor { typename BaseActivationFunctor::AttrPair GetAttrs() { return {{"threshold", &threshold}}; } - bool Inplace() const { return IsInplace("soft_relu"); } template void operator()(Device d, X x, Out out, dOut dout, dX dx) const { @@ -936,7 +951,6 @@ struct HardSigmoidGradFunctor : public BaseActivationFunctor { typename BaseActivationFunctor::AttrPair GetAttrs() { return {{"slope", &slope}, {"offset", &offset}}; } - bool Inplace() { return IsInplace("hard_sigmoid"); } template void operator()(Device d, X x, Out out, dOut dout, dX dx) const { diff --git a/paddle/fluid/operators/alloc_continuous_space_op.cc b/paddle/fluid/operators/alloc_continuous_space_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..df0e9911cf7186e952cfd7fbf7f43889e9098c84 --- /dev/null +++ b/paddle/fluid/operators/alloc_continuous_space_op.cc @@ -0,0 +1,211 @@ +// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include +#include "paddle/fluid/framework/op_registry.h" +#include "paddle/fluid/framework/operator.h" +#include "paddle/fluid/framework/var_type.h" +#include "paddle/fluid/operators/math/math_function.h" + +namespace paddle { +namespace operators { + +static framework::proto::VarType::Type kDefaultDtype = + framework::proto::VarType::Type::VarType_Type_BOOL; + +template +class AllocContinuousSpaceKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext &context) const override { + auto &in_var_names = context.Inputs("Input"); + auto &out_var_names = context.Outputs("Output"); + auto &in_vars = context.MultiInputVar("Input"); + auto out_vars = context.MultiOutputVar("Output"); + + PADDLE_ENFORCE_GT(in_var_names.size(), static_cast(0)); + PADDLE_ENFORCE_EQ(in_var_names.size(), out_var_names.size()); + + for (size_t i = 0; i < in_var_names.size(); ++i) { + // Only support LoDTensor + PADDLE_ENFORCE_NOT_NULL(in_vars[i], "%s should not be nullptr,", + in_var_names[i]); + PADDLE_ENFORCE_NOT_NULL(out_vars[i], "%s should not be nullptr,", + out_var_names[i]); + PADDLE_ENFORCE(in_vars[i]->IsType()); + PADDLE_ENFORCE(out_vars[i]->IsType()); + } + + auto in_tensors = context.MultiInput("Input"); + + if (context.Attr("check_name")) { + for (size_t i = 0; i < in_var_names.size(); ++i) { + PADDLE_ENFORCE_EQ(in_var_names[i], out_var_names[i]); + } + } else { + // Init the output as input + for (size_t i = 0; i < in_tensors.size(); ++i) { + out_vars[i]->GetMutable()->Resize( + in_tensors[i]->dims()); + } + } + + auto &dev_ctx = context.template device_context(); + + // Get numel and dtype + size_t numel = 0; + auto dtype = kDefaultDtype; + GetMemSizeAndDtype(in_tensors, in_var_names, &numel, &dtype); + + // Alloc the continuous space + auto fused_tensor = context.Output("FusedOutput"); + fused_tensor->Resize(framework::make_ddim({static_cast(numel)})) + .mutable_data(context.GetPlace(), dtype); + + // Init the continuous space + auto out_tensors = context.MultiOutput("Output"); + int64_t offset = 0; + if (context.Attr("copy_data")) { + for (size_t i = 0; i < in_var_names.size(); ++i) { + int64_t len = out_tensors[i]->numel(); + auto sub_tensor = fused_tensor->Slice(offset, offset + len); + offset += len; + framework::TensorCopy(*out_tensors[i], context.GetPlace(), dev_ctx, + &sub_tensor); + } + } else if (context.Attr("set_constant")) { + math::SetConstant set_constant; + set_constant(dev_ctx, fused_tensor, + static_cast(context.Attr("constant"))); + } + + // Make the outputs point to the continuous space. + offset = 0; + for (size_t i = 0; i < out_tensors.size(); ++i) { + int64_t len = out_tensors[i]->numel(); + auto dim = out_tensors[i]->dims(); + out_tensors[i] + ->ShareDataWith(fused_tensor->Slice(offset, offset + len)) + .Resize(dim); + offset += len; + VLOG(10) << "alloc_space_for_vars: output(" << out_var_names[i] + << ") ,dim:(" << dim << ")" + << " Address: " << out_tensors[i]->data(); + } + } + + void GetMemSizeAndDtype( + const std::vector &lod_tensors, + const std::vector var_names, size_t *numel, + framework::proto::VarType::Type *dtype) const { + PADDLE_ENFORCE_EQ(lod_tensors.size(), var_names.size()); + *numel = 0; + for (size_t i = 0; i < var_names.size(); ++i) { + PADDLE_ENFORCE(lod_tensors[i]->IsInitialized(), "%s is not initialized.", + var_names[i]); + + auto p_dtype = lod_tensors[i]->type(); + if (*dtype == kDefaultDtype) { + PADDLE_ENFORCE_NE(p_dtype, kDefaultDtype, "%s's type should not be %s.", + var_names[i], kDefaultDtype); + *dtype = p_dtype; + } + PADDLE_ENFORCE_EQ(p_dtype, *dtype, "Input vars is not equal."); + + auto size = lod_tensors[i]->numel(); + PADDLE_ENFORCE_GT(size, 0); + VLOG(10) << "alloc_space_for_vars: input(" << var_names[i] << ") ,dim:(" + << lod_tensors[i]->dims() << ")"; + *numel += size; + } + } +}; + +class AllocContinuousSpaceOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + void InferShape(framework::InferShapeContext *ctx) const override {} +}; + +class AllocContinuousSpaceOpMaker : public framework::OpProtoAndCheckerMaker { + public: + void Make() override { + AddInput("Input", + "(vector) The input tensors of" + " alloc_continuous_space operator.") + .AsDuplicable(); + AddOutput("Output", + "(vector) The output " + "tensors of alloc_continuous_space operator. And the address " + "of output tensors are continuous, they are sliced from the " + "tensor of FusedOutput.") + .AsDuplicable(); + AddOutput("FusedOutput", + "(LoDTensor) The output tensor " + "of alloc_continuous_space operator. And the tensors of" + " Output is sliced from the tensor of FusedOutput."); + AddAttr("copy_data", "Whether to copy the Input value to Output.") + .SetDefault(false); + AddAttr("set_constant", + "Whether to set the Output with a constant value.") + .SetDefault(false); + AddAttr("constant", + "If set_constant is true, the constant value will be used " + "to set the Output.") + .SetDefault(0.0); + AddAttr("check_name", + "Whether to check the name of Input and Output to ensure " + "they are the same separately.") + .SetDefault(false); + AddComment(R"DOC( +AllocContinuousSpace Operator. + +alloc_continuous_space is used to make the address of Output +continuous according to the Input. This Op will alloc a big tensor +according to the tensors of Input, the dtype is the same with those input tensors, +the size is the sum of those input tensors' numel, and the dim of the big +tensor is {sum(numel)}. And the big tensor is stored in FusedOutput. +The tensors of Output are sliced from the tensor of FusedOutput. +Note that, the dtype of Input should be the same, and the dim of Input +and Output should equal. +The tensors of Input and Output could be the same or different. And +alloc_continuous_space allows copying the value of Input to Output, or +setting the Output with a constant value. + +)DOC"); + } +}; + +} // namespace operators +} // namespace paddle + +REGISTER_OPERATOR(alloc_continuous_space, + paddle::operators::AllocContinuousSpaceOp, + paddle::operators::AllocContinuousSpaceOpMaker); +namespace ops = paddle::operators; +REGISTER_OP_CPU_KERNEL( + alloc_continuous_space, + ops::AllocContinuousSpaceKernel, + ops::AllocContinuousSpaceKernel, + ops::AllocContinuousSpaceKernel); + +#ifdef PADDLE_WITH_CUDA +REGISTER_OP_CUDA_KERNEL( + alloc_continuous_space, + ops::AllocContinuousSpaceKernel, + ops::AllocContinuousSpaceKernel, + ops::AllocContinuousSpaceKernel); +#endif diff --git a/paddle/fluid/operators/benchmark/op_tester.cc b/paddle/fluid/operators/benchmark/op_tester.cc index e179de56cddc5fada2e5833086d351659a7cf540..064903c299d947df3c6b42d916fce8dcbd85eebb 100644 --- a/paddle/fluid/operators/benchmark/op_tester.cc +++ b/paddle/fluid/operators/benchmark/op_tester.cc @@ -13,6 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/fluid/operators/benchmark/op_tester.h" +#include #include "gflags/gflags.h" #include "gtest/gtest.h" #include "paddle/fluid/framework/op_info.h" @@ -28,6 +29,7 @@ namespace operators { namespace benchmark { DEFINE_string(op_config_list, "", "Path of op config file."); +DEFINE_int32(specified_config_id, -1, "Test the specified op config."); void OpTester::Init(const std::string &filename) { Init(OpTesterConfig(filename)); @@ -147,7 +149,7 @@ void OpTester::CreateInputVarDesc() { var->SetShape(input->dims); op_desc_.SetInput(name, {var_name}); - inputs_.push_back(var_name); + input_lods_[var_name] = input->lod; } } @@ -162,7 +164,6 @@ void OpTester::CreateOutputVarDesc() { var->SetDataType(framework::proto::VarType::FP32); op_desc_.SetOutput(name, {var_name}); - outputs_.push_back(var_name); } } @@ -218,16 +219,26 @@ void OpTester::CreateVariables(framework::Scope *scope) { } } - // Allocate memory for input tensor - for (auto &name : inputs_) { - VLOG(3) << "Allocate memory for tensor " << name; - auto &var_desc = vars_[name]; + for (auto &item : input_lods_) { + // Allocate memory for input tensor + auto &var_name = item.first; + VLOG(3) << "Allocate memory for tensor " << var_name; + + auto &var_desc = vars_[var_name]; std::vector shape = var_desc->GetShape(); - auto *var = scope->Var(name); + auto *var = scope->Var(var_name); auto *tensor = var->GetMutable(); SetupTensor(tensor, shape, static_cast(0.0), static_cast(1.0)); + + VLOG(3) << "Set lod for tensor " << var_name; + std::vector> &lod_vec = item.second; + framework::LoD lod; + for (size_t i = 0; i < lod_vec.size(); ++i) { + lod.push_back(lod_vec[i]); + } + tensor->set_lod(lod); } } @@ -282,10 +293,32 @@ std::string OpTester::DebugString() { } TEST(op_tester, base) { - OpTester tester; if (!FLAGS_op_config_list.empty()) { - tester.Init(FLAGS_op_config_list); + std::ifstream fin(FLAGS_op_config_list, std::ios::in | std::ios::binary); + PADDLE_ENFORCE(static_cast(fin), "Cannot open file %s", + FLAGS_op_config_list.c_str()); + std::vector op_configs; + while (!fin.eof()) { + OpTesterConfig config; + bool result = config.Init(fin); + if (result) { + op_configs.push_back(config); + } + } + if (FLAGS_specified_config_id >= 0 && + FLAGS_specified_config_id < static_cast(op_configs.size())) { + OpTester tester; + tester.Init(op_configs[FLAGS_specified_config_id]); + tester.Run(); + } else { + for (size_t i = 0; i < op_configs.size(); ++i) { + OpTester tester; + tester.Init(op_configs[i]); + tester.Run(); + } + } } else { + OpTester tester; OpTesterConfig config; config.op_type = "elementwise_add"; config.inputs.resize(2); @@ -294,8 +327,8 @@ TEST(op_tester, base) { config.inputs[1].name = "Y"; config.inputs[1].dims = {64, 1}; tester.Init(config); + tester.Run(); } - tester.Run(); } } // namespace benchmark diff --git a/paddle/fluid/operators/benchmark/op_tester.h b/paddle/fluid/operators/benchmark/op_tester.h index 1723d46c47ed67199713e6d726c6245f34f7c224..8f150b23ad783acdfd203d471d578ab6aae71494 100644 --- a/paddle/fluid/operators/benchmark/op_tester.h +++ b/paddle/fluid/operators/benchmark/op_tester.h @@ -57,8 +57,7 @@ class OpTester { std::string type_; framework::OpDesc op_desc_; std::unordered_map> vars_; - std::vector inputs_; - std::vector outputs_; + std::unordered_map>> input_lods_; std::unique_ptr op_; platform::Place place_; std::unique_ptr scope_; diff --git a/paddle/fluid/operators/benchmark/op_tester_config.cc b/paddle/fluid/operators/benchmark/op_tester_config.cc index 3db8de7f76801eb814b57859d6b95590761c96f3..8336804ec07d2b7b176f55ad4113452086296494 100644 --- a/paddle/fluid/operators/benchmark/op_tester_config.cc +++ b/paddle/fluid/operators/benchmark/op_tester_config.cc @@ -33,21 +33,64 @@ static bool EndWith(const std::string& str, const std::string& substr) { return str.rfind(substr) == (str.length() - substr.length()); } -static void EraseEndSep(std::string* str) { - std::string substr = kSepBetweenItems; +static void EraseEndSep(std::string* str, + std::string substr = kSepBetweenItems) { if (EndWith(*str, substr)) { str->erase(str->length() - substr.length(), str->length()); } } -static std::vector ParseDims(std::string dims_str) { - std::vector dims; +void OpInputConfig::ParseDims(std::istream& is) { + std::string dims_str; + is >> dims_str; + + dims.clear(); std::string token; std::istringstream token_stream(dims_str); while (std::getline(token_stream, token, 'x')) { dims.push_back(std::stoi(token)); } - return dims; +} + +void OpInputConfig::ParseLoD(std::istream& is) { + std::string lod_str; + std::string start_sep = + std::string(kStartSeparator) + std::string(kStartSeparator); + std::string end_sep = std::string(kEndSeparator) + std::string(kEndSeparator); + + std::string sep; + is >> sep; + if (StartWith(sep, start_sep)) { + lod_str += sep; + while (!EndWith(sep, end_sep)) { + is >> sep; + lod_str += sep; + } + } + EraseEndSep(&lod_str); + PADDLE_ENFORCE_GE(lod_str.length(), 4U); + VLOG(4) << "lod: " << lod_str << ", length: " << lod_str.length(); + + // Parse the lod_str + lod.clear(); + for (size_t i = 1; i < lod_str.length() - 1;) { + if (lod_str[i] == '{') { + std::vector level; + while (lod_str[i] != '}') { + ++i; + + std::string number; + while (lod_str[i] >= '0' && lod_str[i] <= '9') { + number += lod_str[i]; + ++i; + } + level.push_back(atoi(number.c_str())); + } + lod.push_back(level); + } else if (lod_str[i] == '}') { + ++i; + } + } } OpInputConfig::OpInputConfig(std::istream& is) { @@ -60,9 +103,9 @@ OpInputConfig::OpInputConfig(std::istream& is) { is >> name; EraseEndSep(&name); } else if (sep == "dims" || sep == "dims:") { - std::string dims_str; - is >> dims_str; - dims = ParseDims(dims_str); + ParseDims(is); + } else if (sep == "lod" || sep == "lod:") { + ParseLoD(is); } } } @@ -76,7 +119,7 @@ OpTesterConfig::OpTesterConfig(const std::string& filename) { Init(fin); } -void OpTesterConfig::Init(std::istream& is) { +bool OpTesterConfig::Init(std::istream& is) { std::string sep; is >> sep; if (sep == kStartSeparator) { @@ -95,9 +138,40 @@ void OpTesterConfig::Init(std::istream& is) { } else if (sep == "input" || sep == "input:") { OpInputConfig input_config(is); inputs.push_back(input_config); + } else if (sep == "attrs" || sep == "attrs:") { + ParseAttrs(is); + } else { + if (sep != kEndSeparator) { + return false; + } } } + } else { + return false; + } + return true; +} + +bool OpTesterConfig::ParseAttrs(std::istream& is) { + std::string sep; + is >> sep; + if (sep == kStartSeparator) { + while (true) { + std::string key; + is >> key; + if (key == kEndSeparator) { + break; + } + + std::string value; + is >> value; + EraseEndSep(&key, ":"); + EraseEndSep(&value); + + attrs[key] = value; + } } + return true; } const OpInputConfig* OpTesterConfig::GetInput(const std::string& name) { diff --git a/paddle/fluid/operators/benchmark/op_tester_config.h b/paddle/fluid/operators/benchmark/op_tester_config.h index f7b62cb8ad03b410a2ea99fe4c2a8dc8a6bea7a7..c2ff6dafc053eb7202a686954d53ae6f3d62d02e 100644 --- a/paddle/fluid/operators/benchmark/op_tester_config.h +++ b/paddle/fluid/operators/benchmark/op_tester_config.h @@ -16,6 +16,7 @@ limitations under the License. */ #include #include +#include #include namespace paddle { @@ -26,19 +27,27 @@ struct OpInputConfig { OpInputConfig() {} explicit OpInputConfig(std::istream& is); + void ParseDims(std::istream& is); + void ParseLoD(std::istream& is); + std::string name; std::vector dims; + std::vector> lod; }; struct OpTesterConfig { OpTesterConfig() {} explicit OpTesterConfig(const std::string& filename); - void Init(std::istream& is); + + bool Init(std::istream& is); + + bool ParseAttrs(std::istream& is); const OpInputConfig* GetInput(const std::string& name); std::string op_type; std::vector inputs; + std::unordered_map attrs; int device_id{-1}; // CPU: -1 int repeat{1}; int profile{0}; diff --git a/paddle/fluid/operators/conv_op.cc b/paddle/fluid/operators/conv_op.cc index a37c8d3ccd9c3bb8fae8a5f198bc4db714301b68..ca6bc4df0fe2c6cddaf548d3e708e777172a0841 100644 --- a/paddle/fluid/operators/conv_op.cc +++ b/paddle/fluid/operators/conv_op.cc @@ -81,6 +81,7 @@ framework::OpKernelType ConvOp::GetExpectedKernelType( framework::OpKernelType::kDefaultCustomizedTypeValue; framework::LibraryType library{framework::LibraryType::kPlain}; // TODO(pzelazko-intel): enable MKLDNN layout when it's ready + auto input_data_type = ctx.Input("Input")->type(); std::string data_format = ctx.Attr("data_format"); framework::DataLayout layout = framework::StringToDataLayout(data_format); @@ -94,11 +95,14 @@ framework::OpKernelType ConvOp::GetExpectedKernelType( platform::CanMKLDNNBeUsed(ctx)) { library = framework::LibraryType::kMKLDNN; layout = framework::DataLayout::kMKLDNN; - customized_type_value = kConvMKLDNNFP32; + customized_type_value = + (input_data_type == framework::DataTypeTrait::DataType || + input_data_type == framework::DataTypeTrait::DataType) + ? kConvMKLDNNINT8 + : kConvMKLDNNFP32; } #endif - auto input_data_type = ctx.Input("Input")->type(); if (input_data_type != framework::proto::VarType::INT8 && input_data_type != framework::proto::VarType::UINT8) { auto filter_data_type = ctx.Input("Filter")->type(); diff --git a/paddle/fluid/operators/cross_entropy_op.cc b/paddle/fluid/operators/cross_entropy_op.cc index 1968e54b00601139e252f0480ca3ae1fc08904f4..3adc7baebddd06ced74afea1e77017beb57582e8 100644 --- a/paddle/fluid/operators/cross_entropy_op.cc +++ b/paddle/fluid/operators/cross_entropy_op.cc @@ -32,14 +32,23 @@ class CrossEntropyOp : public framework::OperatorWithKernel { int rank = x_dims.size(); PADDLE_ENFORCE_EQ(rank, label_dims.size(), "Input(X) and Input(Label) shall have the same rank."); - PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 0, rank - 1), - framework::slice_ddim(label_dims, 0, rank - 1), - "Input(X) and Input(Label) shall have the same shape " - "except the last dimension."); + bool check = true; + if ((!ctx->IsRuntime()) && (framework::product(x_dims) <= 0 || + framework::product(label_dims) <= 0)) { + check = false; + } + if (check) { + PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 0, rank - 1), + framework::slice_ddim(label_dims, 0, rank - 1), + "Input(X) and Input(Label) shall have the same shape " + "except the last dimension."); + } if (ctx->Attrs().Get("soft_label")) { - PADDLE_ENFORCE_EQ(x_dims[rank - 1], label_dims[rank - 1], - "If Attr(soft_label) == true, the last dimension of " - "Input(X) and Input(Label) should be equal."); + if (check) { + PADDLE_ENFORCE_EQ(x_dims[rank - 1], label_dims[rank - 1], + "If Attr(soft_label) == true, the last dimension of " + "Input(X) and Input(Label) should be equal."); + } } else { PADDLE_ENFORCE_EQ(label_dims[rank - 1], 1UL, "If Attr(softLabel) == false, the last dimension of " @@ -82,20 +91,32 @@ class CrossEntropyGradientOp : public framework::OperatorWithKernel { "Input(Y@Grad) and Input(X) should have the same rank."); PADDLE_ENFORCE_EQ(label_dims.size(), rank, "Input(Label) and Input(X) should have the same rank."); - PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 0, rank - 1), - framework::slice_ddim(label_dims, 0, rank - 1), - "The Input(X) and Input(Label) should have the same " - "shape except the last dimension."); - PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 0, rank - 1), - framework::slice_ddim(dy_dims, 0, rank - 1), - "The Input(X) and Input(Y@Grad) should have the same " - "shape except the last dimension."); + + bool check = true; + if ((!ctx->IsRuntime()) && (framework::product(x_dims) <= 0 || + framework::product(label_dims) <= 0)) { + check = false; + } + + if (check) { + PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 0, rank - 1), + framework::slice_ddim(label_dims, 0, rank - 1), + "The Input(X) and Input(Label) should have the same " + "shape except the last dimension."); + PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 0, rank - 1), + framework::slice_ddim(dy_dims, 0, rank - 1), + "The Input(X) and Input(Y@Grad) should have the same " + "shape except the last dimension."); + } PADDLE_ENFORCE_EQ(dy_dims[rank - 1], 1, "The last dimension of Input(Y@Grad) should be 1."); if (ctx->Attrs().Get("soft_label")) { - PADDLE_ENFORCE_EQ(x_dims[rank - 1], label_dims[rank - 1], - "When Attr(soft_label) == true, the last dimension of " - "Input(X) and Input(Label) should be equal."); + if (check) { + PADDLE_ENFORCE_EQ( + x_dims[rank - 1], label_dims[rank - 1], + "When Attr(soft_label) == true, the last dimension of " + "Input(X) and Input(Label) should be equal."); + } } else { PADDLE_ENFORCE_EQ(label_dims[rank - 1], 1, "When Attr(soft_label) == false, the last dimension of " diff --git a/paddle/fluid/operators/data_norm_op.cc b/paddle/fluid/operators/data_norm_op.cc index d5bc25d19cba4de6f059612e3e8c4a65b2edd0f9..45bce6e5203f8c1dbb744e0f954f7f0a71c53372 100644 --- a/paddle/fluid/operators/data_norm_op.cc +++ b/paddle/fluid/operators/data_norm_op.cc @@ -140,9 +140,6 @@ class DataNormOpMaker : public framework::OpProtoAndCheckerMaker { "Scales of the history data batch, " "will apply to output when training") .AsIntermediate(); - AddAttr("use_mkldnn", - "(bool, default false) Only used in mkldnn kernel") - .SetDefault(false); AddComment(R"DOC( Data Normalization. diff --git a/paddle/fluid/operators/detection/prior_box_op.h b/paddle/fluid/operators/detection/prior_box_op.h index f84405664596ebe25983e5acbbb82bfc18c38124..d3e26256b50f2d7010fee3738802d59173678b34 100644 --- a/paddle/fluid/operators/detection/prior_box_op.h +++ b/paddle/fluid/operators/detection/prior_box_op.h @@ -172,6 +172,10 @@ class PriorBoxOpKernel : public framework::OpKernel { framework::make_ddim({1, static_cast(variances.size())}), ctx.GetPlace()); auto var_et = framework::EigenTensor::From(var_t); + +#ifdef PADDLE_WITH_MKLML +#pragma omp parallel for +#endif for (size_t i = 0; i < variances.size(); ++i) { var_et(0, i) = variances[i]; } @@ -181,8 +185,15 @@ class PriorBoxOpKernel : public framework::OpKernel { vars->Resize({box_num, static_cast(variances.size())}); auto e_vars = framework::EigenMatrix::From(*vars); - e_vars = var_et.broadcast(Eigen::DSizes(box_num, 1)); +#ifdef PADDLE_WITH_MKLML +#pragma omp parallel for collapse(2) +#endif + for (int i = 0; i < box_num; ++i) { + for (int j = 0; j < variances.size(); ++j) { + e_vars(i, j) = variances[j]; + } + } vars->Resize(var_dim); } }; // namespace operators diff --git a/paddle/fluid/operators/elementwise/mkldnn/elementwise_add_mkldnn_op.cc b/paddle/fluid/operators/elementwise/mkldnn/elementwise_add_mkldnn_op.cc index 6a6741d8fc54d22addca91b75dfabf5950c1a35a..7aaa607f1585c98fe2dd816e8d66e5c6fd171e80 100644 --- a/paddle/fluid/operators/elementwise/mkldnn/elementwise_add_mkldnn_op.cc +++ b/paddle/fluid/operators/elementwise/mkldnn/elementwise_add_mkldnn_op.cc @@ -77,8 +77,7 @@ class EltwiseAddMKLDNNKernel : public framework::OpKernel { } else { functor.RunMidWise(n, pre, post); } - z->set_layout(DataLayout::kMKLDNN); - z->set_format(x->format()); + z->set_mkldnn_prim_desc(x->get_mkldnn_prim_desc()); } else { PADDLE_ENFORCE(x->layout() == DataLayout::kMKLDNN && x->format() != memory::format::format_undef, @@ -116,7 +115,8 @@ class EltwiseAddMKLDNNKernel : public framework::OpKernel { auto sum_pd = sum::primitive_desc(dst_md, scales, srcs_pd); // create mkldnn memory for dst - memory dst_memory = memory(sum_pd.dst_primitive_desc(), z_data); + auto dst_mem_pd = sum_pd.dst_primitive_desc(); + memory dst_memory = memory(dst_mem_pd, z_data); std::vector inputs; inputs.push_back(srcs[0]); @@ -129,9 +129,7 @@ class EltwiseAddMKLDNNKernel : public framework::OpKernel { pipeline.push_back(sum_prim); stream(stream::kind::eager).submit(pipeline).wait(); - z->set_layout(DataLayout::kMKLDNN); - z->set_format( - (memory::format)dst_memory.get_primitive_desc().desc().data.format); + z->set_mkldnn_prim_desc(dst_mem_pd); } } }; @@ -152,24 +150,19 @@ class EltwiseAddMKLDNNGradKernel : public ElemwiseGradKernel { auto* out = dout; auto *x = dout, *y = dout; - auto set_mkldnn_format = [](Tensor* in, const Tensor* out) { - in->set_layout(DataLayout::kMKLDNN); - in->set_format(out->format()); - }; - if (dx != nullptr && dy != nullptr && dx->dims() == dy->dims()) { if (dx->dims() == dy->dims()) { auto blas = math::GetBlas(ctx); if (dx) { blas.VCOPY(dout->numel(), dout->data(), dx->mutable_data(ctx.GetPlace())); - set_mkldnn_format(dx, dout); + dx->set_mkldnn_prim_desc(dout->get_mkldnn_prim_desc()); } if (dy) { blas.VCOPY(dout->numel(), dout->data(), dy->mutable_data(ctx.GetPlace())); - set_mkldnn_format(dy, dout); + dy->set_mkldnn_prim_desc(dout->get_mkldnn_prim_desc()); } } } else { diff --git a/paddle/fluid/operators/fake_quantize_op.cc b/paddle/fluid/operators/fake_quantize_op.cc index d51eb054a96d27f6ce87ba4b4e717f49dcd8a588..3bb07d383548e6f4be810c96d2a916c0fe5e45f5 100644 --- a/paddle/fluid/operators/fake_quantize_op.cc +++ b/paddle/fluid/operators/fake_quantize_op.cc @@ -31,7 +31,7 @@ template struct FindAbsMaxFunctor { void operator()(const platform::CPUDeviceContext& ctx, const T* in, const int num, T* out) { - *out = *(std::max_element(in + 0, in + num, Compare())); + *out = std::abs(*(std::max_element(in + 0, in + num, Compare()))); } }; @@ -46,10 +46,8 @@ struct ClipAndFakeQuantFunctor { platform::Transform trans; trans(ctx, in.data(), in.data() + in.numel(), out->mutable_data(ctx.GetPlace()), ClipFunctor(-s, s)); - auto in_e = framework::EigenVector::Flatten(in); auto out_e = framework::EigenVector::Flatten(*out); - - out_e.device(*ctx.eigen_device()) = (bin_cnt / s * in_e).round(); + out_e.device(*ctx.eigen_device()) = (bin_cnt / s * out_e).round(); } }; diff --git a/paddle/fluid/operators/fused/fused_embedding_seq_pool_op.cc b/paddle/fluid/operators/fused/fused_embedding_seq_pool_op.cc index fe4c73f4723355d4b56d075423de29b45b9cd4e4..80caf70b08e65932d6ccb90a5293d072b2b2bc72 100644 --- a/paddle/fluid/operators/fused/fused_embedding_seq_pool_op.cc +++ b/paddle/fluid/operators/fused/fused_embedding_seq_pool_op.cc @@ -23,6 +23,9 @@ class FusedEmbeddingSeqPoolOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; void InferShape(framework::InferShapeContext* ctx) const override { + if (ctx->IsRuntime()) { + return; + } PADDLE_ENFORCE(ctx->HasInput("W"), "Input W of FusedEmbeddingSeqPoolOp should not be null."); PADDLE_ENFORCE(ctx->HasInput("Ids"), @@ -42,36 +45,15 @@ class FusedEmbeddingSeqPoolOp : public framework::OperatorWithKernel { // we only support sum now PADDLE_ENFORCE_EQ(combiner, "sum"); - int64_t last_dim = table_dims[1]; - for (int i = 1; i != ids_dims.size(); ++i) { - last_dim *= ids_dims[i]; - } - - if (ctx->IsRuntime()) { - framework::Variable* ids_var = - boost::get(ctx->GetInputVarPtrs("Ids")[0]); - const auto& ids_lod = ids_var->Get().lod(); + int64_t last_dim = FusedEmbeddingSeqPoolLastDim(table_dims, ids_dims); + // in compile time, the lod level of ids must be 1 + framework::VarDesc* ids_desc = + boost::get(ctx->GetInputVarPtrs("Ids")[0]); + PADDLE_ENFORCE_EQ(ids_desc->GetLoDLevel(), 1); - // in run time, the LoD of ids must be 1 - PADDLE_ENFORCE(ids_lod.size(), 1u, - "The LoD level of Input(Ids) must be 1"); - PADDLE_ENFORCE_GE(ids_lod[0].size(), 1u, "The LoD could NOT be empty"); - - int64_t batch_size = ids_lod[0].size() - 1; - - // in run time, the shape from Ids -> output - // should be [seq_length, 1] -> [batch_size, embedding_size] - ctx->SetOutputDim("Out", framework::make_ddim({batch_size, last_dim})); - } else { - // in compile time, the lod level of ids must be 1 - framework::VarDesc* ids_desc = - boost::get(ctx->GetInputVarPtrs("Ids")[0]); - PADDLE_ENFORCE_EQ(ids_desc->GetLoDLevel(), 1); - - // in compile time, the shape from Ids -> output - // should be [-1, 1] -> [-1, embedding_size] - ctx->SetOutputDim("Out", framework::make_ddim({-1, last_dim})); - } + // in compile time, the shape from Ids -> output + // should be [-1, 1] -> [-1, embedding_size] + ctx->SetOutputDim("Out", framework::make_ddim({-1, last_dim})); } protected: diff --git a/paddle/fluid/operators/fused/fused_embedding_seq_pool_op.h b/paddle/fluid/operators/fused/fused_embedding_seq_pool_op.h index 33a1b47d150f653b84a377a61b251491aa719bee..2b0c1f560f23eee7fbdf14444bf933535b704167 100644 --- a/paddle/fluid/operators/fused/fused_embedding_seq_pool_op.h +++ b/paddle/fluid/operators/fused/fused_embedding_seq_pool_op.h @@ -61,6 +61,15 @@ struct EmbeddingVSumFunctor { } }; +inline int FusedEmbeddingSeqPoolLastDim(const framework::DDim &table_dims, + const framework::DDim &ids_dims) { + int64_t last_dim = table_dims[1]; + for (int i = 1; i != ids_dims.size(); ++i) { + last_dim *= ids_dims[i]; + } + return last_dim; +} + template class FusedEmbeddingSeqPoolKernel : public framework::OpKernel { public: @@ -70,6 +79,17 @@ class FusedEmbeddingSeqPoolKernel : public framework::OpKernel { const LoDTensor *table_var = context.Input("W"); const std::string &combiner_type = context.Attr("combiner"); + int64_t last_dim = + FusedEmbeddingSeqPoolLastDim(table_var->dims(), ids_t->dims()); + const auto &ids_lod = ids_t->lod(); + // in run time, the LoD of ids must be 1 + PADDLE_ENFORCE(ids_lod.size(), 1u, "The LoD level of Input(Ids) must be 1"); + PADDLE_ENFORCE_GE(ids_lod[0].size(), 1u, "The LoD could NOT be empty"); + int64_t batch_size = ids_lod[0].size() - 1; + // in run time, the shape from Ids -> output + // should be [seq_length, 1] -> [batch_size, embedding_size] + output_t->Resize({batch_size, last_dim}); + if (combiner_type == "sum") { EmbeddingVSumFunctor functor; functor(context, table_var, ids_t, output_t); diff --git a/paddle/fluid/operators/hash_op.cc b/paddle/fluid/operators/hash_op.cc index b2c2c7954b79658e66f1524a81bcad0b7bf22c35..7a29f80ff1ce413519ea9cea6a35747bdced5885 100644 --- a/paddle/fluid/operators/hash_op.cc +++ b/paddle/fluid/operators/hash_op.cc @@ -14,7 +14,6 @@ limitations under the License. */ #include "paddle/fluid/operators/hash_op.h" #include -#include namespace paddle { namespace operators { @@ -27,6 +26,9 @@ class HashOp : public framework::OperatorWithKernel { : OperatorWithKernel(type, inputs, outputs, attrs) {} void InferShape(framework::InferShapeContext *ctx) const override { + if (ctx->IsRuntime()) { + return; + } PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of HashOp should not be null."); PADDLE_ENFORCE(ctx->HasOutput("Out"), @@ -36,15 +38,8 @@ class HashOp : public framework::OperatorWithKernel { PADDLE_ENFORCE_EQ(dims.size(), 2UL, "The input of hash_op's dimensions must be 2"); std::vector out_dims; - out_dims.reserve(dims.size() + 1); - // copy all dims except the last one - for (int i = 0u; i != dims.size() - 1; ++i) { - out_dims.emplace_back(dims[i]); - } int num_hash = ctx->Attrs().Get("num_hash"); - out_dims.emplace_back(num_hash); - // keep the last dim to 1 - out_dims.emplace_back(1); + HashOutputSize(dims, out_dims, num_hash); ctx->SetOutputDim("Out", framework::make_ddim(out_dims)); ctx->ShareLoD("X", /*->*/ "Out"); @@ -71,4 +66,4 @@ $$Out = scale * X$$ namespace ops = paddle::operators; REGISTER_OP_WITHOUT_GRADIENT(hash, ops::HashOp, ops::HashOpMaker); -REGISTER_OP_CPU_KERNEL(hash, ops::HashKerel, ops::HashKerel); +REGISTER_OP_CPU_KERNEL(hash, ops::HashKernel, ops::HashKernel); diff --git a/paddle/fluid/operators/hash_op.h b/paddle/fluid/operators/hash_op.h index 9781bb0f453642cefb3eb59a05389c339a7de39d..9e7ad5235ff483a2fc0cfbb8bc35c620084bb896 100644 --- a/paddle/fluid/operators/hash_op.h +++ b/paddle/fluid/operators/hash_op.h @@ -17,21 +17,34 @@ limitations under the License. */ extern "C" { #include } +#include #include "paddle/fluid/framework/eigen.h" #include "paddle/fluid/framework/op_registry.h" namespace paddle { namespace operators { -// template + +inline void HashOutputSize(const framework::DDim& in_dims, + std::vector& out_dims, // NOLINT + int num_hash) { + out_dims.reserve(in_dims.size() + 1); + // copy all dims except the last one + for (int i = 0u; i != in_dims.size() - 1; ++i) { + out_dims.emplace_back(in_dims[i]); + } + out_dims.emplace_back(num_hash); + // keep the last dim to 1 + out_dims.emplace_back(1); +} + template -class HashKerel : public framework::OpKernel { +class HashKernel : public framework::OpKernel { public: virtual void Compute(const framework::ExecutionContext& context) const { auto* out_t = context.Output("Out"); auto* in_t = context.Input("X"); int mod_by = context.Attr("mod_by"); int num_hash = context.Attr("num_hash"); - auto* output = out_t->mutable_data(context.GetPlace()); auto in_dims = in_t->dims(); auto in_lod = in_t->lod(); @@ -39,6 +52,11 @@ class HashKerel : public framework::OpKernel { static_cast(in_dims[0]), in_lod[0].back(), "The actual input data's size mismatched with LoD information."); + std::vector out_dims; + HashOutputSize(in_dims, out_dims, num_hash); + out_t->Resize(framework::make_ddim(out_dims)); + auto* output = out_t->mutable_data(context.GetPlace()); + auto seq_length = in_dims[0]; auto last_dim = in_dims[in_dims.size() - 1]; auto* input = in_t->data(); @@ -49,6 +67,7 @@ class HashKerel : public framework::OpKernel { } input += last_dim; } + out_t->set_lod(in_t->lod()); } }; diff --git a/paddle/fluid/operators/interpolate_op.cc b/paddle/fluid/operators/interpolate_op.cc index de91ba6270ac2ed22c8380878c0a0037fb1629c0..10d01af982d01800bdd2d5d59761cfb09e2a8139 100644 --- a/paddle/fluid/operators/interpolate_op.cc +++ b/paddle/fluid/operators/interpolate_op.cc @@ -84,13 +84,13 @@ class InterpolateOpMaker : public framework::OpProtoAndCheckerMaker { .SetDefault("bilinear"); AddAttr( "align_corners", - "an optinal bool. Defaults to True. " + "an optional bool. Defaults to True. " "If True, the centers of 4 corner pixels of the input and output " "tensors are aligned, preserving the values at the corner pixels, " - "if Flase, are not aligned") + "If False, are not aligned") .SetDefault(true); AddAttr("align_mode", - "(int, default \'1\'), optional for bilinear interpolation" + "(int, default \'1\'), optional for bilinear interpolation, " "can be \'0\' for src_idx = scale*(dst_indx+0.5)-0.5 , " "can be \'1\' for src_idx = scale*dst_index .") .SetDefault(1); diff --git a/paddle/fluid/operators/is_empty_op.cc b/paddle/fluid/operators/is_empty_op.cc index ba50bdf34baf2b9b0748b24c98c274aa18e22e36..092a6eae6f5b7edcc5656522377de10a08a01ea8 100644 --- a/paddle/fluid/operators/is_empty_op.cc +++ b/paddle/fluid/operators/is_empty_op.cc @@ -34,9 +34,8 @@ class IsEmptyOp : public framework::OperatorWithKernel { framework::OpKernelType GetExpectedKernelType( const framework::ExecutionContext &ctx) const override { - framework::OpKernelType kt = framework::OpKernelType( - ctx.Input("X")->type(), platform::CPUPlace()); - return kt; + auto *x = ctx.Input("X"); + return framework::OpKernelType(x->type(), x->place()); } }; @@ -58,7 +57,6 @@ It will just return product(tensor.ddims()) > 0; } // namespace paddle namespace ops = paddle::operators; - REGISTER_OPERATOR(is_empty, ops::IsEmptyOp, ops::IsEmptyOpMaker, paddle::framework::EmptyGradOpMaker); REGISTER_OP_CPU_KERNEL( diff --git a/paddle/fluid/operators/is_empty_op.cu.cc b/paddle/fluid/operators/is_empty_op.cu.cc new file mode 100644 index 0000000000000000000000000000000000000000..3c256503baf6ba3bc8f8dff866a2ce9c57ec5bf1 --- /dev/null +++ b/paddle/fluid/operators/is_empty_op.cu.cc @@ -0,0 +1,23 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/fluid/operators/is_empty_op.h" +#include "paddle/fluid/framework/op_registry.h" + +namespace ops = paddle::operators; +REGISTER_OP_CUDA_KERNEL( + is_empty, ops::IsEmptyOpKernel, + ops::IsEmptyOpKernel, + ops::IsEmptyOpKernel, + ops::IsEmptyOpKernel); diff --git a/paddle/fluid/operators/is_empty_op.h b/paddle/fluid/operators/is_empty_op.h index 3e3af22fa8d842b6a1e67418446f1a40949e046b..4f6419eb577709836275481cf617c07ea6c7f4c0 100644 --- a/paddle/fluid/operators/is_empty_op.h +++ b/paddle/fluid/operators/is_empty_op.h @@ -28,6 +28,9 @@ class IsEmptyOpKernel : public framework::OpKernel { // get output auto* output_tensor = context.Output("Out"); + // Note: is_empty is always executed on CPU and the output data should + // always be allocated for CPUPlace. We reigister CUDA kernel for this op to + // avoid the unnecessary data transform. output_tensor->mutable_data(platform::CPUPlace())[0] = framework::product(input_tensor->dims()) == 0; } diff --git a/paddle/fluid/operators/jit/benchmark.cc b/paddle/fluid/operators/jit/benchmark.cc index 3348778ee782ef0cdd1df4c3c4b24060436d7d79..11dc615f5ff8ea78bbbf6eeb655ee88b3a52dc13 100644 --- a/paddle/fluid/operators/jit/benchmark.cc +++ b/paddle/fluid/operators/jit/benchmark.cc @@ -332,6 +332,45 @@ void BenchEmbSeqPoolKernel() { } } +template +void BenchSgdKernel() { + const T lr = 0.1; + auto UnDuplicatedRandomVec = [](int n, const int64_t lower, + const int64_t upper) -> std::vector { + PADDLE_ENFORCE_LE(static_cast(upper - lower), n - 1); + PADDLE_ENFORCE_GT(n, 0); + std::vector all, out; + for (int i = 0; i < n; ++i) { + all.push_back(i); + } + std::random_shuffle(all.begin(), all.end()); + out.insert(out.begin(), all.begin(), all.begin() + n); + return out; + }; + for (int param_h : {1, 1000}) { + for (int grad_w : {1, 2, 8, 16, 30, 256}) { + // only benchmark inplace + Tensor param; + param.Resize({param_h, grad_w}); + T* param_data = param.mutable_data(PlaceType()); + RandomVec(param_h * grad_w, param_data, -2.f, 2.f); + for (int rows_size = 1; rows_size <= std::min(param_h, 10); ++rows_size) { + Tensor grad; + grad.Resize({rows_size, grad_w}); + std::vector rows = + UnDuplicatedRandomVec(rows_size, 0, rows_size - 1); + RandomVec(rows_size * grad_w, grad.mutable_data(PlaceType()), + -2.f, 2.f); + const T* grad_data = grad.data(); + const int64_t* rows_data = rows.data(); + jit::sgd_attr_t attr(param_h, grad_w, rows_size, grad_w, rows_size); + BenchAllImpls, PlaceType>( + attr, &lr, param_data, grad_data, rows_data, param_data, &attr); + } + } + } +} + template void BenchMatMulKernel() { for (int m : {1, 2, 3, 4}) { @@ -477,6 +516,9 @@ BENCH_FP32_CPU(kEmbSeqPool) { BenchEmbSeqPoolKernel(); } +// sgd function +BENCH_FP32_CPU(kSgd) { BenchSgdKernel(); } + // matmul BENCH_FP32_CPU(kMatMul) { BenchMatMulKernel(); } diff --git a/paddle/fluid/operators/jit/gen/CMakeLists.txt b/paddle/fluid/operators/jit/gen/CMakeLists.txt index 294f73d9646c93132e464a032e93562094663a73..eb0c03568ddddf1c456fec6fcc81f3b40d051844 100644 --- a/paddle/fluid/operators/jit/gen/CMakeLists.txt +++ b/paddle/fluid/operators/jit/gen/CMakeLists.txt @@ -32,3 +32,4 @@ USE_JITKERNEL_GEN(kSeqPool) USE_JITKERNEL_GEN(kHMax) USE_JITKERNEL_GEN(kHSum) USE_JITKERNEL_GEN(kEmbSeqPool) +USE_JITKERNEL_GEN(kSgd) diff --git a/paddle/fluid/operators/jit/gen/jitcode.h b/paddle/fluid/operators/jit/gen/jitcode.h index 689df8b1cbb7a928c9f9175d28a8231b56e2e82e..39847d1b65f771976c4dde5a3e34cc40e33851e6 100644 --- a/paddle/fluid/operators/jit/gen/jitcode.h +++ b/paddle/fluid/operators/jit/gen/jitcode.h @@ -31,7 +31,8 @@ namespace gen { // Application Binary Interface constexpr Xbyak::Operand::Code abi_param1(Xbyak::Operand::RDI), abi_param2(Xbyak::Operand::RSI), abi_param3(Xbyak::Operand::RDX), - abi_param4(Xbyak::Operand::RCX); + abi_param4(Xbyak::Operand::RCX), abi_param5(Xbyak::Operand::R8), + abi_param6(Xbyak::Operand::R9); constexpr Xbyak::Operand::Code g_abi_regs[] = { Xbyak::Operand::RBX, Xbyak::Operand::RBP, Xbyak::Operand::R12, diff --git a/paddle/fluid/operators/jit/gen/sgd.cc b/paddle/fluid/operators/jit/gen/sgd.cc new file mode 100644 index 0000000000000000000000000000000000000000..a745a27f9543a75f6915c9316aad62fa41305bb1 --- /dev/null +++ b/paddle/fluid/operators/jit/gen/sgd.cc @@ -0,0 +1,130 @@ +/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. */ + +#include "paddle/fluid/operators/jit/gen/sgd.h" +#include // offsetof +#include +#include "paddle/fluid/operators/jit/registry.h" +#include "paddle/fluid/platform/cpu_info.h" + +namespace paddle { +namespace operators { +namespace jit { +namespace gen { + +void SgdJitCode::genCode() { + preCode(); + constexpr int block = YMM_FLOAT_BLOCK; + constexpr int max_num_regs = 7; + const int num_block = w_ / block; + const int num_groups = num_block / max_num_regs; + const size_t block_size = sizeof(float) * block; + const size_t width_size = w_ * sizeof(float); + std::vector groups(num_groups, max_num_regs); + int rest_num_regs = num_block % max_num_regs; + if (rest_num_regs > 0) { + groups.push_back(rest_num_regs); + } + + vbroadcastss(ymm_lr, ptr[param_lr]); + // protect rdx + mov(reg_ptr_grad_i, param_grad); + mov(reg_ptr_rows_i, param_rows); + + mov(reg_rows_size_in_byte, + qword[param_attr + offsetof(sgd_attr_t, selected_rows_size)]); + mov(rax, sizeof(int64_t)); + mul(reg_rows_size_in_byte); + mov(reg_rows_size_in_byte, rax); + add(reg_rows_size_in_byte, reg_ptr_rows_i); + + Label l_next_row; + L(l_next_row); + { + mov(reg_row, qword[reg_ptr_rows_i]); + mov(rax, width_size); + mul(reg_row); + mov(reg_row, rax); + + mov(reg_ptr_param_i, param_param); + mov(reg_ptr_out_i, param_out); + add(reg_ptr_param_i, reg_row); + add(reg_ptr_out_i, reg_row); + + size_t w_offset = 0; + for (int num_regs : groups) { + // load grad + size_t inner_offfset = w_offset; + for (int reg_i = 0; reg_i < num_regs; ++reg_i) { + vmovups(ymm_t(reg_i), ptr[reg_ptr_grad_i + inner_offfset]); + inner_offfset += block_size; + } + + // load param + inner_offfset = w_offset; + for (int reg_i = 0; reg_i < num_regs; ++reg_i) { + vmovups(ymm_t(reg_i + num_regs), ptr[reg_ptr_param_i + inner_offfset]); + inner_offfset += block_size; + } + + // compute out + for (int reg_i = 0; reg_i < num_regs; ++reg_i) { + vmulps(ymm_t(reg_i), ymm_t(reg_i), ymm_lr); + vsubps(ymm_t(reg_i + num_regs), ymm_t(reg_i + num_regs), ymm_t(reg_i)); + } + + // save out + inner_offfset = w_offset; + for (int reg_i = 0; reg_i < num_regs; ++reg_i) { + vmovups(ptr[reg_ptr_out_i + inner_offfset], ymm_t(reg_i + num_regs)); + inner_offfset += block_size; + } + w_offset += (block_size * num_regs); + } + + add(reg_ptr_grad_i, width_size); + add(reg_ptr_rows_i, sizeof(int64_t)); + cmp(reg_ptr_rows_i, reg_rows_size_in_byte); + jl(l_next_row, T_NEAR); + } + + postCode(); +} + +class SgdCreator : public JitCodeCreator { + public: + bool UseMe(const sgd_attr_t& attr) const override { + return platform::MayIUse(platform::avx) && + attr.grad_width % YMM_FLOAT_BLOCK == 0; + } + size_t CodeSize(const sgd_attr_t& attr) const override { + return 96 + (attr.grad_width / YMM_FLOAT_BLOCK) * 32 * 8; + } + std::unique_ptr CreateJitCode( + const sgd_attr_t& attr) const override { + PADDLE_ENFORCE_EQ(attr.param_width, attr.grad_width); + PADDLE_ENFORCE_LE(attr.selected_rows_size, attr.grad_height); + PADDLE_ENFORCE_GE(attr.selected_rows_size, 0); + return make_unique(attr, CodeSize(attr)); + } +}; + +} // namespace gen +} // namespace jit +} // namespace operators +} // namespace paddle + +namespace gen = paddle::operators::jit::gen; + +REGISTER_JITKERNEL_GEN(kSgd, gen::SgdCreator); diff --git a/paddle/fluid/operators/jit/gen/sgd.h b/paddle/fluid/operators/jit/gen/sgd.h new file mode 100644 index 0000000000000000000000000000000000000000..317edcd2bcb5fea1f14f32260fd16c9c706eaf00 --- /dev/null +++ b/paddle/fluid/operators/jit/gen/sgd.h @@ -0,0 +1,60 @@ +/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. */ + +#pragma once + +#include +#include "glog/logging.h" +#include "paddle/fluid/operators/jit/gen/jitcode.h" +#include "paddle/fluid/platform/enforce.h" + +namespace paddle { +namespace operators { +namespace jit { +namespace gen { + +class SgdJitCode : public JitCode { + public: + explicit SgdJitCode(const sgd_attr_t& attr, size_t code_size = 256 * 1024, + void* code_ptr = nullptr) + : JitCode(code_size, code_ptr), w_(attr.grad_width) { + this->genCode(); + } + + DECLARE_JIT_CODE(SgdJitCode); + void genCode() override; + + private: + int w_; + reg64_t param_lr{abi_param1}; + reg64_t param_param{abi_param2}; + reg64_t param_grad{abi_param3}; + reg64_t param_rows{abi_param4}; + reg64_t param_out{abi_param5}; + reg64_t param_attr{abi_param6}; + + ymm_t ymm_lr = ymm_t(15); + + reg64_t reg_ptr_grad_i{r10}; + reg64_t reg_ptr_rows_i{r11}; + reg64_t reg_rows_size_in_byte{r12}; + reg64_t reg_row{r13}; + reg64_t reg_ptr_param_i{r14}; + reg64_t reg_ptr_out_i{r15}; +}; + +} // namespace gen +} // namespace jit +} // namespace operators +} // namespace paddle diff --git a/paddle/fluid/operators/jit/helper.cc b/paddle/fluid/operators/jit/helper.cc index a76653613289892c4bb41596f998c5f4cc131fd7..1dc60442d5c5f6acf49b6319223b190f6c81e1a6 100644 --- a/paddle/fluid/operators/jit/helper.cc +++ b/paddle/fluid/operators/jit/helper.cc @@ -55,6 +55,7 @@ const char* to_string(KernelType kt) { ONE_CASE(kHSum); ONE_CASE(kSoftmax); ONE_CASE(kEmbSeqPool); + ONE_CASE(kSgd); default: PADDLE_THROW("Not support type: %d, or forget to add it.", kt); return "NOT JITKernel"; diff --git a/paddle/fluid/operators/jit/helper.h b/paddle/fluid/operators/jit/helper.h index 07998588a5a560f9c2ad7cc765b66e76e87da6f6..d85c719c1c58c88ec244f1f6ad8343d66391241d 100644 --- a/paddle/fluid/operators/jit/helper.h +++ b/paddle/fluid/operators/jit/helper.h @@ -181,6 +181,14 @@ inline std::ostream& operator<<(std::ostream& os, return os; } +inline std::ostream& operator<<(std::ostream& os, const sgd_attr_t& attr) { + os << "param_height[" << attr.param_height << "],param_width[" + << attr.param_width << "],grad_height[" << attr.grad_height + << "],grad_width[" << attr.grad_width << "],selected_rows_size[" + << attr.selected_rows_size << "]"; + return os; +} + inline std::ostream& operator<<(std::ostream& os, const matmul_attr_t& attr) { os << "M[" << attr.m << "],N[" << attr.n << "],K[" << attr.k << "]"; return os; diff --git a/paddle/fluid/operators/jit/kernel_base.h b/paddle/fluid/operators/jit/kernel_base.h index 20b6a32bef9860c52ab4423395a8e00f719b0210..895e2d4d6f3809a66443ed6d6bfc1ee02d6c529a 100644 --- a/paddle/fluid/operators/jit/kernel_base.h +++ b/paddle/fluid/operators/jit/kernel_base.h @@ -46,6 +46,7 @@ typedef enum { kVMul, kVRelu, kVScal, + kSgd, kVSigmoid, kVSquare, kVSub, @@ -173,6 +174,28 @@ struct EmbSeqPoolTuples { const emb_seq_pool_attr_t*); }; +typedef struct sgd_attr_s { + int64_t param_height, param_width; + int64_t grad_height, grad_width; + int64_t selected_rows_size; + sgd_attr_s() = default; + explicit sgd_attr_s(int64_t param_h, int64_t param_w, int64_t grad_h, + int64_t grad_w, int64_t selected_rows_sz) + : param_height(param_h), + param_width(param_w), + grad_height(grad_h), + grad_width(grad_w), + selected_rows_size(selected_rows_sz) {} +} sgd_attr_t; + +template +struct SgdTuples { + typedef T data_type; + typedef sgd_attr_t attr_type; + typedef void (*func_type)(const T*, const T*, const T*, const int64_t*, T*, + const sgd_attr_t*); +}; + typedef struct matmul_attr_s { int m, n, k; void* packed_weight{nullptr}; diff --git a/paddle/fluid/operators/jit/kernel_key.cc b/paddle/fluid/operators/jit/kernel_key.cc index e659c6d254391f09ac8692e0b7602c65e1afd47d..740d0f850a072a5ad3238e52402141a83c0b7e33 100644 --- a/paddle/fluid/operators/jit/kernel_key.cc +++ b/paddle/fluid/operators/jit/kernel_key.cc @@ -13,6 +13,7 @@ * limitations under the License. */ #include "paddle/fluid/operators/jit/kernel_key.h" +#include "paddle/fluid/platform/enforce.h" namespace paddle { namespace operators { @@ -23,14 +24,30 @@ size_t JitCodeKey(const int& d) { return d; } +// TODO(TJ): refine and benchmark JitCodeKey generatation constexpr int act_type_shift = 3; // suppot 2^3 act types +static inline int act_type_convert(KernelType type) { + if (type == kVIdentity) { + return 0; + } else if (type == kVExp) { + return 1; + } else if (type == kVRelu) { + return 2; + } else if (type == kVSigmoid) { + return 3; + } else if (type == kVTanh) { + return 4; + } + PADDLE_THROW("Unsupported act type %d", type); + return 0; +} template <> size_t JitCodeKey(const lstm_attr_t& attr) { size_t key = attr.d; - int gate_key = static_cast(attr.act_gate) << 1; - int cand_key = static_cast(attr.act_cand) << (1 + act_type_shift); - int cell_key = static_cast(attr.act_cell) << (1 + act_type_shift * 2); + int gate_key = act_type_convert(attr.act_gate) << 1; + int cand_key = act_type_convert(attr.act_cand) << (1 + act_type_shift); + int cell_key = act_type_convert(attr.act_cell) << (1 + act_type_shift * 2); return (key << (1 + act_type_shift * 3)) + gate_key + cand_key + cell_key + attr.use_peephole; } @@ -38,8 +55,8 @@ size_t JitCodeKey(const lstm_attr_t& attr) { template <> size_t JitCodeKey(const gru_attr_t& attr) { size_t key = attr.d; - return (key << (act_type_shift * 2)) + static_cast(attr.act_gate) + - (static_cast(attr.act_cand) << act_type_shift); + return (key << (act_type_shift * 2)) + act_type_convert(attr.act_gate) + + (act_type_convert(attr.act_cand) << act_type_shift); } template <> @@ -61,6 +78,11 @@ size_t JitCodeKey(const emb_seq_pool_attr_t& attr) { return attr.table_width; } +template <> +size_t JitCodeKey(const sgd_attr_t& attr) { + return attr.grad_width; +} + } // namespace jit } // namespace operators } // namespace paddle diff --git a/paddle/fluid/operators/jit/more/mkl/CMakeLists.txt b/paddle/fluid/operators/jit/more/mkl/CMakeLists.txt index d209f31007255b3a90fdeeb4d609311b80bdc7b5..9a00ad56a6a909a677cb8f60bd80fe399e82952f 100644 --- a/paddle/fluid/operators/jit/more/mkl/CMakeLists.txt +++ b/paddle/fluid/operators/jit/more/mkl/CMakeLists.txt @@ -14,3 +14,4 @@ USE_JITKERNEL_MORE(kVTanh, mkl) USE_JITKERNEL_MORE(kSeqPool, mkl) USE_JITKERNEL_MORE(kSoftmax, mkl) USE_JITKERNEL_MORE(kEmbSeqPool, mkl) +USE_JITKERNEL_MORE(kSgd, mkl) diff --git a/paddle/fluid/operators/jit/more/mkl/mkl.cc b/paddle/fluid/operators/jit/more/mkl/mkl.cc index 29a451f832fa745f8e1f5a45fd934f09e1f41e76..780fda02c1ff3da2e0b945f9b2fece30484e4519 100644 --- a/paddle/fluid/operators/jit/more/mkl/mkl.cc +++ b/paddle/fluid/operators/jit/more/mkl/mkl.cc @@ -184,6 +184,16 @@ bool EmbSeqPoolKernel::UseMe(const emb_seq_pool_attr_t& attr) const { return true; } +template <> +bool SgdKernel::UseMe(const sgd_attr_t& attr) const { + return true; +} + +template <> +bool SgdKernel::UseMe(const sgd_attr_t& attr) const { + return true; +} + template <> bool MatMulKernel::UseMe(const matmul_attr_t& attr) const { return platform::MayIUse(platform::avx); @@ -239,5 +249,6 @@ REGISTER_MKL_KERNEL(kVTanh, VTanh); REGISTER_MKL_KERNEL(kSeqPool, SeqPool); REGISTER_MKL_KERNEL(kEmbSeqPool, EmbSeqPool); REGISTER_MKL_KERNEL(kSoftmax, Softmax); +REGISTER_MKL_KERNEL(kSgd, Sgd); #undef REGISTER_MKL_KERNEL diff --git a/paddle/fluid/operators/jit/more/mkl/mkl.h b/paddle/fluid/operators/jit/more/mkl/mkl.h index 9a72ba83022de2beeb760772ee8489477befdd7e..a7bc2de4a3e8e7d8e2a6b00990bfa459b3029c2a 100644 --- a/paddle/fluid/operators/jit/more/mkl/mkl.h +++ b/paddle/fluid/operators/jit/more/mkl/mkl.h @@ -142,6 +142,32 @@ void Softmax(const T* x, T* y, int n, int bs) { } } +template +void Sgd(const T* lr, const T* param, const T* grad, const int64_t* rows, + T* out, const sgd_attr_t* attr) { + PADDLE_ENFORCE_EQ(attr->param_width, attr->grad_width); + PADDLE_ENFORCE_LE(attr->selected_rows_size, attr->grad_height); + T scalar = -lr[0]; + int width = attr->grad_width; + if (out == param) { + for (int64_t i = 0; i < attr->selected_rows_size; ++i) { + auto h_idx = rows[i]; + PADDLE_ENFORCE_LT(h_idx, attr->param_height); + PADDLE_ENFORCE_GE(h_idx, 0); + VAXPY(scalar, grad + i * width, out + h_idx * width, width); + } + } else { + for (int64_t i = 0; i < attr->selected_rows_size; ++i) { + auto h_idx = rows[i]; + PADDLE_ENFORCE_LT(h_idx, attr->param_height); + PADDLE_ENFORCE_GE(h_idx, 0); + VScal(&scalar, grad + i * width, out + h_idx * width, width); + VAdd(param + h_idx * width, out + h_idx * width, out + h_idx * width, + width); + } + } +} + #define DECLARE_MKL_KERNEL(name, tuples) \ template \ class name##Kernel : public KernelMore> { \ @@ -173,6 +199,8 @@ DECLARE_MKL_KERNEL(EmbSeqPool, EmbSeqPoolTuples); DECLARE_MKL_KERNEL(Softmax, SoftmaxTuples); +DECLARE_MKL_KERNEL(Sgd, SgdTuples); + #undef DECLARE_MKL_KERNEL } // namespace mkl diff --git a/paddle/fluid/operators/jit/refer/CMakeLists.txt b/paddle/fluid/operators/jit/refer/CMakeLists.txt index 218d801c084be455538628d1c1028d8e52142894..cd19dd169d0bfdfe2cb8157ade29f48ad6428453 100644 --- a/paddle/fluid/operators/jit/refer/CMakeLists.txt +++ b/paddle/fluid/operators/jit/refer/CMakeLists.txt @@ -33,3 +33,4 @@ USE_JITKERNEL_REFER(kHSum) USE_JITKERNEL_REFER(kHMax) USE_JITKERNEL_REFER(kSoftmax) USE_JITKERNEL_REFER(kEmbSeqPool) +USE_JITKERNEL_REFER(kSgd) diff --git a/paddle/fluid/operators/jit/refer/refer.cc b/paddle/fluid/operators/jit/refer/refer.cc index 7e7dd6960b66e4e2f77eca6e96604f2a86553120..0c434bd2b8cacdf4b8872da66bb8e763a6a45cee 100644 --- a/paddle/fluid/operators/jit/refer/refer.cc +++ b/paddle/fluid/operators/jit/refer/refer.cc @@ -59,4 +59,6 @@ REGISTER_REFER_KERNEL(kSoftmax, Softmax); REGISTER_REFER_KERNEL(kEmbSeqPool, EmbSeqPool); +REGISTER_REFER_KERNEL(kSgd, Sgd); + #undef REGISTER_REFER_KERNEL diff --git a/paddle/fluid/operators/jit/refer/refer.h b/paddle/fluid/operators/jit/refer/refer.h index fd1193aa41e50e3ede7f61588dc72389279bb95d..0f714edf85bbbf4838bfe09251bd1c2d5f3b3eb7 100644 --- a/paddle/fluid/operators/jit/refer/refer.h +++ b/paddle/fluid/operators/jit/refer/refer.h @@ -446,6 +446,36 @@ void EmbSeqPool(const T* table, const int64_t* idx, T* out, } } +// SGD algorithm: +// lr is pointor of learning rate scalar +// param is an input matrix with (param_h, param_w) +// grad is an input matrix with (grad_h, grad_w), here grad_w == param_w +// selected_rows is a vectot with size selected_rows_size( <= grad_h ) +// out is an output matrix with (param_h, param_w) +// +// support both regular and sparse grad +// regular SGD: out[:] = param[:] - lr[0] * grad[:]; +// sparse SGD: out[rows[i]][:] = param[rows[i]][:] - lr[0] * grad[i][:] +// +// Note: when use sparse SGD, and if out != param, +// the out rows which are not selected have not beed changed, which maybe empty +template +void Sgd(const T* lr, const T* param, const T* grad, const int64_t* rows, + T* out, const sgd_attr_t* attr) { + PADDLE_ENFORCE_EQ(attr->param_width, attr->grad_width); + PADDLE_ENFORCE_LE(attr->selected_rows_size, attr->grad_height); + for (int64_t i = 0; i < attr->selected_rows_size; ++i) { + auto h_idx = rows[i]; + PADDLE_ENFORCE_LT(h_idx, attr->param_height); + PADDLE_ENFORCE_GE(h_idx, 0); + for (int64_t j = 0; j < attr->grad_width; ++j) { + out[h_idx * attr->grad_width + j] = + param[h_idx * attr->grad_width + j] - + lr[0] * grad[i * attr->grad_width + j]; + } + } +} + #define DECLARE_REFER_KERNEL(name, tuples) \ template \ class name##Kernel : public ReferKernel> { \ @@ -496,6 +526,8 @@ DECLARE_REFER_KERNEL(Softmax, SoftmaxTuples); DECLARE_REFER_KERNEL(EmbSeqPool, EmbSeqPoolTuples); +DECLARE_REFER_KERNEL(Sgd, SgdTuples); + #undef DECLARE_REFER_KERNEL } // namespace refer diff --git a/paddle/fluid/operators/jit/test.cc b/paddle/fluid/operators/jit/test.cc index 356eba6f86ad180c7d23bf7fa91eb5d455ff5f08..b618cd6a84be752a052f9d49a4a4c772b1d7eeae 100644 --- a/paddle/fluid/operators/jit/test.cc +++ b/paddle/fluid/operators/jit/test.cc @@ -12,6 +12,7 @@ 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 @@ -36,14 +37,14 @@ void RandomVec(const int n, T* a, const T lower = static_cast(-20.f), } template -void ExpectEQ(const T* target, const T* refer, int n) { +void ExpectEQ(const T* target, const T* refer, size_t n) { if (std::is_floating_point::value) { - for (int i = 0; i < n; ++i) { - EXPECT_NEAR(target[i], refer[i], FLAGS_acc); + for (size_t i = 0; i < n; ++i) { + EXPECT_NEAR(target[i], refer[i], FLAGS_acc) << " at index : " << i; } } else { - for (int i = 0; i < n; ++i) { - EXPECT_EQ(target[i], refer[i]); + for (size_t i = 0; i < n; ++i) { + EXPECT_EQ(target[i], refer[i]) << " at index : " << i; } } } @@ -296,6 +297,45 @@ struct TestFuncWithRefer, std::vector, } }; +template +struct TestFuncWithRefer, T, std::vector, std::vector, + std::vector, std::vector, + typename jit::SgdTuples::attr_type> { + void operator()(const typename jit::SgdTuples::func_type tgt, const T lr, + const std::vector& param, const std::vector& grad, + const std::vector& rows, const std::vector& oref, + const typename jit::SgdTuples::attr_type& attr) { + EXPECT_TRUE(tgt != nullptr); + EXPECT_EQ(param.size(), + static_cast(attr.param_height * attr.param_width)); + EXPECT_EQ(grad.size(), + static_cast(attr.grad_height * attr.grad_width)); + EXPECT_EQ(rows.size(), static_cast(attr.selected_rows_size)); + EXPECT_EQ(param.size(), oref.size()); + const T* param_data = param.data(); + const T* grad_data = grad.data(); + const int64_t* rows_data = rows.data(); + const T* oref_data = oref.data(); + + std::vector out(oref.size()); + T* o_data = out.data(); + tgt(&lr, param_data, grad_data, rows_data, o_data, &attr); + // only the selected rows should be equal + for (size_t i = 0; i < rows.size(); ++i) { + ExpectEQ(o_data + rows[i] * attr.grad_width, + oref_data + rows[i] * attr.grad_width, attr.grad_width); + } + + // inplace + std::copy(param.begin(), param.end(), out.begin()); + tgt(&lr, o_data, grad_data, rows_data, o_data, &attr); + for (size_t i = 0; i < rows.size(); ++i) { + ExpectEQ(o_data + rows[i] * attr.grad_width, + oref_data + rows[i] * attr.grad_width, attr.grad_width); + } + } +}; + template struct TestFuncWithRefer, std::vector, std::vector, std::vector, @@ -407,7 +447,7 @@ void TestAllImpls(const typename KernelTuples::attr_type& attr, Args... args) { } template -void TestXYZNKernel() { +void TestKernelXYZNTuples() { VLOG(10) << "===== Test JITKernel " << jit::to_string(KT); for (int d : TestSizes()) { auto ref = jit::GetRefer>(); @@ -440,7 +480,7 @@ void TestXYZNKernel() { } template -void TestAXYNKernel() { +void TestKernelAXYNTuples() { VLOG(10) << "===== Test JITKernel " << jit::to_string(KT); for (int d : TestSizes()) { auto ref = jit::GetRefer>(); @@ -466,7 +506,7 @@ void TestAXYNKernel() { } template -void TestXRNKernel() { +void TestKernelXRNTuples() { VLOG(10) << "===== Test JITKernel " << jit::to_string(KT); auto last_acc = FLAGS_acc; FLAGS_acc = 1e-4; @@ -484,7 +524,7 @@ void TestXRNKernel() { } template -void TestXYNKernel() { +void TestKernelXYNTuples() { VLOG(10) << "===== Test JITKernel " << jit::to_string(KT); for (int d : TestSizes()) { auto ref = jit::GetRefer>(); @@ -509,10 +549,12 @@ void TestXYNKernel() { } template -void TestLSTMKernel() { +void TestKernelLSTMTuples() { VLOG(10) << "===== Test JITKernel " << jit::to_string(KT); std::vector all_acts = {"sigmoid", "tanh", "relu", "identity"}; - for (int d : TestSizes()) { + auto test_sizes = TestSizes(); + test_sizes.erase(std::remove(test_sizes.begin(), test_sizes.end(), 1000)); + for (int d : test_sizes) { for (bool use_peephole : {true, false}) { for (auto& act_gate : all_acts) { for (auto& act_cand : all_acts) { @@ -559,10 +601,12 @@ void TestLSTMKernel() { } template -void TestGRUKernel() { +void TestKernelGRUTuples() { VLOG(10) << "===== Test JITKernel " << jit::to_string(KT); std::vector all_acts = {"sigmoid", "tanh", "relu", "identity"}; - for (int d : TestSizes()) { + auto test_sizes = TestSizes(); + test_sizes.erase(std::remove(test_sizes.begin(), test_sizes.end(), 1000)); + for (int d : test_sizes) { for (auto& act_gate : all_acts) { for (auto& act_cand : all_acts) { const jit::gru_attr_t attr(d, jit::to_kerneltype(act_gate), @@ -593,14 +637,16 @@ void TestGRUKernel() { } template -void TestSeqPoolKernel() { +void TestKernelSeqPoolTuples() { VLOG(10) << "===== Test JITKernel " << jit::to_string(KT); std::vector pool_types = { jit::SeqPoolType::kSum, jit::SeqPoolType::kAvg, jit::SeqPoolType::kSqrt}; + auto test_sizes = TestSizes(); + test_sizes.erase(std::remove(test_sizes.begin(), test_sizes.end(), 1000)); for (auto type : pool_types) { - for (int w : TestSizes()) { + for (int w : test_sizes) { jit::seq_pool_attr_t attr(w, type); - for (int h : TestSizes()) { + for (int h : test_sizes) { attr.h = h; auto ref = jit::GetRefer>(); EXPECT_TRUE(ref != nullptr); @@ -618,11 +664,11 @@ void TestSeqPoolKernel() { } template -void TestMatMulKernel() { +void TestKernelMatMulTuples() { VLOG(10) << "===== Test JITKernel " << jit::to_string(KT); auto last_acc = FLAGS_acc; - // TODO(intel): fix MKL acc issue - // https://github.com/PaddlePaddle/Paddle/issues/15447 + // export MKL_CBWR=AVX would make MKL force to use AVX + // export KMP_DETERMINISTIC_REDUCTION=yes would make the result deterministic FLAGS_acc = 1e-3; for (int m : {1, 2, 3, 4}) { for (int n : {1, 2, 3, 4}) { @@ -646,7 +692,7 @@ void TestMatMulKernel() { } template -void TestSoftmaxKernel() { +void TestKernelSoftmaxTuples() { VLOG(10) << "===== Test JITKernel " << jit::to_string(KT); for (int bs : {1, 2, 10}) { for (int n : TestSizes()) { @@ -671,12 +717,14 @@ void TestSoftmaxKernel() { } template -void TestEmbSeqPoolKernel() { +void TestKernelEmbSeqPoolTuples() { VLOG(10) << "===== Test JITKernel " << jit::to_string(KT); int64_t tbl_h = 1e4; std::vector pool_types = { jit::SeqPoolType::kSum}; // only support sum yet - for (int tbl_w : TestSizes()) { + auto test_sizes = TestSizes(); + test_sizes.erase(std::remove(test_sizes.begin(), test_sizes.end(), 1000)); + for (int tbl_w : test_sizes) { std::vector table(tbl_h * tbl_w); RandomVec(tbl_h * tbl_w, table.data(), -2.f, 2.f); const T* table_data = table.data(); @@ -705,7 +753,61 @@ void TestEmbSeqPoolKernel() { } template -void TestNCHW16CMulNCKernel() { +void TestKernelSgdTuples() { + VLOG(10) << "===== Test JITKernel " << jit::to_string(KT); + const T lr = 0.1; + auto UnDuplicatedRandomVec = [](int n, const int64_t lower, + const int64_t upper) -> std::vector { + PADDLE_ENFORCE_LE(static_cast(upper - lower), n - 1); + PADDLE_ENFORCE_GT(n, 0); + std::vector all, out; + for (int i = 0; i < n; ++i) { + all.push_back(i); + } + std::random_shuffle(all.begin(), all.end()); + out.insert(out.begin(), all.begin(), all.begin() + n); + return out; + }; + for (int param_h : {1, 10}) { + for (int grad_w : TestSizes()) { + std::vector param(param_h * grad_w); + std::vector param_out(param_h * grad_w); + RandomVec(param_h * grad_w, param.data(), -2.f, 2.f); + const T* param_data = param.data(); + T* out_data = param_out.data(); + for (int rows_size = 1; rows_size <= param_h; ++rows_size) { + std::vector grad(rows_size * grad_w); + std::vector rows = + UnDuplicatedRandomVec(rows_size, 0, rows_size - 1); + RandomVec(rows_size * grad_w, grad.data(), -2.f, 2.f); + const int64_t* rows_data = rows.data(); + const T* grad_data = grad.data(); + auto ref = jit::GetRefer>(); + EXPECT_TRUE(ref != nullptr); + jit::sgd_attr_t attr(param_h, grad_w, rows_size, grad_w, rows_size); + ref(&lr, param_data, grad_data, rows_data, out_data, &attr); + + // inplace test + std::vector inp(param.size()); + std::copy(param.begin(), param.end(), inp.begin()); + T* inp_data = inp.data(); + ref(&lr, inp_data, grad_data, rows_data, inp_data, &attr); + // only the selected rows should be equal + for (int i = 0; i < rows_size; ++i) { + ExpectEQ(inp_data + rows[i] * grad_w, out_data + rows[i] * grad_w, + grad_w); + } + + TestAllImpls, PlaceType, T, std::vector, + std::vector, std::vector, std::vector>( + attr, lr, param, grad, rows, param_out, attr); + } + } + } +} + +template +void TestKernelNCHW16CMulNCTuples() { VLOG(10) << "===== Test JITKernel " << jit::to_string(KT); const int n = 3, c = 16 * 4, h = 10, w = 10; auto ref = jit::GetRefer>(); @@ -758,7 +860,7 @@ void TestNCHW16CMulNCKernel() { } template -void TestLayerNormKernel() { +void TestKernelLayerNormTuples() { VLOG(10) << "===== Test JITKernel " << jit::to_string(KT); const T epsilon = 9.99999975e-06; for (int n : {1, 2, 10}) { @@ -797,11 +899,13 @@ void TestLayerNormKernel() { } template -void TestCRFDecodingKernel() { +void TestKernelCRFDecodingTuples() { VLOG(10) << "===== Test JITKernel " << jit::to_string(KT); constexpr int state_trans_base_idx = 2; + auto test_sizes = TestSizes(); + test_sizes.erase(std::remove(test_sizes.begin(), test_sizes.end(), 1000)); for (int seq_len : {1, 11, 17, 50}) { - for (int tag_num : TestSizes()) { + for (int tag_num : test_sizes) { auto ref = jit::GetRefer>(); EXPECT_TRUE(ref != nullptr); int x_sz = seq_len * tag_num; @@ -822,143 +926,76 @@ void TestCRFDecodingKernel() { } } -// XYZNTuple -TEST(JITKernel, kVMul) { - TestXYZNKernel(); - TestXYZNKernel(); -} - -TEST(JITKernel, kVAdd) { - TestXYZNKernel(); - TestXYZNKernel(); -} - -TEST(JITKernel, kVAddRelu) { - TestXYZNKernel(); - TestXYZNKernel(); -} - -TEST(JITKernel, kVSub) { - TestXYZNKernel(); - TestXYZNKernel(); -} - -// AXYNTuples -TEST(JITKernel, kVScal) { - TestAXYNKernel(); - TestAXYNKernel(); -} - -TEST(JITKernel, kVAddBias) { - TestAXYNKernel(); - TestAXYNKernel(); -} - -// XRNTuples -TEST(JITKernel, kHMax) { - TestXRNKernel(); - TestXRNKernel(); -} - -TEST(JITKernel, kHSum) { - TestXRNKernel(); - TestXRNKernel(); -} - -// XYNTuples -TEST(JITKernel, kVRelu) { - TestXYNKernel(); - TestXYNKernel(); -} - -TEST(JITKernel, kVIdentity) { - TestXYNKernel(); - TestXYNKernel(); -} - -TEST(JITKernel, kVSquare) { - TestXYNKernel(); - TestXYNKernel(); -} - -TEST(JITKernel, kVExp) { - TestXYNKernel(); - TestXYNKernel(); -} - -TEST(JITKernel, kVSigmoid) { - TestXYNKernel(); - TestXYNKernel(); -} +#define TEST_CPU_KERNEL(test_tuple, kernel_type) \ + TEST(JITKernel, kernel_type) { \ + TestKernel##test_tuple(); \ + TestKernel##test_tuple(); \ + } -TEST(JITKernel, kVTanh) { - TestXYNKernel(); - TestXYNKernel(); -} +TEST_CPU_KERNEL(XYZNTuples, kVMul); +TEST_CPU_KERNEL(XYZNTuples, kVAdd); +TEST_CPU_KERNEL(XYZNTuples, kVAddRelu); +TEST_CPU_KERNEL(XYZNTuples, kVSub); -// LSTM -TEST(JITKernel, kLSTMCtHt) { - TestLSTMKernel(); - TestLSTMKernel(); -} +TEST_CPU_KERNEL(AXYNTuples, kVScal); +TEST_CPU_KERNEL(AXYNTuples, kVAddBias); -TEST(JITKernel, kLSTMC1H1) { - TestLSTMKernel(); - TestLSTMKernel(); -} +TEST_CPU_KERNEL(XRNTuples, kHMax); +TEST_CPU_KERNEL(XRNTuples, kHSum); -// GRU -TEST(JITKernel, kGRUH1) { - TestGRUKernel(); - TestGRUKernel(); -} +TEST_CPU_KERNEL(XYNTuples, kVRelu); +TEST_CPU_KERNEL(XYNTuples, kVIdentity); +TEST_CPU_KERNEL(XYNTuples, kVSquare); +TEST_CPU_KERNEL(XYNTuples, kVExp); +TEST_CPU_KERNEL(XYNTuples, kVSigmoid); +TEST_CPU_KERNEL(XYNTuples, kVTanh); -TEST(JITKernel, kGRUHtPart1) { - TestGRUKernel(); - TestGRUKernel(); -} +TEST_CPU_KERNEL(LSTMTuples, kLSTMCtHt); +TEST_CPU_KERNEL(LSTMTuples, kLSTMC1H1); -TEST(JITKernel, kGRUHtPart2) { - TestGRUKernel(); - TestGRUKernel(); -} +TEST_CPU_KERNEL(GRUTuples, kGRUH1); +TEST_CPU_KERNEL(GRUTuples, kGRUHtPart1); +TEST_CPU_KERNEL(GRUTuples, kGRUHtPart2); -TEST(JITKernel, kSeqPool) { - TestSeqPoolKernel(); - TestSeqPoolKernel(); -} +TEST_CPU_KERNEL(NCHW16CMulNCTuples, kNCHW16CMulNC); -TEST(JITKernel, kMatMul) { - TestMatMulKernel(); - TestMatMulKernel(); -} +TEST_CPU_KERNEL(SeqPoolTuples, kSeqPool); +TEST_CPU_KERNEL(MatMulTuples, kMatMul); +TEST_CPU_KERNEL(SoftmaxTuples, kSoftmax); +TEST_CPU_KERNEL(EmbSeqPoolTuples, kEmbSeqPool); +TEST_CPU_KERNEL(SgdTuples, kSgd); +TEST_CPU_KERNEL(LayerNormTuples, kLayerNorm); +TEST_CPU_KERNEL(CRFDecodingTuples, kCRFDecoding); -TEST(JITKernel, kSoftmax) { - TestSoftmaxKernel(); - TestSoftmaxKernel(); -} +TEST(JITKernel_key, lstm) { + jit::lstm_attr_t attr1(8, jit::kVIdentity, jit::kVSigmoid, jit::kVTanh); + jit::lstm_attr_t attr2(9, jit::kVIdentity, jit::kVSigmoid, jit::kVTanh); + jit::lstm_attr_t attr3(9, jit::kVIdentity, jit::kVSigmoid, jit::kVTanh); + jit::lstm_attr_t attr4(9, jit::kVRelu, jit::kVSigmoid, jit::kVTanh); -TEST(JITKernel, kEmbSeqPool) { - TestEmbSeqPoolKernel(); - TestEmbSeqPoolKernel(); -} + auto key1 = jit::JitCodeKey(attr1); + auto key2 = jit::JitCodeKey(attr2); + auto key3 = jit::JitCodeKey(attr3); + auto key4 = jit::JitCodeKey(attr4); -TEST(JITKernel, kNCHW16CMulNC) { - TestNCHW16CMulNCKernel(); - TestNCHW16CMulNCKernel(); + EXPECT_TRUE(key1 != key2); + EXPECT_TRUE(key2 == key3); + EXPECT_TRUE(key3 != key4); } -TEST(JITKernel, kLayerNorm) { - TestLayerNormKernel(); - TestLayerNormKernel(); -} +TEST(JITKernel_key, gru) { + jit::gru_attr_t attr1(8, jit::kVSigmoid, jit::kVTanh); + jit::gru_attr_t attr2(9, jit::kVSigmoid, jit::kVTanh); + jit::gru_attr_t attr3(9, jit::kVSigmoid, jit::kVTanh); + jit::gru_attr_t attr4(9, jit::kVSigmoid, jit::kVIdentity); -TEST(JITKernel, kCRFDecoding) { - TestCRFDecodingKernel(); - TestCRFDecodingKernel(); -} + auto key1 = jit::JitCodeKey(attr1); + auto key2 = jit::JitCodeKey(attr2); + auto key3 = jit::JitCodeKey(attr3); + auto key4 = jit::JitCodeKey(attr4); -TEST(JITKernel, pool) { - // TODO(TJ): add some test + EXPECT_TRUE(key1 != key2); + EXPECT_TRUE(key2 == key3); + EXPECT_TRUE(key3 != key4); } +// TODO(TJ): add more test about key and pool diff --git a/paddle/fluid/operators/math/beam_search.cu b/paddle/fluid/operators/math/beam_search.cu index 61d021ef627f1ccd90b992c2078a7f3ca879422d..d66778a6fe05c0460c805581ee6ffd6d5e9d746e 100644 --- a/paddle/fluid/operators/math/beam_search.cu +++ b/paddle/fluid/operators/math/beam_search.cu @@ -119,6 +119,18 @@ __device__ __forceinline__ int SelectTopBeam( __syncthreads(); } + if ((num_used_threads & 0x1) != 0) { + // If num_used_threads is a odd number, merge local top_beam of thread 0 + // and num_used_threads - 1 + if (tid_of_seq == 0) { + int index_in_sh = (num_used_threads - 1 + tid) * beam_size; + for (int i = 0; i < beam_size; i++) { + Insert(top_beam_local, top_beam[index_in_sh], beam_size); + index_in_sh++; + } + } + } + num_used_threads = num_used_threads >> 1; if (tid_of_seq < num_used_threads) { int index_in_sh = (num_used_threads + tid) * beam_size; diff --git a/paddle/fluid/operators/math/blas.h b/paddle/fluid/operators/math/blas.h index f67f57827bc03e134bf87edd5bf033adb5098916..ce8109f64d62b0d412419107881952f1b4ffc75e 100644 --- a/paddle/fluid/operators/math/blas.h +++ b/paddle/fluid/operators/math/blas.h @@ -184,6 +184,9 @@ class Blas { template void VINV(int n, const T* a, T* y) const; + template + void VMERF(int n, const T* a, T* y, int64_t mode) const; + private: const DeviceContext& context_; }; @@ -290,6 +293,11 @@ class BlasT : private Blas { Base()->template VINV(args...); } + template + void VMERF(ARGS... args) const { + Base()->template VMERF(args...); + } + private: const Blas* Base() const { return static_cast*>(this); diff --git a/paddle/fluid/operators/math/blas_impl.h b/paddle/fluid/operators/math/blas_impl.h index 972366bc093f4b7f0a090cf31213f75ccd89fd82..ba995dabecbfab8c4952bb7efeaa381f8078821a 100644 --- a/paddle/fluid/operators/math/blas_impl.h +++ b/paddle/fluid/operators/math/blas_impl.h @@ -123,6 +123,11 @@ struct CBlas { static void VINV(ARGS... args) { platform::dynload::vsInv(args...); } + + template + static void VMERF(ARGS... args) { + platform::dynload::vmsErf(args...); + } }; template <> @@ -223,6 +228,11 @@ struct CBlas { static void VINV(ARGS... args) { platform::dynload::vdInv(args...); } + + template + static void VMERF(ARGS... args) { + platform::dynload::vmdErf(args...); + } }; #else @@ -625,6 +635,19 @@ void Blas::VINV(int n, const T *a, T *y) const { #endif } +template <> +template +void Blas::VMERF(int n, const T *a, T *y, + int64_t mode) const { +#ifdef PADDLE_WITH_MKLML + CBlas::VMERF(n, a, y, mode); +#else + for (int i = 0; i < n; ++i) { + y[i] = std::erf(a[i]); + } +#endif +} + } // namespace math } // namespace operators } // namespace paddle diff --git a/paddle/fluid/operators/mkldnn/activation_mkldnn_op.cc b/paddle/fluid/operators/mkldnn/activation_mkldnn_op.cc index 5b7505f3c4acdef94fead04efd00b47825274117..43559940d925e6fff29f0c5c66ec1a3dc717aaf4 100644 --- a/paddle/fluid/operators/mkldnn/activation_mkldnn_op.cc +++ b/paddle/fluid/operators/mkldnn/activation_mkldnn_op.cc @@ -96,8 +96,7 @@ void eltwise_forward(const framework::ExecutionContext &ctx, std::vector src_tz = framework::vectorize2int(x->dims()); - auto src_format = - src_tz.size() == 2 ? mkldnn::memory::format::nc : x->format(); + auto src_format = x->format(); const std::string key = gethash(src_tz, algorithm); const std::string key_src_data = @@ -127,10 +126,8 @@ void eltwise_forward(const framework::ExecutionContext &ctx, if (p_fwd == nullptr) { // create mkldnn memory for input X - auto src_md = platform::MKLDNNMemDesc( - src_tz, platform::MKLDNNGetDataType(), src_format); auto src_memory = std::shared_ptr( - new memory({src_md, mkldnn_engine}, to_void_cast(x_data))); + new memory(x->get_mkldnn_prim_desc(), to_void_cast(x_data))); // save src_memory to be referred in backward path dev_ctx.SetBlob(key_src_mem, src_memory); @@ -177,8 +174,7 @@ void eltwise_forward(const framework::ExecutionContext &ctx, pipeline.push_back(*p_fwd); stream(stream::kind::eager).submit(pipeline).wait(); - y->set_layout(DataLayout::kMKLDNN); - y->set_format(GetMKLDNNFormat(*dst_memory)); + y->set_mkldnn_prim_desc(dst_memory->get_primitive_desc()); } template @@ -196,9 +192,6 @@ void eltwise_grad(const framework::ExecutionContext &ctx, std::vector diff_dst_tz = framework::vectorize2int(diff_y->dims()); - auto diff_y_format = - diff_dst_tz.size() == 2 ? mkldnn::memory::format::nc : diff_y->format(); - const std::string key = gethash(diff_dst_tz, algorithm); const std::string key_src_data = key + ctx.op().Input("Out") + "@eltwise_fwd_src_data"; @@ -210,8 +203,8 @@ void eltwise_grad(const framework::ExecutionContext &ctx, key + std::to_string(*p_src_layout) + "@eltwise_fwd_src_mem"; const std::string key_fwd_pd = key + std::to_string(*p_src_layout) + "@eltwise_fwd_pd"; - const std::string key_with_layouts = - key + std::to_string(*p_src_layout) + "-" + std::to_string(diff_y_format); + const std::string key_with_layouts = key + std::to_string(*p_src_layout) + + "-" + std::to_string(diff_y->format()); const std::string key_diff_src_mem = key_with_layouts + "@eltwise_diff_src_mem"; const std::string key_diff_dst_mem = @@ -234,10 +227,8 @@ void eltwise_grad(const framework::ExecutionContext &ctx, if (p_grad == nullptr) { // create mkldnn memory for input diff_y - auto diff_dst_md = platform::MKLDNNMemDesc( - diff_dst_tz, platform::MKLDNNGetDataType(), diff_y_format); auto diff_dst_memory = std::shared_ptr( - new memory({diff_dst_md, mkldnn_engine}, to_void_cast(diff_y_data))); + new memory(diff_y->get_mkldnn_prim_desc(), to_void_cast(diff_y_data))); dev_ctx.SetBlob(key_diff_dst_mem, diff_dst_memory); // retrieve eltwise primitive desc from device context @@ -281,8 +272,7 @@ void eltwise_grad(const framework::ExecutionContext &ctx, pipeline.push_back(*p_grad); stream(stream::kind::eager).submit(pipeline).wait(); - diff_x->set_layout(DataLayout::kMKLDNN); - diff_x->set_format(GetMKLDNNFormat(*diff_src_memory)); + diff_x->set_mkldnn_prim_desc(diff_src_memory->get_primitive_desc()); } template diff --git a/paddle/fluid/operators/mkldnn/batch_norm_mkldnn_op.cc b/paddle/fluid/operators/mkldnn/batch_norm_mkldnn_op.cc index bddca232e6c8a2a7fde998877006e37ee6d3d0dc..04e45d4853907bb7d6b5ce362892a2183fd4b60e 100644 --- a/paddle/fluid/operators/mkldnn/batch_norm_mkldnn_op.cc +++ b/paddle/fluid/operators/mkldnn/batch_norm_mkldnn_op.cc @@ -206,17 +206,14 @@ class BatchNormMKLDNNOpKernel : public paddle::framework::OpKernel { if (fuse_with_relu) flags |= mkldnn::fuse_bn_relu; // create mkldnn memory from input x tensor - mkldnn::memory::format input_format = - platform::MKLDNNFormatForSize(src_tz.size(), x->format()); // keys for backward pass const std::string key = BatchNormMKLDNNHandler::GetHash( - src_tz, epsilon, flags, global_stats, input_format, + src_tz, epsilon, flags, global_stats, x->format(), ctx.op().Output("SavedMean")); const std::string key_batch_norm_fwd_pd = key + "@bn_fwd_pd"; - auto user_src_md = platform::MKLDNNMemDesc( - {src_tz}, platform::MKLDNNGetDataType(), input_format); + auto user_src_md = x->get_mkldnn_prim_desc().desc(); // create primitive descriptor for batch norm forward using bn_fwd_types = bn_type_traits; @@ -230,8 +227,8 @@ class BatchNormMKLDNNOpKernel : public paddle::framework::OpKernel { BatchNormMKLDNNHandler handler(batch_norm_fwd_pd, dev_ctx, mkldnn_engine, key); - auto src_memory = - handler.AcquireSrcMemory(user_src_md, to_void_cast(x_data)); + auto src_memory = handler.AcquireSrcMemory(x->get_mkldnn_prim_desc(), + to_void_cast(x_data)); // crate mkldnn memory for weights(scale/shift) auto scaleshift_memory = @@ -265,8 +262,7 @@ class BatchNormMKLDNNOpKernel : public paddle::framework::OpKernel { variance_memory, false); } - y->set_layout(DataLayout::kMKLDNN); - y->set_format(platform::GetMKLDNNFormat(*dst_memory)); + y->set_mkldnn_prim_desc(dst_memory->get_primitive_desc()); std::vector pipeline; pipeline.push_back(*batch_norm_p); @@ -336,9 +332,6 @@ class BatchNormMKLDNNGradOpKernel : public paddle::framework::OpKernel { using bn_bwd_types = bn_type_traits; - mkldnn::memory::format dst_format = - platform::MKLDNNFormatForSize(src_tz.size(), diff_y->format()); - mkldnn::memory::format input_format = platform::MKLDNNFormatForSize(src_tz.size(), x->format()); @@ -346,14 +339,14 @@ class BatchNormMKLDNNGradOpKernel : public paddle::framework::OpKernel { // keys from forward pass const std::string key = BatchNormMKLDNNHandler::GetHash( - src_tz, epsilon, flags, false, input_format, + src_tz, epsilon, flags, false, x->format(), ctx.op().Input("SavedMean")); const std::string key_batch_norm_fwd_pd = key + "@bn_fwd_pd"; // keys for primitives reuse const std::string key_with_hash = key + BatchNormMKLDNNHandler::GetHash(src_tz, epsilon, flags, false, - input_format); + x->format()); const std::string key_batch_norm_bwd_p = key_with_hash + "@batch_norm_bwd_p"; const std::string key_batch_norm_src_mem_p = @@ -373,9 +366,8 @@ class BatchNormMKLDNNGradOpKernel : public paddle::framework::OpKernel { primitive reorder_diff_dst; bool is_diff_dst_reordered = false; - auto user_diff_dst_memory = memory( - {{{diff_dst_tz}, memory::data_type::f32, dst_format}, mkldnn_engine}, - to_void_cast(diff_y_data)); + auto user_diff_dst_memory = + memory(diff_y->get_mkldnn_prim_desc(), to_void_cast(diff_y_data)); // MKLDNN requires a single piece of memory for scale and shift/bias data const size_t scaleshift_size = 2 * ic; @@ -459,10 +451,7 @@ class BatchNormMKLDNNGradOpKernel : public paddle::framework::OpKernel { dev_ctx.SetBlob(key_batch_norm_diff_dst_mem_p, diff_dst_memory); // set layout/format of output tensors - diff_x->set_layout(DataLayout::kMKLDNN); - diff_x->set_format((memory::format)diff_src_memory->get_primitive_desc() - .desc() - .data.format); + diff_x->set_mkldnn_prim_desc(diff_src_memory->get_primitive_desc()); } else { // primitives already exist UpdateMemoryData(dev_ctx, key_batch_norm_src_mem_p, to_void_cast(x_data)); @@ -487,10 +476,7 @@ class BatchNormMKLDNNGradOpKernel : public paddle::framework::OpKernel { } // set layout/format of output tensors - diff_x->set_layout(DataLayout::kMKLDNN); - diff_x->set_format((memory::format)diff_src_memory->get_primitive_desc() - .desc() - .data.format); + diff_x->set_mkldnn_prim_desc(diff_src_memory->get_primitive_desc()); } // execute optional reorder and batch_norm backward primitive diff --git a/paddle/fluid/operators/mkldnn/concat_mkldnn_op.cc b/paddle/fluid/operators/mkldnn/concat_mkldnn_op.cc index 7ad674056f0d753d79408a11eff1aca47a84998a..54c6a71111a2cc2f9e5004922ae5d3541a9d0a70 100644 --- a/paddle/fluid/operators/mkldnn/concat_mkldnn_op.cc +++ b/paddle/fluid/operators/mkldnn/concat_mkldnn_op.cc @@ -47,11 +47,6 @@ static memory::primitive_desc CreateMemPrimDesc(const Tensor& input, return mem_prim_desc; } -static mkldnn::memory::format GetDstMemFormat( - const concat::primitive_desc& concat_pd) { - return (memory::format)concat_pd.dst_primitive_desc().desc().data.format; -} - static platform::CPUPlace GetCpuPlace( const paddle::framework::ExecutionContext& ctx) { auto place = ctx.GetPlace(); @@ -139,8 +134,7 @@ class ConcatMKLDNNOpKernel : public paddle::framework::OpKernel { auto concat = prim_creator.CreateConcatPrimitive(concat_pd, output, place); stream(stream::kind::eager).submit({concat}).wait(); - output->set_layout(DataLayout::kMKLDNN); - output->set_format(GetDstMemFormat(concat_pd)); + output->set_mkldnn_prim_desc(concat_pd.dst_primitive_desc()); } }; } // namespace operators diff --git a/paddle/fluid/operators/mkldnn/conv_mkldnn_op.cc b/paddle/fluid/operators/mkldnn/conv_mkldnn_op.cc index 7ac64e6ba134c034acc58c7310cd51da0f03d16d..14ca3e8073b9512732876e512a30968b15884495 100644 --- a/paddle/fluid/operators/mkldnn/conv_mkldnn_op.cc +++ b/paddle/fluid/operators/mkldnn/conv_mkldnn_op.cc @@ -282,8 +282,7 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel { pipeline.push_back(*conv_p); stream(stream::kind::eager).submit(pipeline).wait(); - auto dst_mpd = dst_memory_p->get_primitive_desc(); - output->set_mkldnn_prim_desc(dst_mpd); + output->set_mkldnn_prim_desc(dst_memory_p->get_primitive_desc()); } void ComputeINT8(const paddle::framework::ExecutionContext& ctx) const { const bool is_test = ctx.Attr("is_test"); @@ -972,8 +971,7 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel { pipeline.push_back(*conv_bwd_data_p); - input_grad->set_layout(DataLayout::kMKLDNN); - input_grad->set_format(GetMKLDNNFormat(*diff_src_memory_p)); + input_grad->set_mkldnn_prim_desc(diff_src_memory_p->get_primitive_desc()); } stream(stream::kind::eager).submit(pipeline).wait(); } @@ -991,12 +989,12 @@ REGISTER_OP_KERNEL_WITH_CUSTOM_TYPE(conv2d, MKLDNN, REGISTER_OP_KERNEL_WITH_CUSTOM_TYPE(conv2d, MKLDNN, ::paddle::platform::CPUPlace, U8, - ops::kConvMKLDNNFP32, + ops::kConvMKLDNNINT8, ops::ConvMKLDNNOpKernel); REGISTER_OP_KERNEL_WITH_CUSTOM_TYPE(conv2d, MKLDNN, ::paddle::platform::CPUPlace, S8, - ops::kConvMKLDNNFP32, + ops::kConvMKLDNNINT8, ops::ConvMKLDNNOpKernel); REGISTER_OP_KERNEL_WITH_CUSTOM_TYPE(conv2d_grad, MKLDNN, diff --git a/paddle/fluid/operators/mkldnn/conv_transpose_mkldnn_op.cc b/paddle/fluid/operators/mkldnn/conv_transpose_mkldnn_op.cc index 317d4cebe26b81ff03c212e6328233d5152ed1b4..79a0c5c7683d677daeb4feea10deab86407f944c 100644 --- a/paddle/fluid/operators/mkldnn/conv_transpose_mkldnn_op.cc +++ b/paddle/fluid/operators/mkldnn/conv_transpose_mkldnn_op.cc @@ -221,8 +221,7 @@ class ConvTransposeMKLDNNOpKernel : public paddle::framework::OpKernel { pipeline.push_back(*conv_p); mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait(); - output->set_layout(DataLayout::kMKLDNN); - output->set_format(platform::GetMKLDNNFormat(*dst_memory_p)); + output->set_mkldnn_prim_desc(dst_memory_p->get_primitive_desc()); } private: diff --git a/paddle/fluid/operators/mkldnn/dequantize_mkldnn_op.cc b/paddle/fluid/operators/mkldnn/dequantize_mkldnn_op.cc index 262b7408a7f5f65c4d97120914c16f38ce5fdbe7..accc9a9d71ffccf2812d57a7516eaf7e0f83275c 100644 --- a/paddle/fluid/operators/mkldnn/dequantize_mkldnn_op.cc +++ b/paddle/fluid/operators/mkldnn/dequantize_mkldnn_op.cc @@ -17,6 +17,7 @@ limitations under the License. */ #include "paddle/fluid/framework/tensor.h" #include "paddle/fluid/operators/dequantize_op.h" #include "paddle/fluid/platform/mkldnn_helper.h" +#include "paddle/fluid/platform/mkldnn_reuse.h" namespace paddle { namespace operators { @@ -30,6 +31,18 @@ using framework::DataLayout; using mkldnn::stream; using platform::GetMKLDNNFormat; +std::string CreateKey(const paddle::framework::ExecutionContext& ctx, + const mkldnn::memory::data_type& src_dt, + const std::vector& src_tz, const float scale_data) { + std::string key; + key.reserve(platform::MKLDNNHandler::MaxKeyLength); + platform::MKLDNNHandler::AppendKey(&key, std::to_string(src_dt)); + platform::MKLDNNHandler::AppendKeyDims(&key, src_tz); + platform::MKLDNNHandler::AppendKey(&key, std::to_string(scale_data)); + platform::MKLDNNHandler::AppendKey(&key, ctx.op().Output("Output")); + return key; +} + template class DeQuantOpKernel : public framework::OpKernel { public: @@ -51,31 +64,55 @@ class DeQuantOpKernel : public framework::OpKernel { mkldnn::memory::data_type src_dt = paddle::framework::ToMKLDNNDataType(input->type()); mkldnn::memory::format src_fmt = input->format(); + std::string key = CreateKey(ctx, src_dt, src_tz, reorder_scale[0]); + const std::string key_prim = key + "@reorder_p"; + const std::string key_src_mem = key + "@src_mem"; + const std::string key_dst_mem = key + "@dst_mem"; + + std::shared_ptr src_memory; + std::shared_ptr dst_memory; + std::shared_ptr reorder_p; + reorder_p = std::static_pointer_cast(dev_ctx.GetBlob(key_prim)); + + if (reorder_p == nullptr) { + mkldnn::primitive_attr attri; + int mask = 0; + attri.set_output_scales(mask, reorder_scale); + + auto src_md = platform::MKLDNNMemDesc({src_tz}, src_dt, src_fmt); + auto src_pd = mkldnn::memory::primitive_desc(src_md, engine); + src_memory = + std::make_shared(src_pd, to_void_cast(input_data)); + std::shared_ptr src_memory_p = + std::shared_ptr(new primitive::at(*src_memory)); + + auto dst_md = platform::MKLDNNMemDesc({dst_tz}, memory::data_type::f32, + memory::format::nchw); + auto dst_pd = mkldnn::memory::primitive_desc(dst_md, engine); + dst_memory = std::make_shared( + dst_pd, to_void_cast(output_data)); + + auto reorder_pd = std::shared_ptr( + new reorder::primitive_desc(src_pd, dst_pd, attri)); + reorder_p = std::shared_ptr( + new reorder(*reorder_pd, *src_memory_p, *dst_memory)); + dev_ctx.SetBlob(key_prim, reorder_p); + dev_ctx.SetBlob(key_src_mem, src_memory); + dev_ctx.SetBlob(key_dst_mem, dst_memory); + } else { + src_memory = std::static_pointer_cast( + dev_ctx.GetBlob(key_src_mem)); + src_memory->set_data_handle(to_void_cast(input_data)); + + dst_memory = std::static_pointer_cast( + dev_ctx.GetBlob(key_dst_mem)); + dst_memory->set_data_handle(output->mutable_data(ctx.GetPlace())); + } - mkldnn::primitive_attr attri; - int mask = 0; - attri.set_output_scales(mask, reorder_scale); - - auto src_md = platform::MKLDNNMemDesc({src_tz}, src_dt, src_fmt); - auto src_pd = mkldnn::memory::primitive_desc(src_md, engine); - auto src_memory = - std::make_shared(src_pd, to_void_cast(input_data)); - std::shared_ptr src_memory_p = - std::shared_ptr(new primitive::at(*src_memory)); - - auto dst_md = platform::MKLDNNMemDesc({dst_tz}, memory::data_type::f32, - memory::format::nchw); - auto dst_pd = mkldnn::memory::primitive_desc(dst_md, engine); - auto dst_memory = mkldnn::memory(dst_pd, to_void_cast(output_data)); - - auto reorder_pd = std::shared_ptr( - new reorder::primitive_desc(src_pd, dst_pd, attri)); - auto reorder_p = std::shared_ptr( - new reorder(*reorder_pd, *src_memory_p, dst_memory)); pipeline.push_back(*reorder_p); stream(stream::kind::eager).submit(pipeline).wait(); - output->set_format(GetMKLDNNFormat(dst_memory)); + output->set_format(GetMKLDNNFormat(*dst_memory)); } }; diff --git a/paddle/fluid/operators/mkldnn/lrn_mkldnn_op.cc b/paddle/fluid/operators/mkldnn/lrn_mkldnn_op.cc index 097ba01d401dbc7969e30f576cac2567c874ed99..4ff27ab12280b56abdf72056fe69ec713f2f2f46 100644 --- a/paddle/fluid/operators/mkldnn/lrn_mkldnn_op.cc +++ b/paddle/fluid/operators/mkldnn/lrn_mkldnn_op.cc @@ -81,10 +81,7 @@ class LRNMKLDNNOpKernel : public paddle::framework::OpKernel { auto e_mid = framework::EigenTensor::From(*mid); e_mid = e_mid.constant(k); - auto dims = paddle::framework::vectorize2int(x->dims()); - - auto src_md = paddle::platform::MKLDNNMemDesc( - dims, mkldnn::memory::data_type::f32, x->format()); + auto src_md = x->get_mkldnn_prim_desc().desc(); auto forward_desc = mkldnn::lrn_forward::desc{mkldnn::prop_kind::forward, mkldnn::lrn_across_channels, @@ -94,7 +91,7 @@ class LRNMKLDNNOpKernel : public paddle::framework::OpKernel { beta, k}; - auto src_memory_pd = mkldnn::memory::primitive_desc{src_md, mkldnn_engine}; + auto src_memory_pd = x->get_mkldnn_prim_desc(); if (!is_test) { const std::string key = ctx.op().Output("Out"); @@ -111,16 +108,15 @@ class LRNMKLDNNOpKernel : public paddle::framework::OpKernel { src_memory->set_data_handle( static_cast(const_cast(input_data))); - auto dst_memory = mkldnn::memory(forward_pd->dst_primitive_desc(), - static_cast(output_data)); + auto dst_memory_pd = forward_pd->dst_primitive_desc(); + auto dst_memory = + mkldnn::memory(dst_memory_pd, static_cast(output_data)); auto workspace_memory = insert_to_context( key_workspace_memory, dev_ctx, forward_pd->workspace_primitive_desc()); run_primitive(*forward_pd, *src_memory, *workspace_memory, dst_memory); - - out->set_layout(framework::DataLayout::kMKLDNN); - out->set_format(platform::GetMKLDNNFormat(dst_memory)); + out->set_mkldnn_prim_desc(dst_memory_pd); } else { auto forward_pd = mkldnn::lrn_forward::primitive_desc{forward_desc, mkldnn_engine}; @@ -128,13 +124,12 @@ class LRNMKLDNNOpKernel : public paddle::framework::OpKernel { src_memory_pd, static_cast(const_cast(input_data))}; auto workspace_memory = mkldnn::memory{forward_pd.workspace_primitive_desc()}; + auto dst_memory_pd = forward_pd.dst_primitive_desc(); auto dst_memory = mkldnn::memory(forward_pd.dst_primitive_desc(), static_cast(output_data)); run_primitive(forward_pd, src_memory, workspace_memory, dst_memory); - - out->set_layout(framework::DataLayout::kMKLDNN); - out->set_format(platform::GetMKLDNNFormat(dst_memory)); + out->set_mkldnn_prim_desc(dst_memory_pd); } } }; diff --git a/paddle/fluid/operators/mkldnn/pool_mkldnn_op.cc b/paddle/fluid/operators/mkldnn/pool_mkldnn_op.cc index 38a65b50bd22354bea54819e8e71015202e96e9f..5d8e81921157cbdf35f7016741ab45c362b7261f 100644 --- a/paddle/fluid/operators/mkldnn/pool_mkldnn_op.cc +++ b/paddle/fluid/operators/mkldnn/pool_mkldnn_op.cc @@ -15,6 +15,7 @@ limitations under the License. */ #include "paddle/fluid/framework/data_layout_transform.h" #include "paddle/fluid/operators/pool_op.h" #include "paddle/fluid/platform/mkldnn_helper.h" +#include "paddle/fluid/platform/mkldnn_reuse.h" namespace paddle { namespace operators { @@ -29,23 +30,23 @@ using mkldnn::stream; using platform::to_void_cast; // Generate keys for storing/retriving primitives for this operator -// TODO(jczaja): Make hashing function more optimial -static std::string gethash(const memory::dims& input_dims, - const std::string& pooling_type, - const std::vector& ksize, - const std::vector& strides, - const std::vector& paddings, - const memory::data_type& dt, - const std::string& suffix) { - auto dims2str = [](const memory::dims& operand_dims) { - std::string dstr = ""; - for (size_t i = 0; i < operand_dims.size(); ++i) { - dstr += std::to_string(operand_dims[i]) + "-"; - } - return dstr; - }; - return dims2str(input_dims) + dims2str(ksize) + dims2str(strides) + - dims2str(paddings) + std::to_string(dt) + pooling_type + suffix; +std::string CreateKey(const paddle::framework::ExecutionContext& ctx, + const memory::dims& input_dims, + const std::string& pooling_type, + const std::vector& ksize, + const std::vector& strides, + const std::vector& paddings, + const memory::data_type& dt, const std::string& suffix) { + std::string key; + key.reserve(platform::MKLDNNHandler::MaxKeyLength); + platform::MKLDNNHandler::AppendKeyDims(&key, input_dims); + platform::MKLDNNHandler::AppendKey(&key, pooling_type); + platform::MKLDNNHandler::AppendKeyVec(&key, ksize); + platform::MKLDNNHandler::AppendKeyVec(&key, strides); + platform::MKLDNNHandler::AppendKeyVec(&key, paddings); + platform::MKLDNNHandler::AppendKey(&key, std::to_string(dt)); + platform::MKLDNNHandler::AppendKey(&key, suffix); + return key; } static inline int ComputeCeiledOutput(int input_size, int kernel_size, @@ -114,8 +115,8 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel { mkldnn::memory::data_type dt = paddle::framework::ToMKLDNNDataType(input->type()); - const std::string key = gethash(src_tz, pooling_type, ksize, strides, - paddings, dt, ctx.op().Output("Out")); + const std::string key = CreateKey(ctx, src_tz, pooling_type, ksize, strides, + paddings, dt, ctx.op().Output("Out")); const std::string key_pool_p = key + "@pool_p"; const std::string key_pool_pd = key + "@pool_pd"; const std::string key_pool_src_mem_p = key + "@pool_src_mem_p"; @@ -294,8 +295,8 @@ class PoolMKLDNNGradOpKernel : public paddle::framework::OpKernel { // Get an unique name from "argument" name of "Out" variable // This name will be used as key when referring info from device context const std::string key = - gethash(diff_src_tz, pooling_type, ksize, strides, paddings, - memory::data_type::f32, ctx.op().Input("Out")); + CreateKey(ctx, diff_src_tz, pooling_type, ksize, strides, paddings, + memory::data_type::f32, ctx.op().Input("Out")); const std::string key_pool_bwd_p = key + "@pool_bwd_p"; const std::string key_pool_diff_src_mem_p = key + "@pool_diff_src_mem_p"; const std::string key_pool_diff_dst_mem_p = key + "@pool_diff_dst_mem_p"; diff --git a/paddle/fluid/operators/mkldnn/quantize_mkldnn_op.cc b/paddle/fluid/operators/mkldnn/quantize_mkldnn_op.cc index 0638e42873376bcec6e4de61494da46d1f0073d1..04cd60be964a3967a45e73122324c4b3fdf0b3d0 100644 --- a/paddle/fluid/operators/mkldnn/quantize_mkldnn_op.cc +++ b/paddle/fluid/operators/mkldnn/quantize_mkldnn_op.cc @@ -30,6 +30,18 @@ using framework::DataLayout; using mkldnn::stream; using platform::GetMKLDNNFormat; +std::string CreateKey(const paddle::framework::ExecutionContext& ctx, + const std::vector& src_tz, const float scale_data, + const bool is_negative) { + std::string key; + key.reserve(platform::MKLDNNHandler::MaxKeyLength); + platform::MKLDNNHandler::AppendKeyDims(&key, src_tz); + platform::MKLDNNHandler::AppendKey(&key, std::to_string(scale_data)); + platform::MKLDNNHandler::AppendKey(&key, std::to_string(is_negative)); + platform::MKLDNNHandler::AppendKey(&key, ctx.op().Output("Output")); + return key; +} + template class QuantOpKernel : public framework::OpKernel { public: @@ -47,32 +59,61 @@ class QuantOpKernel : public framework::OpKernel { const T* input_data = input->data(); - mkldnn::primitive_attr attri; - int mask = 0; - attri.set_output_scales(mask, {scale_data}); - - auto src_md = platform::MKLDNNMemDesc({src_tz}, memory::data_type::f32, - input->format()); - auto src_pd = mkldnn::memory::primitive_desc(src_md, engine); - auto src_memory = - std::make_shared(src_pd, to_void_cast(input_data)); - std::shared_ptr src_memory_p = - std::shared_ptr(new primitive::at(*src_memory)); - bool is_negative = ctx.Attr("is_negative_input"); - std::shared_ptr dst_pd; + std::string key = CreateKey(ctx, src_tz, scale_data, is_negative); + const std::string key_prim = key + "@reorder_p"; + const std::string key_src_mem = key + "@src_mem"; + const std::string key_dst_mem = key + "@dst_mem"; + + std::shared_ptr src_memory; std::shared_ptr dst_memory; - if (is_negative) { - platform::ConvMKLDNNHandler::SetDstMemory( - ctx, output, dst_tz, engine, dst_pd, dst_memory); + std::shared_ptr reorder_p; + reorder_p = std::static_pointer_cast(dev_ctx.GetBlob(key_prim)); + + if (reorder_p == nullptr) { + mkldnn::primitive_attr attri; + int mask = 0; + attri.set_output_scales(mask, {scale_data}); + + auto src_md = platform::MKLDNNMemDesc({src_tz}, memory::data_type::f32, + input->format()); + auto src_pd = mkldnn::memory::primitive_desc(src_md, engine); + src_memory = + std::make_shared(src_pd, to_void_cast(input_data)); + std::shared_ptr src_memory_p = + std::shared_ptr(new primitive::at(*src_memory)); + + std::shared_ptr dst_pd; + if (is_negative) { + platform::ConvMKLDNNHandler::SetDstMemory( + ctx, output, dst_tz, engine, dst_pd, dst_memory); + } else { + platform::ConvMKLDNNHandler::SetDstMemory( + ctx, output, dst_tz, engine, dst_pd, dst_memory); + } + auto reorder_pd = std::shared_ptr( + new reorder::primitive_desc(src_pd, *dst_pd, attri)); + reorder_p = std::shared_ptr( + new reorder(*reorder_pd, *src_memory_p, *dst_memory)); + + dev_ctx.SetBlob(key_prim, reorder_p); + dev_ctx.SetBlob(key_src_mem, src_memory); + dev_ctx.SetBlob(key_dst_mem, dst_memory); } else { - platform::ConvMKLDNNHandler::SetDstMemory( - ctx, output, dst_tz, engine, dst_pd, dst_memory); + src_memory = std::static_pointer_cast( + dev_ctx.GetBlob(key_src_mem)); + src_memory->set_data_handle(to_void_cast(input_data)); + + dst_memory = std::static_pointer_cast( + dev_ctx.GetBlob(key_dst_mem)); + auto place = ctx.GetPlace(); + if (is_negative) { + dst_memory->set_data_handle(output->mutable_data(place)); + } else { + dst_memory->set_data_handle(output->mutable_data(place)); + } } - auto reorder_pd = std::shared_ptr( - new reorder::primitive_desc(src_pd, *dst_pd, attri)); - auto reorder_p = std::shared_ptr( - new reorder(*reorder_pd, *src_memory_p, *dst_memory)); + pipeline.push_back(*reorder_p); stream(stream::kind::eager).submit(pipeline).wait(); output->set_layout(DataLayout::kMKLDNN); diff --git a/paddle/fluid/operators/mkldnn/softmax_mkldnn_op.cc b/paddle/fluid/operators/mkldnn/softmax_mkldnn_op.cc index dc1176f0848b93dd6872f676c3a71dab4f3455fd..0ce552219458859e147ba207c94270bf84a1fe75 100644 --- a/paddle/fluid/operators/mkldnn/softmax_mkldnn_op.cc +++ b/paddle/fluid/operators/mkldnn/softmax_mkldnn_op.cc @@ -158,6 +158,14 @@ class SoftmaxMKLDNNKernel : public paddle::framework::OpKernel { auto softmax_p = handler.AcquireSoftmax(softmax_dst_memory_p, softmax_src_memory_p); + // We cannot use softmax_dst_memory_p to get prim desc as + // it contains flattened dims (2D) while output tensor can + // have 2,3,4+ dims + auto output_mem_pd = paddle::platform::create_prim_desc_from_dims( + paddle::framework::vectorize2int(output->dims()), + mkldnn::memory::format::blocked); + output->set_mkldnn_prim_desc(output_mem_pd); + std::vector pipeline{ *(static_cast(softmax_p.get()))}; stream(stream::kind::eager).submit(pipeline).wait(); diff --git a/paddle/fluid/operators/mkldnn/sum_mkldnn_op.cc b/paddle/fluid/operators/mkldnn/sum_mkldnn_op.cc index fe4131df2c77ed28cd36f23002d000dac3e8a129..aef5b7d4311adfedb3db157f17506c3a2c76fbf6 100644 --- a/paddle/fluid/operators/mkldnn/sum_mkldnn_op.cc +++ b/paddle/fluid/operators/mkldnn/sum_mkldnn_op.cc @@ -79,15 +79,6 @@ class SumMKLDNNOpKernel : public paddle::framework::OpKernel { memory::format input_format = input0.format(); - if (src_tz.size() == 1 && (input_format == memory::format::nchw || - input_format == memory::format::nhwc)) { - input_format = memory::format::x; - } - if (src_tz.size() == 2 && (input_format == memory::format::nchw || - input_format == memory::format::nhwc)) { - input_format = memory::format::nc; - } - for (int i = 0; i < N; i++) { PADDLE_ENFORCE(in_vars[i]->IsType(), "all inputs must be all LoDTensors"); @@ -115,12 +106,12 @@ class SumMKLDNNOpKernel : public paddle::framework::OpKernel { memory::desc(dst_tz, memory::data_type::f32, memory::format::any); auto sum_pd = sum::primitive_desc(dst_md, scales, srcs_mpd); - + auto dst_mem_pd = sum_pd.dst_primitive_desc(); std::shared_ptr dst_mem; if (in_place) { - dst_mem.reset(new memory(sum_pd.dst_primitive_desc())); + dst_mem.reset(new memory(dst_mem_pd)); } else { - dst_mem.reset(new memory(sum_pd.dst_primitive_desc(), output_data)); + dst_mem.reset(new memory(dst_mem_pd, output_data)); } std::vector inputs; for (size_t i = 0; i < srcs_mem.size(); ++i) { @@ -145,107 +136,11 @@ class SumMKLDNNOpKernel : public paddle::framework::OpKernel { if (in_place) pipeline.push_back(reorder_prim); stream(stream::kind::eager).submit(pipeline).wait(); - output->set_layout(DataLayout::kMKLDNN); - output->set_format(output_format); - } else if (out_var->IsType()) { - // TODO(@mozga-intel) Add MKLDNN SelectedRows support - std::unique_ptr in0; - if (in_place) { - // If is in_place, we store the input[0] to in0 - auto& in_sel0 = in_vars[0]->Get(); - auto& rows = in_sel0.rows(); - in0.reset(new framework::SelectedRows(rows, in_sel0.height())); - in0->mutable_value()->ShareDataWith(in_sel0.value()); - } - - auto get_selected_row = [&](size_t i) -> const SelectedRows& { - if (i == 0 && in0) { - return *in0; - } else { - return in_vars[i]->Get(); - } - }; - auto* out = ctx.Output("Out"); - out->mutable_rows()->clear(); - auto* out_value = out->mutable_value(); - - // Runtime InferShape - size_t first_dim = 0; - for (int i = 0; i < N; i++) { - auto& sel_row = get_selected_row(i); - first_dim += sel_row.rows().size(); - } - - std::vector in_dim; - for (int i = 0; i < N; i++) { - auto& sel_row = get_selected_row(i); - if (sel_row.rows().size() > 0) { - in_dim = framework::vectorize(sel_row.value().dims()); - break; - } - } - - if (in_dim.empty()) { - VLOG(3) << "WARNING: all the inputs are empty"; - in_dim = framework::vectorize(get_selected_row(N - 1).value().dims()); - } else { - in_dim[0] = static_cast(first_dim); - } - - in_dim[0] = static_cast(first_dim); - - out_value->Resize(framework::make_ddim(in_dim)); - - out_value->mutable_data(ctx.GetPlace()); - - // if all the input sparse vars are empty, no need to - // merge these vars. - if (first_dim == 0UL) { - return; - } - - math::SelectedRowsAddTo functor; - int64_t offset = 0; - for (int i = 0; i < N; i++) { - auto& sel_row = get_selected_row(i); - if (sel_row.rows().size() == 0) { - continue; - } - PADDLE_ENFORCE_EQ(out->height(), sel_row.height()); - functor(ctx.template device_context(), sel_row, - offset, out); - offset += sel_row.value().numel(); - } - } else if (out_var->IsType()) { - // TODO(@mozga-intel) Add MKLDNN LoDTensorArray support - auto& out_array = *out_var->GetMutable(); - for (size_t i = in_place ? 1 : 0; i < in_vars.size(); ++i) { - PADDLE_ENFORCE(in_vars[i]->IsType(), - "Only support all inputs are TensorArray"); - auto& in_array = in_vars[i]->Get(); - - for (size_t i = 0; i < in_array.size(); ++i) { - if (in_array[i].numel() != 0) { - if (i >= out_array.size()) { - out_array.resize(i + 1); - } - if (out_array[i].numel() == 0) { - framework::TensorCopy(in_array[i], in_array[i].place(), - ctx.device_context(), &out_array[i]); - out_array[i].set_lod(in_array[i].lod()); - } else { - PADDLE_ENFORCE(out_array[i].lod() == in_array[i].lod()); - auto in = EigenVector::Flatten(in_array[i]); - auto result = EigenVector::Flatten(out_array[i]); - result.device(*ctx.template device_context() - .eigen_device()) = result + in; - } - } - } - } - } else { - PADDLE_THROW("Unexpected branch, output variable type is %s", - framework::ToTypeName(out_var->Type())); + output->set_mkldnn_prim_desc(dst_mem_pd); + } else { // Fallback to naive version + // TODO(@mozga-intel) Add MKLDNN SelectedRows & LoDTensorArray support + SumKernel reference_kernel; + reference_kernel.Compute(ctx); } } }; diff --git a/paddle/fluid/operators/ngraph/ngraph_bridge.cc b/paddle/fluid/operators/ngraph/ngraph_bridge.cc index 996376c53f07b5c26eccad382e734f187f75f5a1..dafc31b546e3ca6d8dc8d5634dd51cff9fe5bfb7 100644 --- a/paddle/fluid/operators/ngraph/ngraph_bridge.cc +++ b/paddle/fluid/operators/ngraph/ngraph_bridge.cc @@ -14,6 +14,7 @@ limitations under the License. */ #include #include +#include #include #include "ngraph/ngraph.hpp" diff --git a/paddle/fluid/operators/ngraph/ngraph_bridge.h b/paddle/fluid/operators/ngraph/ngraph_bridge.h index 952d5b0b4362aa1c1112782885ab5d30698f5cff..b609c284959238689eaf35c87d1bc4e4330b5c1f 100644 --- a/paddle/fluid/operators/ngraph/ngraph_bridge.h +++ b/paddle/fluid/operators/ngraph/ngraph_bridge.h @@ -16,6 +16,7 @@ limitations under the License. */ #include #include +#include #include #include diff --git a/paddle/fluid/operators/ngraph/ngraph_engine.cc b/paddle/fluid/operators/ngraph/ngraph_engine.cc index 660a3298cbe4bf5d83851a916bb3ea8d260214a3..41037d9039bb53038af80eafa269ee9246dc9980 100644 --- a/paddle/fluid/operators/ngraph/ngraph_engine.cc +++ b/paddle/fluid/operators/ngraph/ngraph_engine.cc @@ -16,7 +16,10 @@ limitations under the License. */ #include #include +#include #include +#include +#include #include #include "paddle/fluid/framework/block_desc.h" @@ -483,7 +486,8 @@ void NgraphEngine::Run(const framework::Scope& scope, } } - backend_->call(backend_->compile(ngraph_function_), t_out, t_in); + auto handle = backend_->compile(ngraph_function_); + handle->call_with_validate(t_out, t_in); } // NgraphEngine::Run } // namespace operators } // namespace paddle diff --git a/paddle/fluid/operators/ngraph/ops/accuracy_op.h b/paddle/fluid/operators/ngraph/ops/accuracy_op.h index d90ec97298b0f6fb8480e97ca57cb427784261e4..0da57517a733985ce1208732f13b08cd7bb8ca30 100644 --- a/paddle/fluid/operators/ngraph/ops/accuracy_op.h +++ b/paddle/fluid/operators/ngraph/ops/accuracy_op.h @@ -14,7 +14,9 @@ limitations under the License. */ #pragma once +#include #include +#include #include #include "ngraph/ngraph.hpp" #include "paddle/fluid/operators/ngraph/ops/op_bridge.h" diff --git a/paddle/fluid/operators/ngraph/ops/activation_op.h b/paddle/fluid/operators/ngraph/ops/activation_op.h index d1b0b80d227a5042219a17e35255617726aa8042..d04dbf648616d9957e2dfb0c416b624540747fe2 100644 --- a/paddle/fluid/operators/ngraph/ops/activation_op.h +++ b/paddle/fluid/operators/ngraph/ops/activation_op.h @@ -14,7 +14,9 @@ limitations under the License. */ #pragma once +#include #include +#include #include "ngraph/ngraph.hpp" #include "paddle/fluid/operators/ngraph/ops/op_bridge.h" diff --git a/paddle/fluid/operators/ngraph/ops/adam_op.h b/paddle/fluid/operators/ngraph/ops/adam_op.h new file mode 100644 index 0000000000000000000000000000000000000000..beba5d3d237d4dea578651f440b65a15251d5ad2 --- /dev/null +++ b/paddle/fluid/operators/ngraph/ops/adam_op.h @@ -0,0 +1,79 @@ +/*Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#pragma once + +#include +#include +#include + +#include "ngraph/ngraph.hpp" +#include "paddle/fluid/operators/ngraph/ops/elementwise_scalar_op.h" +#include "paddle/fluid/operators/ngraph/ops/op_bridge.h" +#include "paddle/fluid/platform/ngraph_helper.h" + +namespace paddle { +namespace operators { +namespace ngraphs { + +void BuildAdamNode( + const std::shared_ptr& op, + std::shared_ptr< + std::unordered_map>> + ngb_node_map) { + auto op_attrs = framework::AttrReader(op->Attrs()); + auto beta1pow = platform::GetInputNode(op, "Beta1Pow", ngb_node_map); + auto beta2pow = platform::GetInputNode(op, "Beta2Pow", ngb_node_map); + auto grad = platform::GetInputNode(op, "Grad", ngb_node_map); + auto learning_rate = platform::GetInputNode(op, "LearningRate", ngb_node_map); + auto moment1 = platform::GetInputNode(op, "Moment1", ngb_node_map); + auto moment2 = platform::GetInputNode(op, "Moment2", ngb_node_map); + auto param = platform::GetInputNode(op, "Param", ngb_node_map); + + auto epsilon = op_attrs.Get("epsilon"); + auto beta2 = op_attrs.Get("beta2"); + auto beta1 = op_attrs.Get("beta1"); + + auto moment1_shape = moment1->get_shape(); + auto grad_shape = grad->get_shape(); + + auto moment1out = std::make_shared( + ElementwiseScalar(beta1, moment1), + ElementwiseScalar(1. - beta1, grad)); + + auto grad_square = std::make_shared(grad, grad); + auto moment2out = std::make_shared( + ElementwiseScalar(beta2, moment2), + ElementwiseScalar(1. - beta2, grad_square)); + auto node_sqrt = std::make_shared( + ElementwiseScalar(1., beta2pow)); + auto lr = std::make_shared( + node_sqrt, ElementwiseScalar(1., beta1pow)); + auto updated_lr = std::make_shared(learning_rate, lr); + + auto moment2_sqrt = std::make_shared(moment2out); + auto param_grad = std::make_shared( + moment1out, ElementwiseScalar(epsilon, moment2_sqrt)); + auto delta = ElementwiseScalar(updated_lr, param_grad); + auto param_out = std::make_shared(param, delta); + + platform::SetOutputNode(op, "Moment1Out", moment1out, ngb_node_map); + platform::SetOutputNode(op, "Moment2Out", moment2out, ngb_node_map); + platform::SetOutputNode(op, "ParamOut", param_out, ngb_node_map); +} +} // namespace ngraphs +} // namespace operators +} // namespace paddle + +REGISTER_NG_OP(adam, BuildAdamNode); diff --git a/paddle/fluid/operators/ngraph/ops/batch_norm_op.h b/paddle/fluid/operators/ngraph/ops/batch_norm_op.h index 2d638bb53f084ee75014d64302ec3d86b3bcf26f..01fe78cdb24652429f713d09ea2abb8c73bbddf5 100644 --- a/paddle/fluid/operators/ngraph/ops/batch_norm_op.h +++ b/paddle/fluid/operators/ngraph/ops/batch_norm_op.h @@ -14,7 +14,9 @@ limitations under the License. */ #pragma once +#include #include +#include #include #include "ngraph/ngraph.hpp" diff --git a/paddle/fluid/operators/ngraph/ops/binary_unary_op.h b/paddle/fluid/operators/ngraph/ops/binary_unary_op.h index 375f188286c123b1d652f8780989404760c8e1a4..2d11775849a778262dcd3e36ff35d8851fb350f1 100644 --- a/paddle/fluid/operators/ngraph/ops/binary_unary_op.h +++ b/paddle/fluid/operators/ngraph/ops/binary_unary_op.h @@ -14,7 +14,9 @@ limitations under the License. */ #pragma once +#include #include +#include #include "ngraph/ngraph.hpp" #include "paddle/fluid/operators/ngraph/ops/op_bridge.h" #include "paddle/fluid/platform/ngraph_helper.h" diff --git a/paddle/fluid/operators/ngraph/ops/concat_op.h b/paddle/fluid/operators/ngraph/ops/concat_op.h new file mode 100644 index 0000000000000000000000000000000000000000..27d796851501b9158e1ce7f6415b4d5373e88e2d --- /dev/null +++ b/paddle/fluid/operators/ngraph/ops/concat_op.h @@ -0,0 +1,50 @@ +/*Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#pragma once + +#include +#include +#include +#include +#include "ngraph/ngraph.hpp" +#include "paddle/fluid/operators/ngraph/ops/op_bridge.h" +#include "paddle/fluid/platform/ngraph_helper.h" + +namespace paddle { +namespace operators { +namespace ngraphs { + +void BuildConcatNode( + const std::shared_ptr& op, + std::shared_ptr< + std::unordered_map>> + ngb_node_map) { + std::vector> args; + for (auto& var_name_item : op->Inputs()) { + for (auto& var_name : var_name_item.second) { + auto& node0 = ngb_node_map->at(var_name); + args.push_back(node0); + } + } + auto op_attrs = framework::AttrReader(op->Attrs()); + const size_t axis = op_attrs.Get("axis"); + auto out = std::make_shared(args, axis); + platform::SetOutputNode(op, "Out", out, ngb_node_map); +} +} // namespace ngraphs +} // namespace operators +} // namespace paddle + +REGISTER_NG_OP(concat, BuildConcatNode); diff --git a/paddle/fluid/operators/ngraph/ops/conv2d_op.h b/paddle/fluid/operators/ngraph/ops/conv2d_op.h index d664825c53ebf17435a0ec532969978abe6d30ca..be766ebeb4796be102c917296238b8ab14710131 100644 --- a/paddle/fluid/operators/ngraph/ops/conv2d_op.h +++ b/paddle/fluid/operators/ngraph/ops/conv2d_op.h @@ -14,7 +14,9 @@ limitations under the License. */ #pragma once +#include #include +#include #include #include "ngraph/ngraph.hpp" #include "paddle/fluid/operators/ngraph/ops/op_bridge.h" diff --git a/paddle/fluid/operators/ngraph/ops/cross_entropy_op.h b/paddle/fluid/operators/ngraph/ops/cross_entropy_op.h index 3ab158f3e13a33bdb7e423919c7592831fa9831a..be36b9d21ef6ebe5c11d783462e7dc564afe2aba 100644 --- a/paddle/fluid/operators/ngraph/ops/cross_entropy_op.h +++ b/paddle/fluid/operators/ngraph/ops/cross_entropy_op.h @@ -15,7 +15,9 @@ limitations under the License. */ #pragma once #include +#include #include +#include #include "ngraph/ngraph.hpp" #include "paddle/fluid/operators/ngraph/ops/op_bridge.h" diff --git a/paddle/fluid/operators/ngraph/ops/elementwise_add_op.h b/paddle/fluid/operators/ngraph/ops/elementwise_add_op.h index fb796c336a9b45966a0ff703286faa8b61752483..d7485a706a193a52113cb993a3604c444b4303c0 100644 --- a/paddle/fluid/operators/ngraph/ops/elementwise_add_op.h +++ b/paddle/fluid/operators/ngraph/ops/elementwise_add_op.h @@ -14,7 +14,9 @@ limitations under the License. */ #pragma once +#include #include +#include #include #include "ngraph/ngraph.hpp" diff --git a/paddle/fluid/operators/ngraph/ops/fill_constant_op.h b/paddle/fluid/operators/ngraph/ops/fill_constant_op.h index bc958f2ba27cf929408d56d41bf22976caf7d6ae..42c2df5259242b7ae28613ab12c237834febc574 100644 --- a/paddle/fluid/operators/ngraph/ops/fill_constant_op.h +++ b/paddle/fluid/operators/ngraph/ops/fill_constant_op.h @@ -14,7 +14,9 @@ limitations under the License. */ #pragma once +#include #include +#include #include #include "ngraph/ngraph.hpp" #include "paddle/fluid/operators/ngraph/ops/op_bridge.h" diff --git a/paddle/fluid/operators/ngraph/ops/mean_op.h b/paddle/fluid/operators/ngraph/ops/mean_op.h index f839d9978d71c2967a7f2c2f22622dc615907831..86e697d260eb0f26428258b5faea958a7319948c 100644 --- a/paddle/fluid/operators/ngraph/ops/mean_op.h +++ b/paddle/fluid/operators/ngraph/ops/mean_op.h @@ -15,7 +15,9 @@ limitations under the License. */ #pragma once #include +#include #include +#include #include "ngraph/ngraph.hpp" #include "paddle/fluid/operators/ngraph/ops/elementwise_scalar_op.h" diff --git a/paddle/fluid/operators/ngraph/ops/momentum_op.h b/paddle/fluid/operators/ngraph/ops/momentum_op.h index b8291a08a28b585a7ceb67642ba28c3314195790..84bddacba89d2921bca4915af7f64dcfbfdd42db 100644 --- a/paddle/fluid/operators/ngraph/ops/momentum_op.h +++ b/paddle/fluid/operators/ngraph/ops/momentum_op.h @@ -14,7 +14,9 @@ limitations under the License. */ #pragma once +#include #include +#include #include #include "ngraph/ngraph.hpp" #include "paddle/fluid/operators/ngraph/ops/op_bridge.h" diff --git a/paddle/fluid/operators/ngraph/ops/mul_op.h b/paddle/fluid/operators/ngraph/ops/mul_op.h index 98c70a1a99aa899ed8fdd3c4674668cefd14c4ae..d13665864b8950436298b7cf685c803593007803 100644 --- a/paddle/fluid/operators/ngraph/ops/mul_op.h +++ b/paddle/fluid/operators/ngraph/ops/mul_op.h @@ -14,7 +14,9 @@ limitations under the License. */ #pragma once +#include #include +#include #include "ngraph/ngraph.hpp" #include "paddle/fluid/operators/ngraph/ops/op_bridge.h" #include "paddle/fluid/platform/ngraph_helper.h" diff --git a/paddle/fluid/operators/ngraph/ops/pool2d_op.h b/paddle/fluid/operators/ngraph/ops/pool2d_op.h index a6371372ef10c093c41153cb0dc73f4f9e95687f..c7b9c9316171a448d16ed68339f5754d25f3cabd 100644 --- a/paddle/fluid/operators/ngraph/ops/pool2d_op.h +++ b/paddle/fluid/operators/ngraph/ops/pool2d_op.h @@ -14,7 +14,9 @@ limitations under the License. */ #pragma once +#include #include +#include #include #include "ngraph/ngraph.hpp" diff --git a/paddle/fluid/operators/ngraph/ops/scale_op.h b/paddle/fluid/operators/ngraph/ops/scale_op.h index a334192419f572c429f5842cd9e418d8945eb0ef..1461b85b16ece79548f3ca95be811fb31136c610 100644 --- a/paddle/fluid/operators/ngraph/ops/scale_op.h +++ b/paddle/fluid/operators/ngraph/ops/scale_op.h @@ -14,7 +14,9 @@ limitations under the License. */ #pragma once +#include #include +#include #include "ngraph/ngraph.hpp" #include "paddle/fluid/operators/ngraph/ops/elementwise_scalar_op.h" #include "paddle/fluid/operators/ngraph/ops/op_bridge.h" diff --git a/paddle/fluid/operators/ngraph/ops/softmax_op.h b/paddle/fluid/operators/ngraph/ops/softmax_op.h index 1df6418de06d000892d2802596df61320fcdc759..7d5720c460c4194ce06670a715b8d7ff4435bb2a 100644 --- a/paddle/fluid/operators/ngraph/ops/softmax_op.h +++ b/paddle/fluid/operators/ngraph/ops/softmax_op.h @@ -14,7 +14,9 @@ limitations under the License. */ #pragma once +#include #include +#include #include #include "ngraph/ngraph.hpp" #include "paddle/fluid/operators/ngraph/ops/elementwise_scalar_op.h" diff --git a/paddle/fluid/operators/ngraph/ops/sum_op.h b/paddle/fluid/operators/ngraph/ops/sum_op.h index 97f4ce64aa58bfa8cb70c36f9a12b7b8135da637..ab8cdb8f4d847c0acb60b39d07dc83f085b60bbd 100644 --- a/paddle/fluid/operators/ngraph/ops/sum_op.h +++ b/paddle/fluid/operators/ngraph/ops/sum_op.h @@ -18,6 +18,7 @@ limitations under the License. */ #include #include "ngraph/ngraph.hpp" +#include "paddle/fluid/operators/ngraph/ops/op_bridge.h" #include "paddle/fluid/platform/ngraph_helper.h" namespace paddle { @@ -53,3 +54,5 @@ void BuildSumNode( } // namespace ngraphs } // namespace operators } // namespace paddle + +REGISTER_NG_OP(sum, BuildSumNode); diff --git a/paddle/fluid/operators/ngraph/ops/top_k_op.h b/paddle/fluid/operators/ngraph/ops/top_k_op.h index 6d10faa7c2efb9cbd87fa8ef1c6ecb4fa350d8f6..cdc26f6afd58700c3a1f57fa955d60bc8925d2d1 100644 --- a/paddle/fluid/operators/ngraph/ops/top_k_op.h +++ b/paddle/fluid/operators/ngraph/ops/top_k_op.h @@ -14,7 +14,9 @@ limitations under the License. */ #pragma once +#include #include +#include #include "ngraph/ngraph.hpp" #include "paddle/fluid/operators/ngraph/ops/op_bridge.h" #include "paddle/fluid/platform/ngraph_helper.h" diff --git a/paddle/fluid/operators/optimizers/sgd_op.h b/paddle/fluid/operators/optimizers/sgd_op.h index 98bae5e1d329005f9463fd7bb0751c44952dea88..c9c9f530fe846c1713ad176e05a377996d04470b 100644 --- a/paddle/fluid/operators/optimizers/sgd_op.h +++ b/paddle/fluid/operators/optimizers/sgd_op.h @@ -16,6 +16,7 @@ limitations under the License. */ #include "paddle/fluid/framework/eigen.h" #include "paddle/fluid/framework/op_registry.h" #include "paddle/fluid/framework/selected_rows.h" +#include "paddle/fluid/operators/jit/kernels.h" namespace paddle { namespace operators { @@ -32,53 +33,57 @@ class SGDOpKernel : public framework::OpKernel { if (param_var->IsType()) { const auto *param = ctx.Input("Param"); auto *param_out = ctx.Output("ParamOut"); - // Actually, all tensors are LoDTensor except SelectedRows. if (grad_var->IsType()) { - param_out->mutable_data(ctx.GetPlace()); const auto *grad = ctx.Input("Grad"); - - auto p = framework::EigenVector::Flatten(*param); - auto g = framework::EigenVector::Flatten(*grad); - auto o = framework::EigenVector::Flatten(*param_out); - auto *lr = learning_rate->data(); - - o = p - lr[0] * g; + auto sz = param_out->numel(); + PADDLE_ENFORCE_EQ(param->numel(), sz); + PADDLE_ENFORCE_EQ(grad->numel(), sz); + + jit::sgd_attr_t attr(1, sz, 1, sz, 1); + const T *lr = learning_rate->data(); + const T *param_data = param->data(); + const T *grad_data = grad->data(); + int64_t rows_idx = 0; + T *out_data = param_out->mutable_data(ctx.GetPlace()); + + auto sgd = + jit::Get, platform::CPUPlace>(attr); + sgd(lr, param_data, grad_data, &rows_idx, out_data, &attr); } else if (grad_var->IsType()) { // TODO(qijun): In Sparse SGD operator, in-place update is enforced. // This manual optimization brings difficulty to track data dependency. // It's better to find a more elegant solution. PADDLE_ENFORCE_EQ(param, param_out); const auto *grad = ctx.Input("Grad"); + auto &grad_rows = grad->rows(); // for distributed training, a sparse var may be empty, // just skip updating. - if (grad->rows().size() == 0) { + if (grad_rows.size() == 0) { return; } - auto grad_height = grad->height(); auto out_dims = param_out->dims(); - PADDLE_ENFORCE_EQ(grad_height, out_dims[0]); - + PADDLE_ENFORCE_EQ(grad->height(), out_dims[0]); auto &grad_value = grad->value(); - auto &grad_rows = grad->rows(); - - size_t grad_row_numel = grad_value.numel() / grad_rows.size(); - PADDLE_ENFORCE_EQ(static_cast(grad_row_numel), - param_out->numel() / grad_height); - - auto *grad_data = grad_value.data(); - auto *out_data = param_out->data(); - auto *lr = learning_rate->data(); - for (size_t i = 0; i < grad_rows.size(); i++) { - PADDLE_ENFORCE(grad_rows[i] < grad_height, - "Input rows index should less than height"); - for (size_t j = 0; j < grad_row_numel; j++) { - out_data[grad_rows[i] * grad_row_numel + j] -= - lr[0] * grad_data[i * grad_row_numel + j]; - } - } + const T *param_data = param->data(); + const T *grad_data = grad_value.data(); + const T *lr = learning_rate->data(); + const int64_t *rows_data = grad_rows.data(); + T *out_data = param_out->mutable_data(ctx.GetPlace()); + + jit::sgd_attr_t attr; + attr.param_height = out_dims[0]; + attr.param_width = param_out->numel() / attr.param_height; + attr.grad_height = grad_rows.size(); // note: it is not grad->height() + attr.grad_width = grad_value.numel() / attr.grad_height; + attr.selected_rows_size = grad_rows.size(); + PADDLE_ENFORCE_EQ(attr.grad_width, attr.param_width); + + auto sgd = + jit::Get, platform::CPUPlace>(attr); + sgd(lr, param_data, grad_data, rows_data, out_data, &attr); } else { PADDLE_THROW("Unsupported Variable Type of Grad"); } diff --git a/paddle/fluid/operators/reader/buffered_reader.cc b/paddle/fluid/operators/reader/buffered_reader.cc index db80fda695dc7666ebe8d930427dd781449e26dd..719e0ee8a1709c89a94dcce7088622157eafc6d9 100644 --- a/paddle/fluid/operators/reader/buffered_reader.cc +++ b/paddle/fluid/operators/reader/buffered_reader.cc @@ -13,6 +13,7 @@ // limitations under the License. #include "paddle/fluid/operators/reader/buffered_reader.h" +#include #include #include "paddle/fluid/framework/data_type.h" diff --git a/paddle/fluid/operators/sequence_ops/sequence_enumerate_op.cc b/paddle/fluid/operators/sequence_ops/sequence_enumerate_op.cc index 0932211cadf30d0c464d43ca652a5c52df15747e..d3dcd1f96a986d2450c8af780a12183f7dfc66d5 100644 --- a/paddle/fluid/operators/sequence_ops/sequence_enumerate_op.cc +++ b/paddle/fluid/operators/sequence_ops/sequence_enumerate_op.cc @@ -22,6 +22,9 @@ class SequenceEnumerateOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; void InferShape(framework::InferShapeContext* ctx) const override { + if (ctx->IsRuntime()) { + return; + } PADDLE_ENFORCE( ctx->HasInput("X"), "Input(X) of SequecceEnumerate operator should not be null."); @@ -33,9 +36,9 @@ class SequenceEnumerateOp : public framework::OperatorWithKernel { PADDLE_ENFORCE_EQ( x_dims.size(), 2, "Input(X) of SequenceEnumerate operator's rank should be 2."); - PADDLE_ENFORCE_EQ( - x_dims[1], 1, - "Input(X) of SequenceEnumerate operator's 2nd dimension should be 1."); + PADDLE_ENFORCE_EQ(x_dims[1], 1, + "Input(X) of SequenceEnumerate operator's 2nd " + "dimension should be 1."); const auto win_size = ctx->Attrs().Get("win_size"); ctx->SetOutputDim("Out", {x_dims[0], win_size}); diff --git a/paddle/fluid/operators/sequence_ops/sequence_enumerate_op.cu b/paddle/fluid/operators/sequence_ops/sequence_enumerate_op.cu index 28821e7129c1601f1214b0b56696fbf526a2123f..d5deb7582c7c00f3102ea568a716b715611212ce 100644 --- a/paddle/fluid/operators/sequence_ops/sequence_enumerate_op.cu +++ b/paddle/fluid/operators/sequence_ops/sequence_enumerate_op.cu @@ -65,6 +65,7 @@ class SequenceEnumerateOpCUDAKernel : public framework::OpKernel { auto lod0 = in_lod[0]; auto in_len = in->numel(); auto in_data = in->data(); + out->Resize({in_dims[0], win_size}); auto out_data = out->mutable_data(context.GetPlace()); // Copy LoD to GPU const size_t* dev_in_lod_ptr = lod0.CUDAData(context.GetPlace()); @@ -72,6 +73,7 @@ class SequenceEnumerateOpCUDAKernel : public framework::OpKernel { CalcOutPut<<<(in_len - 1) / PADDLE_CUDA_NUM_THREADS + 1, PADDLE_CUDA_NUM_THREADS, 0, stream>>>( in_data, dev_in_lod_ptr, lod0.size(), win_size, pad_value, out_data); + out->set_lod(in->lod()); } }; diff --git a/paddle/fluid/operators/sequence_ops/sequence_enumerate_op.h b/paddle/fluid/operators/sequence_ops/sequence_enumerate_op.h index dc18d9b2071303377505155476b87ed029eaf986..18da69993b2ad5879dd4678ec0d4b06d7e30cb0a 100644 --- a/paddle/fluid/operators/sequence_ops/sequence_enumerate_op.h +++ b/paddle/fluid/operators/sequence_ops/sequence_enumerate_op.h @@ -39,6 +39,7 @@ class SequenceEnumerateKernel : public framework::OpKernel { // Generate enumerate sequence set auto lod0 = in_lod[0]; auto in_data = in->data(); + out->Resize({in_dims[0], win_size}); auto out_data = out->mutable_data(context.GetPlace()); for (size_t i = 0; i < lod0.size() - 1; ++i) { for (size_t idx = lod0[i]; idx < lod0[i + 1]; ++idx) { @@ -49,6 +50,7 @@ class SequenceEnumerateKernel : public framework::OpKernel { } } } + out->set_lod(in->lod()); } }; diff --git a/paddle/fluid/operators/sequence_ops/sequence_expand_op.cu b/paddle/fluid/operators/sequence_ops/sequence_expand_op.cu index afc08c7b3f6596efd3b6e0b74c17aa3c9268c47d..888d1a12e6751eeb91f0af04b50cf6d5bea74162 100644 --- a/paddle/fluid/operators/sequence_ops/sequence_expand_op.cu +++ b/paddle/fluid/operators/sequence_ops/sequence_expand_op.cu @@ -13,6 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. */ #include +#include "paddle/fluid/memory/memcpy.h" #include "paddle/fluid/operators/sequence_ops/sequence_expand_op.h" #include "paddle/fluid/platform/cuda_primitives.h" @@ -88,6 +89,49 @@ void GetOutputOffset(const framework::Vector& x_lod, } } +template +static int ExpandByMemoryCopy(const platform::CUDADeviceContext& context, + const LoDTensor& x, LoDTensor* out, + const framework::Vector& x_lod, + const framework::Vector& ref_lod, + bool do_copy) { + auto out_data = out->data(); + auto x_data = x.data(); + + auto& gpu_place = boost::get(context.GetPlace()); + + int x_item_length = x.numel() / x.dims()[0]; + int out_offset = 0; + int num_copys = 0; + for (size_t i = 1; i < ref_lod.size(); ++i) { + int repeat_num = ref_lod[i] - ref_lod[i - 1]; + int x_start = x_lod[i - 1]; + int x_end = x_lod[i]; + int x_seq_len = x_end - x_start; + if (repeat_num > 0) { + if (do_copy) { + int out_start = out_offset; + if (out->lod().size() == 1) { + out_start = out->lod()[0][out_offset]; + } + for (int j = 0; j < repeat_num; j++) { + for (int k = 0; k < x_seq_len; k++) { + memory::Copy( + gpu_place, + out_data + (out_start + j * x_seq_len + k) * x_item_length, + gpu_place, x_data + (x_start + k) * x_item_length, + sizeof(T) * x_item_length, context.stream()); + } + } + } else { + num_copys += repeat_num * x_seq_len; + } + } + out_offset += repeat_num; + } + return num_copys; +} + template struct SequenceExpandFunctor { void operator()( @@ -95,22 +139,40 @@ struct SequenceExpandFunctor { const framework::Vector& x_lod, /*expand source lod*/ const framework::Vector& ref_lod, /*expand referenced lod*/ LoDTensor* out) { - int x_item_length = x.numel() / x.dims()[0]; - framework::Vector out_offset(x_lod.size()); - GetOutputOffset(x_lod, ref_lod, &out_offset); - - int thread_x = std::min(32, std::max(static_cast(ref_lod.size()), 16)); - int thread_y = 16; - int thread_z = 1024 / thread_x / thread_y; - int block_x = static_cast(ref_lod.size()); - dim3 block_size(thread_x, thread_y, thread_z); - dim3 grid_size(block_x, 1); + int num_copys = + ExpandByMemoryCopy(context, x, out, x_lod, ref_lod, false); + // Sometimes direct copies will be faster, this maybe need deeply analysis. + if (num_copys < 5) { + ExpandByMemoryCopy(context, x, out, x_lod, ref_lod, true); + } else { + int x_item_length = x.numel() / x.dims()[0]; + size_t x_lod_size = x_lod.size(); + framework::Vector out_offset(x_lod_size * 2 + ref_lod.size()); + GetOutputOffset(x_lod, ref_lod, &out_offset); + + for (size_t i = 0; i < x_lod_size; ++i) { + out_offset[x_lod_size + i] = x_lod[i]; + } + for (size_t i = 0; i < ref_lod.size(); ++i) { + out_offset[2 * x_lod_size + i] = ref_lod[i]; + } - sequence_expand_kernel<<>>( - x.data(), x_lod.CUDAData(context.GetPlace()), - ref_lod.CUDAData(context.GetPlace()), - out_offset.CUDAData(context.GetPlace()), x_lod.size(), x_item_length, - out->mutable_data(context.GetPlace())); + const size_t* out_offset_data = out_offset.CUDAData(context.GetPlace()); + const size_t* x_lod_data = out_offset_data + x_lod_size; + const size_t* ref_lod_data = out_offset_data + 2 * x_lod_size; + + int thread_x = + std::min(32, std::max(static_cast(ref_lod.size()), 16)); + int thread_y = 16; + int thread_z = 1024 / thread_x / thread_y; + int block_x = static_cast(ref_lod.size()); + dim3 block_size(thread_x, thread_y, thread_z); + dim3 grid_size(block_x, 1); + + sequence_expand_kernel<<>>( + x.data(), x_lod_data, ref_lod_data, out_offset_data, x_lod_size, + x_item_length, out->mutable_data(context.GetPlace())); + } } }; diff --git a/paddle/fluid/operators/softmax_with_cross_entropy_op.cc b/paddle/fluid/operators/softmax_with_cross_entropy_op.cc index 0397c7791e1768393ff642743d2f7085b25fb551..7754d2bfebdbc81e25432641b2eb4315386f75ff 100644 --- a/paddle/fluid/operators/softmax_with_cross_entropy_op.cc +++ b/paddle/fluid/operators/softmax_with_cross_entropy_op.cc @@ -46,10 +46,10 @@ class SoftmaxWithCrossEntropyOpMaker .SetDefault(false); AddAttr( "numeric_stable_mode", - "(bool, default: false), A flag to indicate whether to use more " + "(bool, default: true), A flag to indicate whether to use more " "numerically stable algorithm. This flag is only valid when " "soft_label is false and GPU is used.") - .SetDefault(false); + .SetDefault(true); AddAttr( "ignore_index", "(int, default -100), Specifies a target value that is ignored and" diff --git a/paddle/fluid/operators/teacher_student_sigmoid_loss_op.cc b/paddle/fluid/operators/teacher_student_sigmoid_loss_op.cc index c8ee13875c5ae772de3c09f97fded8f70c5698e6..640644a94690d9682a5e6b1aa788a9ebdc5d2a54 100644 --- a/paddle/fluid/operators/teacher_student_sigmoid_loss_op.cc +++ b/paddle/fluid/operators/teacher_student_sigmoid_loss_op.cc @@ -117,11 +117,11 @@ class TeacherStudentSigmoidLossOpMaker "[N x 1]. The teacher student sigmoid loss."); AddAttr( "soft_max_up_bound", - "fp32, if input > soft_max_up_bound, will be bound, default 15.0") + "fp32, if input > soft_max_up_bound, input will be bound, default 15.0") .SetDefault(15.0); - AddAttr( - "soft_max_lower_bound", - "fp32, if input < soft_max_lower_bound, will be bound, default -15.0") + AddAttr("soft_max_lower_bound", + "fp32, if input < soft_max_lower_bound, input will be " + "bound, default -15.0") .SetDefault(-15.0); AddComment(R"DOC( TeacherStudentSigmoidLoss Operator. @@ -134,7 +134,7 @@ we add another label(z') to original. label = {-2, -1, [0, 2]} when z' is not exist, clk = 0 : label = -2; when z' is not exist, clk = 1 : label = -1; - when z' is exist , clk = 0 : label = 0 + z'; + when z' is exist , clk = 0 : label = 0 + z'; when z' is exist , clk = 1 : label = 1 + z'; )DOC"); diff --git a/paddle/fluid/platform/CMakeLists.txt b/paddle/fluid/platform/CMakeLists.txt index 1838506c8931b2e1ff82adf6f277925dc9d53374..9220d35707b286d76ab4824e3f1080453f60bfe6 100644 --- a/paddle/fluid/platform/CMakeLists.txt +++ b/paddle/fluid/platform/CMakeLists.txt @@ -82,6 +82,7 @@ nv_test(device_context_test SRCS device_context_test.cu DEPS device_context gpu_ cc_test(init_test SRCS init_test.cc DEPS device_context) nv_test(cudnn_helper_test SRCS cudnn_helper_test.cc DEPS dynload_cuda) +nv_test(cudnn_desc_test SRCS cudnn_desc_test.cc DEPS dynload_cuda) nv_test(transform_test SRCS transform_test.cu DEPS memory place device_context) cc_library(timer SRCS timer.cc) diff --git a/paddle/fluid/platform/cudnn_desc.h b/paddle/fluid/platform/cudnn_desc.h new file mode 100644 index 0000000000000000000000000000000000000000..1062b403f289610a6dec28dead9177d387f0d4e0 --- /dev/null +++ b/paddle/fluid/platform/cudnn_desc.h @@ -0,0 +1,124 @@ +// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include "paddle/fluid/platform/cudnn_helper.h" + +namespace paddle { +namespace platform { +using framework::Tensor; + +template +cudnnDataType_t ToCudnnDataType(const T& t) { + auto type = framework::ToDataType(t); + return ToCudnnDataType(type); +} + +template <> +cudnnDataType_t ToCudnnDataType(const framework::proto::VarType::Type& t) { + cudnnDataType_t type = CUDNN_DATA_FLOAT; + switch (t) { + case framework::proto::VarType::FP16: + type = CUDNN_DATA_HALF; + break; + case framework::proto::VarType::FP32: + type = CUDNN_DATA_FLOAT; + break; + case framework::proto::VarType::FP64: + type = CUDNN_DATA_DOUBLE; + break; + default: + break; + } + return type; +} + +class ActivationDescriptor { + public: + using T = cudnnActivationStruct; + struct Deleter { + void operator()(T* t) { + if (t != nullptr) { + PADDLE_ENFORCE(dynload::cudnnDestroyActivationDescriptor(t)); + t = nullptr; + } + } + }; + ActivationDescriptor() { + T* raw_ptr; + PADDLE_ENFORCE(dynload::cudnnCreateActivationDescriptor(&raw_ptr)); + desc_.reset(raw_ptr); + } + template + void set(cudnnActivationMode_t mode, const T& coef) { + CUDNN_ENFORCE(dynload::cudnnSetActivationDescriptor( + desc_.get(), mode, CUDNN_NOT_PROPAGATE_NAN, static_cast(coef))); + } + + T* desc() { return desc_.get(); } + T* desc() const { return desc_.get(); } + + private: + std::unique_ptr desc_; +}; + +class TensorDescriptor { + public: + using T = cudnnTensorStruct; + struct Deleter { + void operator()(T* t) { + if (t != nullptr) { + PADDLE_ENFORCE(dynload::cudnnDestroyTensorDescriptor(t)); + t = nullptr; + } + } + }; + TensorDescriptor() { + T* raw_ptr; + PADDLE_ENFORCE(dynload::cudnnCreateTensorDescriptor(&raw_ptr)); + desc_.reset(raw_ptr); + } + T* desc() { return desc_.get(); } + T* desc() const { return desc_.get(); } + void set(const Tensor& tensor, const int groups = 1) { + auto dims = framework::vectorize2int(tensor.dims()); + std::vector strides(dims.size()); + strides[dims.size() - 1] = 1; + for (int i = dims.size() - 2; i >= 0; i--) { + strides[i] = dims[i + 1] * strides[i + 1]; + } + std::vector dims_with_group(dims.begin(), dims.end()); + if (groups > 1) { + dims_with_group[1] = dims_with_group[1] / groups; + } + PADDLE_ENFORCE(dynload::cudnnSetTensorNdDescriptor( + desc_.get(), ToCudnnDataType(tensor.type()), dims_with_group.size(), + dims_with_group.data(), strides.data())); + } + + private: + std::unique_ptr desc_; +}; + +} // namespace platform +} // namespace paddle diff --git a/paddle/fluid/platform/cudnn_desc_test.cc b/paddle/fluid/platform/cudnn_desc_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..a60102a54899b25c89d8c131220dde21f77bba70 --- /dev/null +++ b/paddle/fluid/platform/cudnn_desc_test.cc @@ -0,0 +1,41 @@ +// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "paddle/fluid/platform/cudnn_desc.h" +#include + +namespace paddle { +namespace platform { + +TEST(TensorDescriptor, Empty) { + ActivationDescriptor a; + TensorDescriptor t; + TensorDescriptor t1; + TensorDescriptor *t11 = new TensorDescriptor(); + delete t11; + std::unique_ptr tt(new TensorDescriptor()); +} + +TEST(TensorDescriptor, Normal) { + framework::Tensor tt; + tt.Resize({2, 3, 4}); + tt.mutable_data(platform::CPUPlace()); + + TensorDescriptor desc; + desc.set(tt); + EXPECT_TRUE(desc.desc() != nullptr); +} + +} // namespace platform +} // namespace paddle diff --git a/paddle/fluid/platform/dynload/cudnn.h b/paddle/fluid/platform/dynload/cudnn.h index 2f4f8101e4b957634d68fb0d64649ff8afba7c54..3008c166938d7db190e8f716ca925fda5ccebc25 100644 --- a/paddle/fluid/platform/dynload/cudnn.h +++ b/paddle/fluid/platform/dynload/cudnn.h @@ -99,6 +99,7 @@ extern void EnforceCUDNNLoaded(const char* fn_name); __macro(cudnnDestroy); \ __macro(cudnnSetStream); \ __macro(cudnnActivationForward); \ + __macro(cudnnActivationBackward); \ __macro(cudnnConvolutionForward); \ __macro(cudnnConvolutionBackwardBias); \ __macro(cudnnGetConvolutionForwardWorkspaceSize); \ diff --git a/paddle/fluid/platform/dynload/mklml.h b/paddle/fluid/platform/dynload/mklml.h index a260cda49138580b209e647af459e9392d9f18f1..a5b846f500f3677188b170dda76c65047d628064 100644 --- a/paddle/fluid/platform/dynload/mklml.h +++ b/paddle/fluid/platform/dynload/mklml.h @@ -86,6 +86,8 @@ extern void* mklml_dso_handle; __macro(vdPowx); \ __macro(vsInv); \ __macro(vdInv); \ + __macro(vmsErf); \ + __macro(vmdErf); \ __macro(MKL_Set_Num_Threads) MKLML_ROUTINE_EACH(DECLARE_DYNAMIC_LOAD_MKLML_WRAP); diff --git a/paddle/fluid/platform/event.h b/paddle/fluid/platform/event.h index a4db23758b1c477114cd03dcd0e9f51296c575c6..2dcf966754cbed2670acb9c3548c23355be5503c 100644 --- a/paddle/fluid/platform/event.h +++ b/paddle/fluid/platform/event.h @@ -14,6 +14,9 @@ limitations under the License. */ #pragma once #include +#ifdef PADDLE_WITH_CUDA +#include +#endif namespace paddle { namespace platform { diff --git a/paddle/fluid/platform/mkldnn_reuse.h b/paddle/fluid/platform/mkldnn_reuse.h index 4a674ca526f455314613d43847faa7e01f4d7802..4fa6774f028bef901f6e11f2d3dafe52a10a548e 100644 --- a/paddle/fluid/platform/mkldnn_reuse.h +++ b/paddle/fluid/platform/mkldnn_reuse.h @@ -271,7 +271,6 @@ class MKLDNNHandler { AppendKey(key, suffix); } - protected: static void AppendKeyDims(std::string* key, const mkldnn::memory::dims& dims) { for (unsigned int i = 0; i < dims.size(); i++) { @@ -289,6 +288,7 @@ class MKLDNNHandler { key->append(s); } + protected: static std::string dims2str(const mkldnn::memory::dims& operand_dims) { std::string dstr = ""; for (size_t i = 0; i < operand_dims.size(); ++i) { @@ -302,6 +302,9 @@ class MKLDNNHandler { mkldnn::engine engine_; std::string key_; bool is_reusing_; + + public: + static constexpr int MaxKeyLength = 256; }; class TransposeMKLDNNHandler : public MKLDNNHandler { diff --git a/paddle/fluid/pybind/imperative.h b/paddle/fluid/pybind/imperative.h index f947b743f99d5d4994b1a87f89fd6815357d8125..8c48b2a7153c566930a074bd0bab1f054c13c2d5 100644 --- a/paddle/fluid/pybind/imperative.h +++ b/paddle/fluid/pybind/imperative.h @@ -33,7 +33,7 @@ class Layer : public imperative::Layer { } }; -class PyOpBase : public imperative::OpBase { +class PYBIND11_HIDDEN PyOpBase : public imperative::OpBase { public: using imperative::OpBase::OpBase; // Inherit constructors }; diff --git a/paddle/fluid/pybind/ir.cc b/paddle/fluid/pybind/ir.cc index 069750e2406bcbf327591641bf624f36969acc25..68f74a8531fff0c49c8a62d12f5cde7af77faf8a 100644 --- a/paddle/fluid/pybind/ir.cc +++ b/paddle/fluid/pybind/ir.cc @@ -14,6 +14,7 @@ #include "paddle/fluid/pybind/ir.h" #include +#include #include #include #include @@ -116,7 +117,7 @@ void BindNode(py::module *m) { .def("is_var", &Node::IsVar) .def("is_ctrl_var", &Node::IsCtrlVar) .def("clear_inputs", [](Node &self) { self.inputs.clear(); }) - .def("inputs_remove", + .def("remove_input", [](Node &self, int node_id) { auto pos = std::find_if( self.inputs.begin(), self.inputs.end(), @@ -125,7 +126,7 @@ void BindNode(py::module *m) { self.inputs.erase(pos); } }) - .def("inputs_remove", + .def("remove_input", [](Node &self, Node &node) { auto pos = std::find(self.inputs.begin(), self.inputs.end(), &node); @@ -133,10 +134,10 @@ void BindNode(py::module *m) { self.inputs.erase(pos); } }) - .def("inputs_append", + .def("append_input", [](Node &self, Node &node) { self.inputs.push_back(&node); }) .def("clear_outputs", [](Node &self) { self.outputs.clear(); }) - .def("outputs_remove", + .def("remove_output", [](Node &self, int node_id) { auto pos = std::find_if( self.outputs.begin(), self.outputs.end(), @@ -145,7 +146,7 @@ void BindNode(py::module *m) { self.outputs.erase(pos); } }) - .def("outputs_remove", + .def("remove_output", [](Node &self, Node &node) { auto pos = std::find(self.outputs.begin(), self.outputs.end(), &node); @@ -153,7 +154,7 @@ void BindNode(py::module *m) { self.outputs.erase(pos); } }) - .def("outputs_append", + .def("append_output", [](Node &self, Node &node) { self.outputs.push_back(&node); }) .def_readwrite("inputs", &Node::inputs) .def_readwrite("outputs", &Node::outputs); diff --git a/paddle/fluid/pybind/protobuf.cc b/paddle/fluid/pybind/protobuf.cc index 48fe445b7d01287c37bcf7d4811f687785ca78d5..e729be4a95a58510f1e0162af4216feaa400d971 100644 --- a/paddle/fluid/pybind/protobuf.cc +++ b/paddle/fluid/pybind/protobuf.cc @@ -189,8 +189,6 @@ void BindBlockDesc(pybind11::module *m) { return self.HasVar(name); }, pybind11::return_value_policy::reference) - .def("_clear_block", [](pd::BlockDesc &self) { return self.Clear(); }, - pybind11::return_value_policy::reference) .def("_rename_var", [](pd::BlockDesc &self, const pybind11::bytes &byte_name, const pybind11::bytes &byte_name_new) { diff --git a/paddle/fluid/pybind/pybind.cc b/paddle/fluid/pybind/pybind.cc index af049127aa3960cb283345e314d680a85056fbd5..6d1fc0be232ec8e43c77e64b71666fdd5af92d86 100644 --- a/paddle/fluid/pybind/pybind.cc +++ b/paddle/fluid/pybind/pybind.cc @@ -86,6 +86,14 @@ bool IsCompiledWithCUDA() { #endif } +bool IsCompiledWithMKLDNN() { +#ifndef PADDLE_WITH_MKLDNN + return false; +#else + return true; +#endif +} + bool IsCompiledWithBrpc() { #ifndef PADDLE_WITH_DISTRIBUTE return false; @@ -169,6 +177,23 @@ PYBIND11_MODULE(core, m) { py::return_value_policy::take_ownership) .def("value", [](const imperative::VarBase &self) { return self.var_; }, py::return_value_policy::reference) + .def_property("name", + [](const imperative::VarBase &self) { return self.name_; }, + [](imperative::VarBase &self, const std::string &name) { + self.name_ = name; + }) + .def_property("block", + [](const imperative::VarBase &self) { return self.block_; }, + [](imperative::VarBase &self, framework::BlockDesc *block) { + self.block_ = block; + }, + py::return_value_policy::reference) + .def_property( + "persistable", + [](const imperative::VarBase &self) { return self.persistable_; }, + [](imperative::VarBase &self, const bool persistable) { + self.persistable_ = persistable; + }) .def_property( "desc", [](const imperative::VarBase &self) { return self.var_desc_; }, @@ -185,6 +210,10 @@ PYBIND11_MODULE(core, m) { py::class_(m, "OpBase", R"DOC()DOC") .def(py::init<>()) + .def("register_backward_hooks", + [](imperative::OpBase &self, const py::object &callable) { + self.RegisterBackwardHooks(callable); + }) .def_property( "desc", [](const imperative::OpBase &self) { return self.op_desc_; }, [](imperative::OpBase &self, framework::OpDesc *op_desc) { @@ -193,6 +222,16 @@ PYBIND11_MODULE(core, m) { } }, py::return_value_policy::reference) + .def_property("_trace_id", + [](const imperative::OpBase &self) { + pybind11::gil_scoped_release release; + return self.trace_id_; + }, + [](imperative::OpBase &self, int trace_id) { + pybind11::gil_scoped_release release; + self.trace_id_ = trace_id; + }, + py::return_value_policy::reference) .def_property( "forward_id", [](const imperative::OpBase &self) { return self.forward_id_; }, @@ -405,11 +444,11 @@ PYBIND11_MODULE(core, m) { Set LoD of the LoDTensor according to recursive sequence length. For example, if recursive_sequence_lengths=[[2, 3]], meaning that - there are two sequences with length 2 and 3 respectively, the - corresponding lod would be [[0, 2, 2+3]], i.e, [[0, 2, 5]]. + there are two sequences with length 2 and 3 respectively, the + corresponding lod would be [[0, 2, 2+3]], i.e, [[0, 2, 5]]. Args: - recursive_sequence_lengths (List[List[int]]): sequence lengths. + recursive_sequence_lengths (List[List[int]]): sequence lengths. )DOC") .def("lod", [](LoDTensor &self) -> std::vector> { @@ -440,7 +479,7 @@ PYBIND11_MODULE(core, m) { Return the sequence length of the LoDTensor corresponding to LoD. Returns: - out (List[List[int]): the sequence lengths. + out (List[List[int]): the sequence lengths. )DOC") .def("has_valid_recursive_sequence_lengths", [](LoDTensor &self) -> bool { @@ -592,29 +631,29 @@ All parameter, weight, gradient are variables in Paddle. }, py::arg("name"), R"DOC( - Find or create variable named :code:`name` in the current scope. + Find or create variable named :code:`name` in the current scope. - If the variable named :code:`name` does not exist in the + If the variable named :code:`name` does not exist in the current scope, the variable would be created. Otherwise, - return the existing variable. + return the existing variable. Args: - name (str): the variable name. - + name (str): the variable name. + Returns: - out (core.Variable): the found or created variable. + out (core.Variable): the found or created variable. )DOC", py::return_value_policy::reference) .def("find_var", &Scope::FindVar, py::arg("name"), R"DOC( - Find variable named :code:`name` in the current scope or + Find variable named :code:`name` in the current scope or its parent scope. Return None if not found. - + Args: name (str): the variable name. - + Returns: - out (core.Variable|None): the found variable or None. + out (core.Variable|None): the found variable or None. )DOC", py::return_value_policy::reference) .def("new_scope", [](Scope &self) -> Scope * { return &self.NewScope(); }, @@ -638,7 +677,7 @@ All parameter, weight, gradient are variables in Paddle. }, R"DOC( Create a new scope. - + Returns: out (core._Scope): the created scope. )DOC", @@ -849,6 +888,7 @@ All parameter, weight, gradient are variables in Paddle. [](bool init_p2p) { framework::InitDevices(init_p2p); }); m.def("is_compiled_with_cuda", IsCompiledWithCUDA); + m.def("is_compiled_with_mkldnn", IsCompiledWithMKLDNN); m.def("is_compiled_with_brpc", IsCompiledWithBrpc); m.def("is_compiled_with_dist", IsCompiledWithDIST); #ifdef PADDLE_WITH_CUDA diff --git a/paddle/scripts/cpplint.py b/paddle/scripts/cpplint.py deleted file mode 100644 index dff4339ea33b72e22104a56183e3302067dc583d..0000000000000000000000000000000000000000 --- a/paddle/scripts/cpplint.py +++ /dev/null @@ -1,6425 +0,0 @@ -#!/usr/bin/env python -# -# Copyright (c) 2009 Google Inc. All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are -# met: -# -# * Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. -# * Redistributions in binary form must reproduce the above -# copyright notice, this list of conditions and the following disclaimer -# in the documentation and/or other materials provided with the -# distribution. -# * Neither the name of Google Inc. nor the names of its -# contributors may be used to endorse or promote products derived from -# this software without specific prior written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -"""Does google-lint on c++ files. - -The goal of this script is to identify places in the code that *may* -be in non-compliance with google style. It does not attempt to fix -up these problems -- the point is to educate. It does also not -attempt to find all problems, or to ensure that everything it does -find is legitimately a problem. - -In particular, we can get very confused by /* and // inside strings! -We do a small hack, which is to ignore //'s with "'s after them on the -same line, but it is far from perfect (in either direction). - -EDIT(yuyang18): Add #pragma once as include guard. -EDIT(yuyang18): Add NOLINTNEXTLINES_ to suppress multiline lint. -""" - -import codecs -import copy -import getopt -import math # for log -import os -import re -import sre_compile -import string -import sys -import unicodedata - -_USAGE = """ -Syntax: cpplint.py [--verbose=#] [--output=vs7] [--filter=-x,+y,...] - [--counting=total|toplevel|detailed] [--root=subdir] - [--linelength=digits] - [--write-success=success_status_file] - [file] ... - - The style guidelines this tries to follow are those in - http://google-styleguide.googlecode.com/svn/trunk/cppguide.xml - - Every problem is given a confidence score from 1-5, with 5 meaning we are - certain of the problem, and 1 meaning it could be a legitimate construct. - This will miss some errors, and is not a substitute for a code review. - - To suppress false-positive errors of a certain category, add a - 'NOLINT(category)' comment to the line. NOLINT or NOLINT(*) - suppresses errors of all categories on that line. - - The files passed in will be linted; at least one file must be provided. - Default linted extensions are .cc, .cpp, .cu, .cuh and .h. Change the - extensions with the --extensions flag. - - Flags: - - output=vs7 - By default, the output is formatted to ease emacs parsing. Visual Studio - compatible output (vs7) may also be used. Other formats are unsupported. - - verbose=# - Specify a number 0-5 to restrict errors to certain verbosity levels. - - filter=-x,+y,... - Specify a comma-separated list of category-filters to apply: only - error messages whose category names pass the filters will be printed. - (Category names are printed with the message and look like - "[whitespace/indent]".) Filters are evaluated left to right. - "-FOO" and "FOO" means "do not print categories that start with FOO". - "+FOO" means "do print categories that start with FOO". - - Examples: --filter=-whitespace,+whitespace/braces - --filter=whitespace,runtime/printf,+runtime/printf_format - --filter=-,+build/include_what_you_use - - To see a list of all the categories used in cpplint, pass no arg: - --filter= - - counting=total|toplevel|detailed - The total number of errors found is always printed. If - 'toplevel' is provided, then the count of errors in each of - the top-level categories like 'build' and 'whitespace' will - also be printed. If 'detailed' is provided, then a count - is provided for each category like 'build/class'. - - root=subdir - The root directory used for deriving header guard CPP variable. - By default, the header guard CPP variable is calculated as the relative - path to the directory that contains .git, .hg, or .svn. When this flag - is specified, the relative path is calculated from the specified - directory. If the specified directory does not exist, this flag is - ignored. - - Examples: - Assuming that src/.git exists, the header guard CPP variables for - src/chrome/browser/ui/browser.h are: - - No flag => CHROME_BROWSER_UI_BROWSER_H_ - --root=chrome => BROWSER_UI_BROWSER_H_ - --root=chrome/browser => UI_BROWSER_H_ - - linelength=digits - This is the allowed line length for the project. The default value is - 80 characters. - - Examples: - --linelength=120 - - extensions=extension,extension,... - The allowed file extensions that cpplint will check - - Examples: - --extensions=hpp,cpp - - cpplint.py supports per-directory configurations specified in CPPLINT.cfg - files. CPPLINT.cfg file can contain a number of key=value pairs. - Currently the following options are supported: - - set noparent - filter=+filter1,-filter2,... - exclude_files=regex - linelength=80 - - "set noparent" option prevents cpplint from traversing directory tree - upwards looking for more .cfg files in parent directories. This option - is usually placed in the top-level project directory. - - The "filter" option is similar in function to --filter flag. It specifies - message filters in addition to the |_DEFAULT_FILTERS| and those specified - through --filter command-line flag. - - "exclude_files" allows to specify a regular expression to be matched against - a file name. If the expression matches, the file is skipped and not run - through liner. - - "linelength" allows to specify the allowed line length for the project. - - CPPLINT.cfg has an effect on files in the same directory and all - sub-directories, unless overridden by a nested configuration file. - - Example file: - filter=-build/include_order,+build/include_alpha - exclude_files=.*\.cc - - The above example disables build/include_order warning and enables - build/include_alpha as well as excludes all .cc from being - processed by linter, in the current directory (where the .cfg - file is located) and all sub-directories. -""" - -# We categorize each error message we print. Here are the categories. -# We want an explicit list so we can list them all in cpplint --filter=. -# If you add a new error message with a new category, add it to the list -# here! cpplint_unittest.py should tell you if you forget to do this. -_ERROR_CATEGORIES = [ - 'build/class', - 'build/c++11', - 'build/deprecated', - 'build/endif_comment', - 'build/explicit_make_pair', - 'build/forward_decl', - 'build/header_guard', - 'build/include', - 'build/include_alpha', - 'build/include_order', - 'build/include_what_you_use', - 'build/namespaces', - 'build/printf_format', - 'build/storage_class', - 'legal/copyright', - 'readability/alt_tokens', - 'readability/braces', - 'readability/casting', - 'readability/check', - 'readability/constructors', - 'readability/fn_size', - 'readability/function', - 'readability/inheritance', - 'readability/multiline_comment', - 'readability/multiline_string', - 'readability/namespace', - 'readability/nolint', - 'readability/nul', - 'readability/strings', - 'readability/todo', - 'readability/utf8', - 'runtime/arrays', - 'runtime/casting', - 'runtime/explicit', - 'runtime/int', - 'runtime/init', - 'runtime/invalid_increment', - 'runtime/member_string_references', - 'runtime/memset', - 'runtime/indentation_namespace', - 'runtime/operator', - 'runtime/printf', - 'runtime/printf_format', - 'runtime/references', - 'runtime/string', - 'runtime/threadsafe_fn', - 'runtime/vlog', - 'whitespace/blank_line', - 'whitespace/braces', - 'whitespace/comma', - 'whitespace/comments', - 'whitespace/empty_conditional_body', - 'whitespace/empty_loop_body', - 'whitespace/end_of_line', - 'whitespace/ending_newline', - 'whitespace/forcolon', - 'whitespace/indent', - 'whitespace/line_length', - 'whitespace/newline', - 'whitespace/operators', - 'whitespace/parens', - 'whitespace/semicolon', - 'whitespace/tab', - 'whitespace/todo', -] - -# These error categories are no longer enforced by cpplint, but for backwards- -# compatibility they may still appear in NOLINT comments. -_LEGACY_ERROR_CATEGORIES = ['readability/streams', ] - -# The default state of the category filter. This is overridden by the --filter= -# flag. By default all errors are on, so only add here categories that should be -# off by default (i.e., categories that must be enabled by the --filter= flags). -# All entries here should start with a '-' or '+', as in the --filter= flag. -_DEFAULT_FILTERS = ['-build/include_alpha'] - -# We used to check for high-bit characters, but after much discussion we -# decided those were OK, as long as they were in UTF-8 and didn't represent -# hard-coded international strings, which belong in a separate i18n file. - -# C++ headers -_CPP_HEADERS = frozenset([ - # Legacy - 'algobase.h', - 'algo.h', - 'alloc.h', - 'builtinbuf.h', - 'bvector.h', - 'complex.h', - 'defalloc.h', - 'deque.h', - 'editbuf.h', - 'fstream.h', - 'function.h', - 'hash_map', - 'hash_map.h', - 'hash_set', - 'hash_set.h', - 'hashtable.h', - 'heap.h', - 'indstream.h', - 'iomanip.h', - 'iostream.h', - 'istream.h', - 'iterator.h', - 'list.h', - 'map.h', - 'multimap.h', - 'multiset.h', - 'ostream.h', - 'pair.h', - 'parsestream.h', - 'pfstream.h', - 'procbuf.h', - 'pthread_alloc', - 'pthread_alloc.h', - 'rope', - 'rope.h', - 'ropeimpl.h', - 'set.h', - 'slist', - 'slist.h', - 'stack.h', - 'stdiostream.h', - 'stl_alloc.h', - 'stl_relops.h', - 'streambuf.h', - 'stream.h', - 'strfile.h', - 'strstream.h', - 'tempbuf.h', - 'tree.h', - 'type_traits.h', - 'vector.h', - # 17.6.1.2 C++ library headers - 'algorithm', - 'array', - 'atomic', - 'bitset', - 'chrono', - 'codecvt', - 'complex', - 'condition_variable', - 'deque', - 'exception', - 'forward_list', - 'fstream', - 'functional', - 'future', - 'initializer_list', - 'iomanip', - 'ios', - 'iosfwd', - 'iostream', - 'istream', - 'iterator', - 'limits', - 'list', - 'locale', - 'map', - 'memory', - 'mutex', - 'new', - 'numeric', - 'ostream', - 'queue', - 'random', - 'ratio', - 'regex', - 'set', - 'sstream', - 'stack', - 'stdexcept', - 'streambuf', - 'string', - 'strstream', - 'system_error', - 'thread', - 'tuple', - 'typeindex', - 'typeinfo', - 'type_traits', - 'unordered_map', - 'unordered_set', - 'utility', - 'valarray', - 'vector', - # 17.6.1.2 C++ headers for C library facilities - 'cassert', - 'ccomplex', - 'cctype', - 'cerrno', - 'cfenv', - 'cfloat', - 'cinttypes', - 'ciso646', - 'climits', - 'clocale', - 'cmath', - 'csetjmp', - 'csignal', - 'cstdalign', - 'cstdarg', - 'cstdbool', - 'cstddef', - 'cstdint', - 'cstdio', - 'cstdlib', - 'cstring', - 'ctgmath', - 'ctime', - 'cuchar', - 'cwchar', - 'cwctype', -]) - -# These headers are excluded from [build/include] and [build/include_order] -# checks: -# - Anything not following google file name conventions (containing an -# uppercase character, such as Python.h or nsStringAPI.h, for example). -# - Lua headers. -_THIRD_PARTY_HEADERS_PATTERN = re.compile( - r'^(?:[^/]*[A-Z][^/]*\.h|lua\.h|lauxlib\.h|lualib\.h)$') - -# Assertion macros. These are defined in base/logging.h and -# testing/base/gunit.h. Note that the _M versions need to come first -# for substring matching to work. -_CHECK_MACROS = [ - 'DCHECK', - 'CHECK', - 'EXPECT_TRUE_M', - 'EXPECT_TRUE', - 'ASSERT_TRUE_M', - 'ASSERT_TRUE', - 'EXPECT_FALSE_M', - 'EXPECT_FALSE', - 'ASSERT_FALSE_M', - 'ASSERT_FALSE', -] - -# Replacement macros for CHECK/DCHECK/EXPECT_TRUE/EXPECT_FALSE -_CHECK_REPLACEMENT = dict([(m, {}) for m in _CHECK_MACROS]) - -for op, replacement in [('==', 'EQ'), ('!=', 'NE'), ('>=', 'GE'), ('>', 'GT'), - ('<=', 'LE'), ('<', 'LT')]: - _CHECK_REPLACEMENT['DCHECK'][op] = 'DCHECK_%s' % replacement - _CHECK_REPLACEMENT['CHECK'][op] = 'CHECK_%s' % replacement - _CHECK_REPLACEMENT['EXPECT_TRUE'][op] = 'EXPECT_%s' % replacement - _CHECK_REPLACEMENT['ASSERT_TRUE'][op] = 'ASSERT_%s' % replacement - _CHECK_REPLACEMENT['EXPECT_TRUE_M'][op] = 'EXPECT_%s_M' % replacement - _CHECK_REPLACEMENT['ASSERT_TRUE_M'][op] = 'ASSERT_%s_M' % replacement - -for op, inv_replacement in [('==', 'NE'), ('!=', 'EQ'), ('>=', 'LT'), - ('>', 'LE'), ('<=', 'GT'), ('<', 'GE')]: - _CHECK_REPLACEMENT['EXPECT_FALSE'][op] = 'EXPECT_%s' % inv_replacement - _CHECK_REPLACEMENT['ASSERT_FALSE'][op] = 'ASSERT_%s' % inv_replacement - _CHECK_REPLACEMENT['EXPECT_FALSE_M'][op] = 'EXPECT_%s_M' % inv_replacement - _CHECK_REPLACEMENT['ASSERT_FALSE_M'][op] = 'ASSERT_%s_M' % inv_replacement - -# Alternative tokens and their replacements. For full list, see section 2.5 -# Alternative tokens [lex.digraph] in the C++ standard. -# -# Digraphs (such as '%:') are not included here since it's a mess to -# match those on a word boundary. -_ALT_TOKEN_REPLACEMENT = { - 'and': '&&', - 'bitor': '|', - 'or': '||', - 'xor': '^', - 'compl': '~', - 'bitand': '&', - 'and_eq': '&=', - 'or_eq': '|=', - 'xor_eq': '^=', - 'not': '!', - 'not_eq': '!=' -} - -# Compile regular expression that matches all the above keywords. The "[ =()]" -# bit is meant to avoid matching these keywords outside of boolean expressions. -# -# False positives include C-style multi-line comments and multi-line strings -# but those have always been troublesome for cpplint. -_ALT_TOKEN_REPLACEMENT_PATTERN = re.compile(r'[ =()](' + ('|'.join( - _ALT_TOKEN_REPLACEMENT.keys())) + r')(?=[ (]|$)') - -# These constants define types of headers for use with -# _IncludeState.CheckNextIncludeOrder(). -_C_SYS_HEADER = 1 -_CPP_SYS_HEADER = 2 -_LIKELY_MY_HEADER = 3 -_POSSIBLE_MY_HEADER = 4 -_OTHER_HEADER = 5 - -# These constants define the current inline assembly state -_NO_ASM = 0 # Outside of inline assembly block -_INSIDE_ASM = 1 # Inside inline assembly block -_END_ASM = 2 # Last line of inline assembly block -_BLOCK_ASM = 3 # The whole block is an inline assembly block - -# Match start of assembly blocks -_MATCH_ASM = re.compile(r'^\s*(?:asm|_asm|__asm|__asm__)' - r'(?:\s+(volatile|__volatile__))?' - r'\s*[{(]') - -_regexp_compile_cache = {} - -# {str, set(int)}: a map from error categories to sets of linenumbers -# on which those errors are expected and should be suppressed. -_error_suppressions = {} - -# The root directory used for deriving header guard CPP variable. -# This is set by --root flag. -_root = None - -# The allowed line length of files. -# This is set by --linelength flag. -_line_length = 80 - -# The allowed extensions for file names -# This is set by --extensions flag. -_valid_extensions = set(['cc', 'h', 'cpp', 'cu', 'cuh']) - -_write_success = None - - -def ParseNolintSuppressions(filename, raw_line, linenum, error): - """Updates the global list of error-suppressions. - - Parses any NOLINT comments on the current line, updating the global - error_suppressions store. Reports an error if the NOLINT comment - was malformed. - - Args: - filename: str, the name of the input file. - raw_line: str, the line of input text, with comments. - linenum: int, the number of the current line. - error: function, an error handler. - """ - matched = Search(r'\bNOLINT(NEXTLINE(S_\d+)?)?\b(\([^)]+\))?', raw_line) - if matched: - if matched.group(1): - lines = matched.group(2) - if lines: - lines = int(lines[2:]) - suppressed_line = [linenum + i for i in xrange(lines)] - else: - suppressed_line = linenum + 1 - else: - suppressed_line = linenum - category = matched.group(3) - if category in (None, '(*)'): # => "suppress all" - if isinstance(suppressed_line, int): - _error_suppressions.setdefault(None, set()).add(suppressed_line) - else: - for _line in suppressed_line: - _error_suppressions.setdefault(None, set()).add(_line) - else: - if category.startswith('(') and category.endswith(')'): - category = category[1:-1] - if category in _ERROR_CATEGORIES: - if isinstance(suppressed_line, int): - _error_suppressions.setdefault( - category, set()).add(suppressed_line) - else: - for _line in suppressed_line: - _error_suppressions.setdefault(category, - set()).add(_line) - elif category not in _LEGACY_ERROR_CATEGORIES: - error(filename, linenum, 'readability/nolint', 5, - 'Unknown NOLINT error category: %s' % category) - - -def ResetNolintSuppressions(): - """Resets the set of NOLINT suppressions to empty.""" - _error_suppressions.clear() - - -def IsErrorSuppressedByNolint(category, linenum): - """Returns true if the specified error category is suppressed on this line. - - Consults the global error_suppressions map populated by - ParseNolintSuppressions/ResetNolintSuppressions. - - Args: - category: str, the category of the error. - linenum: int, the current line number. - Returns: - bool, True iff the error should be suppressed due to a NOLINT comment. - """ - return (linenum in _error_suppressions.get(category, set()) or - linenum in _error_suppressions.get(None, set())) - - -def Match(pattern, s): - """Matches the string with the pattern, caching the compiled regexp.""" - # The regexp compilation caching is inlined in both Match and Search for - # performance reasons; factoring it out into a separate function turns out - # to be noticeably expensive. - if pattern not in _regexp_compile_cache: - _regexp_compile_cache[pattern] = sre_compile.compile(pattern) - return _regexp_compile_cache[pattern].match(s) - - -def ReplaceAll(pattern, rep, s): - """Replaces instances of pattern in a string with a replacement. - - The compiled regex is kept in a cache shared by Match and Search. - - Args: - pattern: regex pattern - rep: replacement text - s: search string - - Returns: - string with replacements made (or original string if no replacements) - """ - if pattern not in _regexp_compile_cache: - _regexp_compile_cache[pattern] = sre_compile.compile(pattern) - return _regexp_compile_cache[pattern].sub(rep, s) - - -def Search(pattern, s): - """Searches the string for the pattern, caching the compiled regexp.""" - if pattern not in _regexp_compile_cache: - _regexp_compile_cache[pattern] = sre_compile.compile(pattern) - return _regexp_compile_cache[pattern].search(s) - - -class _IncludeState(object): - """Tracks line numbers for includes, and the order in which includes appear. - - include_list contains list of lists of (header, line number) pairs. - It's a lists of lists rather than just one flat list to make it - easier to update across preprocessor boundaries. - - Call CheckNextIncludeOrder() once for each header in the file, passing - in the type constants defined above. Calls in an illegal order will - raise an _IncludeError with an appropriate error message. - - """ - # self._section will move monotonically through this set. If it ever - # needs to move backwards, CheckNextIncludeOrder will raise an error. - _INITIAL_SECTION = 0 - _MY_H_SECTION = 1 - _C_SECTION = 2 - _CPP_SECTION = 3 - _OTHER_H_SECTION = 4 - - _TYPE_NAMES = { - _C_SYS_HEADER: 'C system header', - _CPP_SYS_HEADER: 'C++ system header', - _LIKELY_MY_HEADER: 'header this file implements', - _POSSIBLE_MY_HEADER: 'header this file may implement', - _OTHER_HEADER: 'other header', - } - _SECTION_NAMES = { - _INITIAL_SECTION: "... nothing. (This can't be an error.)", - _MY_H_SECTION: 'a header this file implements', - _C_SECTION: 'C system header', - _CPP_SECTION: 'C++ system header', - _OTHER_H_SECTION: 'other header', - } - - def __init__(self): - self.include_list = [[]] - self.ResetSection('') - - def FindHeader(self, header): - """Check if a header has already been included. - - Args: - header: header to check. - Returns: - Line number of previous occurrence, or -1 if the header has not - been seen before. - """ - for section_list in self.include_list: - for f in section_list: - if f[0] == header: - return f[1] - return -1 - - def ResetSection(self, directive): - """Reset section checking for preprocessor directive. - - Args: - directive: preprocessor directive (e.g. "if", "else"). - """ - # The name of the current section. - self._section = self._INITIAL_SECTION - # The path of last found header. - self._last_header = '' - - # Update list of includes. Note that we never pop from the - # include list. - if directive in ('if', 'ifdef', 'ifndef'): - self.include_list.append([]) - elif directive in ('else', 'elif'): - self.include_list[-1] = [] - - def SetLastHeader(self, header_path): - self._last_header = header_path - - def CanonicalizeAlphabeticalOrder(self, header_path): - """Returns a path canonicalized for alphabetical comparison. - - - replaces "-" with "_" so they both cmp the same. - - removes '-inl' since we don't require them to be after the main header. - - lowercase everything, just in case. - - Args: - header_path: Path to be canonicalized. - - Returns: - Canonicalized path. - """ - return header_path.replace('-inl.h', '.h').replace('-', '_').lower() - - def IsInAlphabeticalOrder(self, clean_lines, linenum, header_path): - """Check if a header is in alphabetical order with the previous header. - - Args: - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - header_path: Canonicalized header to be checked. - - Returns: - Returns true if the header is in alphabetical order. - """ - # If previous section is different from current section, _last_header will - # be reset to empty string, so it's always less than current header. - # - # If previous line was a blank line, assume that the headers are - # intentionally sorted the way they are. - if (self._last_header > header_path and - Match(r'^\s*#\s*include\b', clean_lines.elided[linenum - 1])): - return False - return True - - def CheckNextIncludeOrder(self, header_type): - """Returns a non-empty error message if the next header is out of order. - - This function also updates the internal state to be ready to check - the next include. - - Args: - header_type: One of the _XXX_HEADER constants defined above. - - Returns: - The empty string if the header is in the right order, or an - error message describing what's wrong. - - """ - error_message = ('Found %s after %s' % ( - self._TYPE_NAMES[header_type], self._SECTION_NAMES[self._section])) - - last_section = self._section - - if header_type == _C_SYS_HEADER: - if self._section <= self._C_SECTION: - self._section = self._C_SECTION - else: - self._last_header = '' - return error_message - elif header_type == _CPP_SYS_HEADER: - if self._section <= self._CPP_SECTION: - self._section = self._CPP_SECTION - else: - self._last_header = '' - return error_message - elif header_type == _LIKELY_MY_HEADER: - if self._section <= self._MY_H_SECTION: - self._section = self._MY_H_SECTION - else: - self._section = self._OTHER_H_SECTION - elif header_type == _POSSIBLE_MY_HEADER: - if self._section <= self._MY_H_SECTION: - self._section = self._MY_H_SECTION - else: - # This will always be the fallback because we're not sure - # enough that the header is associated with this file. - self._section = self._OTHER_H_SECTION - else: - assert header_type == _OTHER_HEADER - self._section = self._OTHER_H_SECTION - - if last_section != self._section: - self._last_header = '' - - return '' - - -class _CppLintState(object): - """Maintains module-wide state..""" - - def __init__(self): - self.verbose_level = 1 # global setting. - self.error_count = 0 # global count of reported errors - # filters to apply when emitting error messages - self.filters = _DEFAULT_FILTERS[:] - # backup of filter list. Used to restore the state after each file. - self._filters_backup = self.filters[:] - self.counting = 'total' # In what way are we counting errors? - self.errors_by_category = {} # string to int dict storing error counts - - # output format: - # "emacs" - format that emacs can parse (default) - # "vs7" - format that Microsoft Visual Studio 7 can parse - self.output_format = 'emacs' - - def SetOutputFormat(self, output_format): - """Sets the output format for errors.""" - self.output_format = output_format - - def SetVerboseLevel(self, level): - """Sets the module's verbosity, and returns the previous setting.""" - last_verbose_level = self.verbose_level - self.verbose_level = level - return last_verbose_level - - def SetCountingStyle(self, counting_style): - """Sets the module's counting options.""" - self.counting = counting_style - - def SetFilters(self, filters): - """Sets the error-message filters. - - These filters are applied when deciding whether to emit a given - error message. - - Args: - filters: A string of comma-separated filters (eg "+whitespace/indent"). - Each filter should start with + or -; else we die. - - Raises: - ValueError: The comma-separated filters did not all start with '+' or '-'. - E.g. "-,+whitespace,-whitespace/indent,whitespace/badfilter" - """ - # Default filters always have less priority than the flag ones. - self.filters = _DEFAULT_FILTERS[:] - self.AddFilters(filters) - - def AddFilters(self, filters): - """ Adds more filters to the existing list of error-message filters. """ - for filt in filters.split(','): - clean_filt = filt.strip() - if clean_filt: - self.filters.append(clean_filt) - for filt in self.filters: - if not (filt.startswith('+') or filt.startswith('-')): - raise ValueError( - 'Every filter in --filters must start with + or -' - ' (%s does not)' % filt) - - def BackupFilters(self): - """ Saves the current filter list to backup storage.""" - self._filters_backup = self.filters[:] - - def RestoreFilters(self): - """ Restores filters previously backed up.""" - self.filters = self._filters_backup[:] - - def ResetErrorCounts(self): - """Sets the module's error statistic back to zero.""" - self.error_count = 0 - self.errors_by_category = {} - - def IncrementErrorCount(self, category): - """Bumps the module's error statistic.""" - self.error_count += 1 - if self.counting in ('toplevel', 'detailed'): - if self.counting != 'detailed': - category = category.split('/')[0] - if category not in self.errors_by_category: - self.errors_by_category[category] = 0 - self.errors_by_category[category] += 1 - - def PrintErrorCounts(self): - """Print a summary of errors by category, and the total.""" - for category, count in self.errors_by_category.iteritems(): - sys.stdout.write('Category \'%s\' errors found: %d\n' % - (category, count)) - sys.stdout.write('Total errors found: %d\n' % self.error_count) - - -_cpplint_state = _CppLintState() - - -def _OutputFormat(): - """Gets the module's output format.""" - return _cpplint_state.output_format - - -def _SetOutputFormat(output_format): - """Sets the module's output format.""" - _cpplint_state.SetOutputFormat(output_format) - - -def _VerboseLevel(): - """Returns the module's verbosity setting.""" - return _cpplint_state.verbose_level - - -def _SetVerboseLevel(level): - """Sets the module's verbosity, and returns the previous setting.""" - return _cpplint_state.SetVerboseLevel(level) - - -def _SetCountingStyle(level): - """Sets the module's counting options.""" - _cpplint_state.SetCountingStyle(level) - - -def _Filters(): - """Returns the module's list of output filters, as a list.""" - return _cpplint_state.filters - - -def _SetFilters(filters): - """Sets the module's error-message filters. - - These filters are applied when deciding whether to emit a given - error message. - - Args: - filters: A string of comma-separated filters (eg "whitespace/indent"). - Each filter should start with + or -; else we die. - """ - _cpplint_state.SetFilters(filters) - - -def _AddFilters(filters): - """Adds more filter overrides. - - Unlike _SetFilters, this function does not reset the current list of filters - available. - - Args: - filters: A string of comma-separated filters (eg "whitespace/indent"). - Each filter should start with + or -; else we die. - """ - _cpplint_state.AddFilters(filters) - - -def _BackupFilters(): - """ Saves the current filter list to backup storage.""" - _cpplint_state.BackupFilters() - - -def _RestoreFilters(): - """ Restores filters previously backed up.""" - _cpplint_state.RestoreFilters() - - -class _FunctionState(object): - """Tracks current function name and the number of lines in its body.""" - - _NORMAL_TRIGGER = 250 # for --v=0, 500 for --v=1, etc. - _TEST_TRIGGER = 400 # about 50% more than _NORMAL_TRIGGER. - - def __init__(self): - self.in_a_function = False - self.lines_in_function = 0 - self.current_function = '' - - def Begin(self, function_name): - """Start analyzing function body. - - Args: - function_name: The name of the function being tracked. - """ - self.in_a_function = True - self.lines_in_function = 0 - self.current_function = function_name - - def Count(self): - """Count line in current function body.""" - if self.in_a_function: - self.lines_in_function += 1 - - def Check(self, error, filename, linenum): - """Report if too many lines in function body. - - Args: - error: The function to call with any errors found. - filename: The name of the current file. - linenum: The number of the line to check. - """ - if Match(r'T(EST|est)', self.current_function): - base_trigger = self._TEST_TRIGGER - else: - base_trigger = self._NORMAL_TRIGGER - trigger = base_trigger * 2**_VerboseLevel() - - if self.lines_in_function > trigger: - error_level = int( - math.log(self.lines_in_function / base_trigger, 2)) - # 50 => 0, 100 => 1, 200 => 2, 400 => 3, 800 => 4, 1600 => 5, ... - if error_level > 5: - error_level = 5 - error(filename, linenum, 'readability/fn_size', error_level, - 'Small and focused functions are preferred:' - ' %s has %d non-comment lines' - ' (error triggered by exceeding %d lines).' % ( - self.current_function, self.lines_in_function, trigger)) - - def End(self): - """Stop analyzing function body.""" - self.in_a_function = False - - -class _IncludeError(Exception): - """Indicates a problem with the include order in a file.""" - pass - - -class FileInfo(object): - """Provides utility functions for filenames. - - FileInfo provides easy access to the components of a file's path - relative to the project root. - """ - - def __init__(self, filename): - self._filename = filename - - def FullName(self): - """Make Windows paths like Unix.""" - return os.path.abspath(self._filename).replace('\\', '/') - - def RepositoryName(self): - """FullName after removing the local path to the repository. - - If we have a real absolute path name here we can try to do something smart: - detecting the root of the checkout and truncating /path/to/checkout from - the name so that we get header guards that don't include things like - "C:\Documents and Settings\..." or "/home/username/..." in them and thus - people on different computers who have checked the source out to different - locations won't see bogus errors. - """ - fullname = self.FullName() - - if os.path.exists(fullname): - project_dir = os.path.dirname(fullname) - - if os.path.exists(os.path.join(project_dir, ".svn")): - # If there's a .svn file in the current directory, we recursively look - # up the directory tree for the top of the SVN checkout - root_dir = project_dir - one_up_dir = os.path.dirname(root_dir) - while os.path.exists(os.path.join(one_up_dir, ".svn")): - root_dir = os.path.dirname(root_dir) - one_up_dir = os.path.dirname(one_up_dir) - - prefix = os.path.commonprefix([root_dir, project_dir]) - return fullname[len(prefix) + 1:] - - # Not SVN <= 1.6? Try to find a git, hg, or svn top level directory by - # searching up from the current path. - root_dir = os.path.dirname(fullname) - while (root_dir != os.path.dirname(root_dir) and - not os.path.exists(os.path.join(root_dir, ".git")) and - not os.path.exists(os.path.join(root_dir, ".hg")) and - not os.path.exists(os.path.join(root_dir, ".svn"))): - root_dir = os.path.dirname(root_dir) - - if (os.path.exists(os.path.join(root_dir, ".git")) or - os.path.exists(os.path.join(root_dir, ".hg")) or - os.path.exists(os.path.join(root_dir, ".svn"))): - prefix = os.path.commonprefix([root_dir, project_dir]) - return fullname[len(prefix) + 1:] - - # Don't know what to do; header guard warnings may be wrong... - return fullname - - def Split(self): - """Splits the file into the directory, basename, and extension. - - For 'chrome/browser/browser.cc', Split() would - return ('chrome/browser', 'browser', '.cc') - - Returns: - A tuple of (directory, basename, extension). - """ - - googlename = self.RepositoryName() - project, rest = os.path.split(googlename) - return (project, ) + os.path.splitext(rest) - - def BaseName(self): - """File base name - text after the final slash, before the final period.""" - return self.Split()[1] - - def Extension(self): - """File extension - text following the final period.""" - return self.Split()[2] - - def NoExtension(self): - """File has no source file extension.""" - return '/'.join(self.Split()[0:2]) - - def IsSource(self): - """File has a source file extension.""" - return self.Extension()[1:] in ('c', 'cc', 'cpp', 'cxx') - - -def _ShouldPrintError(category, confidence, linenum): - """If confidence >= verbose, category passes filter and is not suppressed.""" - - # There are three ways we might decide not to print an error message: - # a "NOLINT(category)" comment appears in the source, - # the verbosity level isn't high enough, or the filters filter it out. - if IsErrorSuppressedByNolint(category, linenum): - return False - - if confidence < _cpplint_state.verbose_level: - return False - - is_filtered = False - for one_filter in _Filters(): - if one_filter.startswith('-'): - if category.startswith(one_filter[1:]): - is_filtered = True - elif one_filter.startswith('+'): - if category.startswith(one_filter[1:]): - is_filtered = False - else: - assert False # should have been checked for in SetFilter. - if is_filtered: - return False - - return True - - -def Error(filename, linenum, category, confidence, message): - """Logs the fact we've found a lint error. - - We log where the error was found, and also our confidence in the error, - that is, how certain we are this is a legitimate style regression, and - not a misidentification or a use that's sometimes justified. - - False positives can be suppressed by the use of - "cpplint(category)" comments on the offending line. These are - parsed into _error_suppressions. - - Args: - filename: The name of the file containing the error. - linenum: The number of the line containing the error. - category: A string used to describe the "category" this bug - falls under: "whitespace", say, or "runtime". Categories - may have a hierarchy separated by slashes: "whitespace/indent". - confidence: A number from 1-5 representing a confidence score for - the error, with 5 meaning that we are certain of the problem, - and 1 meaning that it could be a legitimate construct. - message: The error message. - """ - if _ShouldPrintError(category, confidence, linenum): - _cpplint_state.IncrementErrorCount(category) - if _cpplint_state.output_format == 'vs7': - sys.stderr.write('%s(%s): %s [%s] [%d]\n' % - (filename, linenum, message, category, confidence)) - elif _cpplint_state.output_format == 'eclipse': - sys.stderr.write('%s:%s: warning: %s [%s] [%d]\n' % - (filename, linenum, message, category, confidence)) - else: - sys.stderr.write('%s:%s: %s [%s] [%d]\n' % - (filename, linenum, message, category, confidence)) - - -# Matches standard C++ escape sequences per 2.13.2.3 of the C++ standard. -_RE_PATTERN_CLEANSE_LINE_ESCAPES = re.compile( - r'\\([abfnrtv?"\\\']|\d+|x[0-9a-fA-F]+)') -# Match a single C style comment on the same line. -_RE_PATTERN_C_COMMENTS = r'/\*(?:[^*]|\*(?!/))*\*/' -# Matches multi-line C style comments. -# This RE is a little bit more complicated than one might expect, because we -# have to take care of space removals tools so we can handle comments inside -# statements better. -# The current rule is: We only clear spaces from both sides when we're at the -# end of the line. Otherwise, we try to remove spaces from the right side, -# if this doesn't work we try on left side but only if there's a non-character -# on the right. -_RE_PATTERN_CLEANSE_LINE_C_COMMENTS = re.compile( - r'(\s*' + _RE_PATTERN_C_COMMENTS + r'\s*$|' + _RE_PATTERN_C_COMMENTS + - r'\s+|' + r'\s+' + _RE_PATTERN_C_COMMENTS + r'(?=\W)|' + - _RE_PATTERN_C_COMMENTS + r')') - - -def IsCppString(line): - """Does line terminate so, that the next symbol is in string constant. - - This function does not consider single-line nor multi-line comments. - - Args: - line: is a partial line of code starting from the 0..n. - - Returns: - True, if next character appended to 'line' is inside a - string constant. - """ - - line = line.replace(r'\\', 'XX') # after this, \\" does not match to \" - return ((line.count('"') - line.count(r'\"') - line.count("'\"'")) & 1) == 1 - - -def CleanseRawStrings(raw_lines): - """Removes C++11 raw strings from lines. - - Before: - static const char kData[] = R"( - multi-line string - )"; - - After: - static const char kData[] = "" - (replaced by blank line) - ""; - - Args: - raw_lines: list of raw lines. - - Returns: - list of lines with C++11 raw strings replaced by empty strings. - """ - - delimiter = None - lines_without_raw_strings = [] - for line in raw_lines: - if delimiter: - # Inside a raw string, look for the end - end = line.find(delimiter) - if end >= 0: - # Found the end of the string, match leading space for this - # line and resume copying the original lines, and also insert - # a "" on the last line. - leading_space = Match(r'^(\s*)\S', line) - line = leading_space.group(1) + '""' + line[end + len( - delimiter):] - delimiter = None - else: - # Haven't found the end yet, append a blank line. - line = '""' - - # Look for beginning of a raw string, and replace them with - # empty strings. This is done in a loop to handle multiple raw - # strings on the same line. - while delimiter is None: - # Look for beginning of a raw string. - # See 2.14.15 [lex.string] for syntax. - matched = Match(r'^(.*)\b(?:R|u8R|uR|UR|LR)"([^\s\\()]*)\((.*)$', - line) - if matched: - delimiter = ')' + matched.group(2) + '"' - - end = matched.group(3).find(delimiter) - if end >= 0: - # Raw string ended on same line - line = (matched.group(1) + '""' + - matched.group(3)[end + len(delimiter):]) - delimiter = None - else: - # Start of a multi-line raw string - line = matched.group(1) + '""' - else: - break - - lines_without_raw_strings.append(line) - - # TODO(unknown): if delimiter is not None here, we might want to - # emit a warning for unterminated string. - return lines_without_raw_strings - - -def FindNextMultiLineCommentStart(lines, lineix): - """Find the beginning marker for a multiline comment.""" - while lineix < len(lines): - if lines[lineix].strip().startswith('/*'): - # Only return this marker if the comment goes beyond this line - if lines[lineix].strip().find('*/', 2) < 0: - return lineix - lineix += 1 - return len(lines) - - -def FindNextMultiLineCommentEnd(lines, lineix): - """We are inside a comment, find the end marker.""" - while lineix < len(lines): - if lines[lineix].strip().endswith('*/'): - return lineix - lineix += 1 - return len(lines) - - -def RemoveMultiLineCommentsFromRange(lines, begin, end): - """Clears a range of lines for multi-line comments.""" - # Having // dummy comments makes the lines non-empty, so we will not get - # unnecessary blank line warnings later in the code. - for i in range(begin, end): - lines[i] = '/**/' - - -def RemoveMultiLineComments(filename, lines, error): - """Removes multiline (c-style) comments from lines.""" - lineix = 0 - while lineix < len(lines): - lineix_begin = FindNextMultiLineCommentStart(lines, lineix) - if lineix_begin >= len(lines): - return - lineix_end = FindNextMultiLineCommentEnd(lines, lineix_begin) - if lineix_end >= len(lines): - error(filename, lineix_begin + 1, 'readability/multiline_comment', - 5, 'Could not find end of multi-line comment') - return - RemoveMultiLineCommentsFromRange(lines, lineix_begin, lineix_end + 1) - lineix = lineix_end + 1 - - -def CleanseComments(line): - """Removes //-comments and single-line C-style /* */ comments. - - Args: - line: A line of C++ source. - - Returns: - The line with single-line comments removed. - """ - commentpos = line.find('//') - if commentpos != -1 and not IsCppString(line[:commentpos]): - line = line[:commentpos].rstrip() - # get rid of /* ... */ - return _RE_PATTERN_CLEANSE_LINE_C_COMMENTS.sub('', line) - - -class CleansedLines(object): - """Holds 4 copies of all lines with different preprocessing applied to them. - - 1) elided member contains lines without strings and comments. - 2) lines member contains lines without comments. - 3) raw_lines member contains all the lines without processing. - 4) lines_without_raw_strings member is same as raw_lines, but with C++11 raw - strings removed. - All these members are of , and of the same length. - """ - - def __init__(self, lines): - self.elided = [] - self.lines = [] - self.raw_lines = lines - self.num_lines = len(lines) - self.lines_without_raw_strings = CleanseRawStrings(lines) - for linenum in range(len(self.lines_without_raw_strings)): - self.lines.append( - CleanseComments(self.lines_without_raw_strings[linenum])) - elided = self._CollapseStrings(self.lines_without_raw_strings[ - linenum]) - self.elided.append(CleanseComments(elided)) - - def NumLines(self): - """Returns the number of lines represented.""" - return self.num_lines - - @staticmethod - def _CollapseStrings(elided): - """Collapses strings and chars on a line to simple "" or '' blocks. - - We nix strings first so we're not fooled by text like '"http://"' - - Args: - elided: The line being processed. - - Returns: - The line with collapsed strings. - """ - if _RE_PATTERN_INCLUDE.match(elided): - return elided - - # Remove escaped characters first to make quote/single quote collapsing - # basic. Things that look like escaped characters shouldn't occur - # outside of strings and chars. - elided = _RE_PATTERN_CLEANSE_LINE_ESCAPES.sub('', elided) - - # Replace quoted strings and digit separators. Both single quotes - # and double quotes are processed in the same loop, otherwise - # nested quotes wouldn't work. - collapsed = '' - while True: - # Find the first quote character - match = Match(r'^([^\'"]*)([\'"])(.*)$', elided) - if not match: - collapsed += elided - break - head, quote, tail = match.groups() - - if quote == '"': - # Collapse double quoted strings - second_quote = tail.find('"') - if second_quote >= 0: - collapsed += head + '""' - elided = tail[second_quote + 1:] - else: - # Unmatched double quote, don't bother processing the rest - # of the line since this is probably a multiline string. - collapsed += elided - break - else: - # Found single quote, check nearby text to eliminate digit separators. - # - # There is no special handling for floating point here, because - # the integer/fractional/exponent parts would all be parsed - # correctly as long as there are digits on both sides of the - # separator. So we are fine as long as we don't see something - # like "0.'3" (gcc 4.9.0 will not allow this literal). - if Search(r'\b(?:0[bBxX]?|[1-9])[0-9a-fA-F]*$', head): - match_literal = Match(r'^((?:\'?[0-9a-zA-Z_])*)(.*)$', - "'" + tail) - collapsed += head + match_literal.group(1).replace("'", '') - elided = match_literal.group(2) - else: - second_quote = tail.find('\'') - if second_quote >= 0: - collapsed += head + "''" - elided = tail[second_quote + 1:] - else: - # Unmatched single quote - collapsed += elided - break - - return collapsed - - -def FindEndOfExpressionInLine(line, startpos, stack): - """Find the position just after the end of current parenthesized expression. - - Args: - line: a CleansedLines line. - startpos: start searching at this position. - stack: nesting stack at startpos. - - Returns: - On finding matching end: (index just after matching end, None) - On finding an unclosed expression: (-1, None) - Otherwise: (-1, new stack at end of this line) - """ - for i in xrange(startpos, len(line)): - char = line[i] - if char in '([{': - # Found start of parenthesized expression, push to expression stack - stack.append(char) - elif char == '<': - # Found potential start of template argument list - if i > 0 and line[i - 1] == '<': - # Left shift operator - if stack and stack[-1] == '<': - stack.pop() - if not stack: - return (-1, None) - elif i > 0 and Search(r'\boperator\s*$', line[0:i]): - # operator<, don't add to stack - continue - else: - # Tentative start of template argument list - stack.append('<') - elif char in ')]}': - # Found end of parenthesized expression. - # - # If we are currently expecting a matching '>', the pending '<' - # must have been an operator. Remove them from expression stack. - while stack and stack[-1] == '<': - stack.pop() - if not stack: - return (-1, None) - if ((stack[-1] == '(' and char == ')') or - (stack[-1] == '[' and char == ']') or - (stack[-1] == '{' and char == '}')): - stack.pop() - if not stack: - return (i + 1, None) - else: - # Mismatched parentheses - return (-1, None) - elif char == '>': - # Found potential end of template argument list. - - # Ignore "->" and operator functions - if (i > 0 and (line[i - 1] == '-' or Search(r'\boperator\s*$', - line[0:i - 1]))): - continue - - # Pop the stack if there is a matching '<'. Otherwise, ignore - # this '>' since it must be an operator. - if stack: - if stack[-1] == '<': - stack.pop() - if not stack: - return (i + 1, None) - elif char == ';': - # Found something that look like end of statements. If we are currently - # expecting a '>', the matching '<' must have been an operator, since - # template argument list should not contain statements. - while stack and stack[-1] == '<': - stack.pop() - if not stack: - return (-1, None) - - # Did not find end of expression or unbalanced parentheses on this line - return (-1, stack) - - -def CloseExpression(clean_lines, linenum, pos): - """If input points to ( or { or [ or <, finds the position that closes it. - - If lines[linenum][pos] points to a '(' or '{' or '[' or '<', finds the - linenum/pos that correspond to the closing of the expression. - - TODO(unknown): cpplint spends a fair bit of time matching parentheses. - Ideally we would want to index all opening and closing parentheses once - and have CloseExpression be just a simple lookup, but due to preprocessor - tricks, this is not so easy. - - Args: - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - pos: A position on the line. - - Returns: - A tuple (line, linenum, pos) pointer *past* the closing brace, or - (line, len(lines), -1) if we never find a close. Note we ignore - strings and comments when matching; and the line we return is the - 'cleansed' line at linenum. - """ - - line = clean_lines.elided[linenum] - if (line[pos] not in '({[<') or Match(r'<[<=]', line[pos:]): - return (line, clean_lines.NumLines(), -1) - - # Check first line - (end_pos, stack) = FindEndOfExpressionInLine(line, pos, []) - if end_pos > -1: - return (line, linenum, end_pos) - - # Continue scanning forward - while stack and linenum < clean_lines.NumLines() - 1: - linenum += 1 - line = clean_lines.elided[linenum] - (end_pos, stack) = FindEndOfExpressionInLine(line, 0, stack) - if end_pos > -1: - return (line, linenum, end_pos) - - # Did not find end of expression before end of file, give up - return (line, clean_lines.NumLines(), -1) - - -def FindStartOfExpressionInLine(line, endpos, stack): - """Find position at the matching start of current expression. - - This is almost the reverse of FindEndOfExpressionInLine, but note - that the input position and returned position differs by 1. - - Args: - line: a CleansedLines line. - endpos: start searching at this position. - stack: nesting stack at endpos. - - Returns: - On finding matching start: (index at matching start, None) - On finding an unclosed expression: (-1, None) - Otherwise: (-1, new stack at beginning of this line) - """ - i = endpos - while i >= 0: - char = line[i] - if char in ')]}': - # Found end of expression, push to expression stack - stack.append(char) - elif char == '>': - # Found potential end of template argument list. - # - # Ignore it if it's a "->" or ">=" or "operator>" - if (i > 0 and - (line[i - 1] == '-' or Match(r'\s>=\s', line[i - 1:]) or - Search(r'\boperator\s*$', line[0:i]))): - i -= 1 - else: - stack.append('>') - elif char == '<': - # Found potential start of template argument list - if i > 0 and line[i - 1] == '<': - # Left shift operator - i -= 1 - else: - # If there is a matching '>', we can pop the expression stack. - # Otherwise, ignore this '<' since it must be an operator. - if stack and stack[-1] == '>': - stack.pop() - if not stack: - return (i, None) - elif char in '([{': - # Found start of expression. - # - # If there are any unmatched '>' on the stack, they must be - # operators. Remove those. - while stack and stack[-1] == '>': - stack.pop() - if not stack: - return (-1, None) - if ((char == '(' and stack[-1] == ')') or - (char == '[' and stack[-1] == ']') or - (char == '{' and stack[-1] == '}')): - stack.pop() - if not stack: - return (i, None) - else: - # Mismatched parentheses - return (-1, None) - elif char == ';': - # Found something that look like end of statements. If we are currently - # expecting a '<', the matching '>' must have been an operator, since - # template argument list should not contain statements. - while stack and stack[-1] == '>': - stack.pop() - if not stack: - return (-1, None) - - i -= 1 - - return (-1, stack) - - -def ReverseCloseExpression(clean_lines, linenum, pos): - """If input points to ) or } or ] or >, finds the position that opens it. - - If lines[linenum][pos] points to a ')' or '}' or ']' or '>', finds the - linenum/pos that correspond to the opening of the expression. - - Args: - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - pos: A position on the line. - - Returns: - A tuple (line, linenum, pos) pointer *at* the opening brace, or - (line, 0, -1) if we never find the matching opening brace. Note - we ignore strings and comments when matching; and the line we - return is the 'cleansed' line at linenum. - """ - line = clean_lines.elided[linenum] - if line[pos] not in ')}]>': - return (line, 0, -1) - - # Check last line - (start_pos, stack) = FindStartOfExpressionInLine(line, pos, []) - if start_pos > -1: - return (line, linenum, start_pos) - - # Continue scanning backward - while stack and linenum > 0: - linenum -= 1 - line = clean_lines.elided[linenum] - (start_pos, stack) = FindStartOfExpressionInLine(line, - len(line) - 1, stack) - if start_pos > -1: - return (line, linenum, start_pos) - - # Did not find start of expression before beginning of file, give up - return (line, 0, -1) - - -def CheckForCopyright(filename, lines, error): - """Logs an error if no Copyright message appears at the top of the file.""" - - # We'll say it should occur by line 10. Don't forget there's a - # dummy line at the front. - for line in xrange(1, min(len(lines), 11)): - if re.search(r'Copyright', lines[line], re.I): break - else: # means no copyright line was found - error(filename, 0, 'legal/copyright', 5, 'No copyright message found. ' - 'You should have a line: "Copyright [year] "') - - -def GetIndentLevel(line): - """Return the number of leading spaces in line. - - Args: - line: A string to check. - - Returns: - An integer count of leading spaces, possibly zero. - """ - indent = Match(r'^( *)\S', line) - if indent: - return len(indent.group(1)) - else: - return 0 - - -def GetHeaderGuardCPPVariable(filename): - """Returns the CPP variable that should be used as a header guard. - - Args: - filename: The name of a C++ header file. - - Returns: - The CPP variable that should be used as a header guard in the - named file. - - """ - filename = os.path.basename(filename) - return re.sub(r'[^a-zA-Z0-9]', '_', filename).upper() + '_' - - -def CheckForHeaderGuard(filename, clean_lines, error): - """Checks that the file contains a header guard. - - Logs an error if no #ifndef header guard is present. For other - headers, checks that the full pathname is used. - - Args: - filename: The name of the C++ header file. - clean_lines: A CleansedLines instance containing the file. - error: The function to call with any errors found. - """ - - # Don't check for header guards if there are error suppression - # comments somewhere in this file. - # - # Because this is silencing a warning for a nonexistent line, we - # only support the very specific NOLINT(build/header_guard) syntax, - # and not the general NOLINT or NOLINT(*) syntax. - raw_lines = clean_lines.lines_without_raw_strings - for i in raw_lines: - if Search(r'//\s*NOLINT\(build/header_guard\)', i): - return - - cppvar = GetHeaderGuardCPPVariable(filename) - - ifndef = '' - ifndef_linenum = 0 - define = '' - endif = '' - endif_linenum = 0 - pragma_linenum = -1 - for linenum, line in enumerate(raw_lines): - linesplit = line.split() - if len(linesplit) >= 2: - if linesplit[0] == '#pragma' and linesplit[1] == 'once': - pragma_linenum = linenum - # find the first occurrence of #ifndef and #define, save arg - if not ifndef and linesplit[0] == '#ifndef': - # set ifndef to the header guard presented on the #ifndef line. - ifndef = linesplit[1] - ifndef_linenum = linenum - if not define and linesplit[0] == '#define': - define = linesplit[1] - # find the last occurrence of #endif, save entire line - if line.startswith('#endif'): - endif = line - endif_linenum = linenum - if pragma_linenum != -1: - return # short path for pragma once - if not ifndef or not define or ifndef != define: - error(filename, 0, 'build/header_guard', 5, - 'No #ifndef header guard found, suggested CPP variable is: %s' % - cppvar) - return - - # The guard should be PATH_FILE_H_, but we also allow PATH_FILE_H__ - # for backward compatibility. - if ifndef != cppvar: - error_level = 0 - if ifndef != cppvar + '_': - error_level = 5 - - ParseNolintSuppressions(filename, raw_lines[ifndef_linenum], - ifndef_linenum, error) - error(filename, ifndef_linenum, 'build/header_guard', error_level, - '#ifndef header guard has wrong style, please use: %s' % cppvar) - - # Check for "//" comments on endif line. - ParseNolintSuppressions(filename, raw_lines[endif_linenum], endif_linenum, - error) - match = Match(r'#endif\s*//\s*' + cppvar + r'(_)?\b', endif) - if match: - if match.group(1) == '_': - # Issue low severity warning for deprecated double trailing underscore - error(filename, endif_linenum, 'build/header_guard', 0, - '#endif line should be "#endif // %s"' % cppvar) - return - - # Didn't find the corresponding "//" comment. If this file does not - # contain any "//" comments at all, it could be that the compiler - # only wants "/**/" comments, look for those instead. - no_single_line_comments = True - for i in xrange(1, len(raw_lines) - 1): - line = raw_lines[i] - if Match(r'^(?:(?:\'(?:\.|[^\'])*\')|(?:"(?:\.|[^"])*")|[^\'"])*//', - line): - no_single_line_comments = False - break - - if no_single_line_comments: - match = Match(r'#endif\s*/\*\s*' + cppvar + r'(_)?\s*\*/', endif) - if match: - if match.group(1) == '_': - # Low severity warning for double trailing underscore - error(filename, endif_linenum, 'build/header_guard', 0, - '#endif line should be "#endif /* %s */"' % cppvar) - return - - # Didn't find anything - error(filename, endif_linenum, 'build/header_guard', 5, - '#endif line should be "#endif // %s"' % cppvar) - - -def CheckHeaderFileIncluded(filename, include_state, error): - """Logs an error if a .cc file does not include its header.""" - - # Do not check test files - if filename.endswith('_test.cc') or filename.endswith('_unittest.cc'): - return - - fileinfo = FileInfo(filename) - headerfile = filename[0:len(filename) - 2] + 'h' - if not os.path.exists(headerfile): - return - headername = FileInfo(headerfile).RepositoryName() - first_include = 0 - for section_list in include_state.include_list: - for f in section_list: - if headername in f[0] or f[0] in headername: - return - if not first_include: - first_include = f[1] - - error(filename, first_include, 'build/include', 5, - '%s should include its header file %s' % (fileinfo.RepositoryName(), - headername)) - - -def CheckForBadCharacters(filename, lines, error): - """Logs an error for each line containing bad characters. - - Two kinds of bad characters: - - 1. Unicode replacement characters: These indicate that either the file - contained invalid UTF-8 (likely) or Unicode replacement characters (which - it shouldn't). Note that it's possible for this to throw off line - numbering if the invalid UTF-8 occurred adjacent to a newline. - - 2. NUL bytes. These are problematic for some tools. - - Args: - filename: The name of the current file. - lines: An array of strings, each representing a line of the file. - error: The function to call with any errors found. - """ - for linenum, line in enumerate(lines): - if u'\ufffd' in line: - error( - filename, linenum, 'readability/utf8', 5, - 'Line contains invalid UTF-8 (or Unicode replacement character).' - ) - if '\0' in line: - error(filename, linenum, 'readability/nul', 5, - 'Line contains NUL byte.') - - -def CheckForNewlineAtEOF(filename, lines, error): - """Logs an error if there is no newline char at the end of the file. - - Args: - filename: The name of the current file. - lines: An array of strings, each representing a line of the file. - error: The function to call with any errors found. - """ - - # The array lines() was created by adding two newlines to the - # original file (go figure), then splitting on \n. - # To verify that the file ends in \n, we just have to make sure the - # last-but-two element of lines() exists and is empty. - if len(lines) < 3 or lines[-2]: - error(filename, - len(lines) - 2, 'whitespace/ending_newline', 5, - 'Could not find a newline character at the end of the file.') - - -def CheckForMultilineCommentsAndStrings(filename, clean_lines, linenum, error): - """Logs an error if we see /* ... */ or "..." that extend past one line. - - /* ... */ comments are legit inside macros, for one line. - Otherwise, we prefer // comments, so it's ok to warn about the - other. Likewise, it's ok for strings to extend across multiple - lines, as long as a line continuation character (backslash) - terminates each line. Although not currently prohibited by the C++ - style guide, it's ugly and unnecessary. We don't do well with either - in this lint program, so we warn about both. - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - error: The function to call with any errors found. - """ - line = clean_lines.elided[linenum] - - # Remove all \\ (escaped backslashes) from the line. They are OK, and the - # second (escaped) slash may trigger later \" detection erroneously. - line = line.replace('\\\\', '') - - if line.count('/*') > line.count('*/'): - error(filename, linenum, 'readability/multiline_comment', 5, - 'Complex multi-line /*...*/-style comment found. ' - 'Lint may give bogus warnings. ' - 'Consider replacing these with //-style comments, ' - 'with #if 0...#endif, ' - 'or with more clearly structured multi-line comments.') - - if (line.count('"') - line.count('\\"')) % 2: - error(filename, linenum, 'readability/multiline_string', 5, - 'Multi-line string ("...") found. This lint script doesn\'t ' - 'do well with such strings, and may give bogus warnings. ' - 'Use C++11 raw strings or concatenation instead.') - - -# (non-threadsafe name, thread-safe alternative, validation pattern) -# -# The validation pattern is used to eliminate false positives such as: -# _rand(); // false positive due to substring match. -# ->rand(); // some member function rand(). -# ACMRandom rand(seed); // some variable named rand. -# ISAACRandom rand(); // another variable named rand. -# -# Basically we require the return value of these functions to be used -# in some expression context on the same line by matching on some -# operator before the function name. This eliminates constructors and -# member function calls. -_UNSAFE_FUNC_PREFIX = r'(?:[-+*/=%^&|(<]\s*|>\s+)' -_THREADING_LIST = ( - ('asctime(', 'asctime_r(', _UNSAFE_FUNC_PREFIX + r'asctime\([^)]+\)'), - ('ctime(', 'ctime_r(', _UNSAFE_FUNC_PREFIX + r'ctime\([^)]+\)'), - ('getgrgid(', 'getgrgid_r(', _UNSAFE_FUNC_PREFIX + r'getgrgid\([^)]+\)'), - ('getgrnam(', 'getgrnam_r(', _UNSAFE_FUNC_PREFIX + r'getgrnam\([^)]+\)'), - ('getlogin(', 'getlogin_r(', _UNSAFE_FUNC_PREFIX + r'getlogin\(\)'), - ('getpwnam(', 'getpwnam_r(', _UNSAFE_FUNC_PREFIX + r'getpwnam\([^)]+\)'), - ('getpwuid(', 'getpwuid_r(', _UNSAFE_FUNC_PREFIX + r'getpwuid\([^)]+\)'), - ('gmtime(', 'gmtime_r(', _UNSAFE_FUNC_PREFIX + r'gmtime\([^)]+\)'), - ('localtime(', 'localtime_r(', _UNSAFE_FUNC_PREFIX + r'localtime\([^)]+\)'), - ('rand(', 'rand_r(', _UNSAFE_FUNC_PREFIX + r'rand\(\)'), - ('strtok(', 'strtok_r(', _UNSAFE_FUNC_PREFIX + r'strtok\([^)]+\)'), - ('ttyname(', 'ttyname_r(', _UNSAFE_FUNC_PREFIX + r'ttyname\([^)]+\)'), ) - - -def CheckPosixThreading(filename, clean_lines, linenum, error): - """Checks for calls to thread-unsafe functions. - - Much code has been originally written without consideration of - multi-threading. Also, engineers are relying on their old experience; - they have learned posix before threading extensions were added. These - tests guide the engineers to use thread-safe functions (when using - posix directly). - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - error: The function to call with any errors found. - """ - line = clean_lines.elided[linenum] - for single_thread_func, multithread_safe_func, pattern in _THREADING_LIST: - # Additional pattern matching check to confirm that this is the - # function we are looking for - if Search(pattern, line): - error(filename, linenum, 'runtime/threadsafe_fn', 2, - 'Consider using ' + multithread_safe_func + '...) instead of ' - + single_thread_func + '...) for improved thread safety.') - - -def CheckVlogArguments(filename, clean_lines, linenum, error): - """Checks that VLOG() is only used for defining a logging level. - - For example, VLOG(2) is correct. VLOG(INFO), VLOG(WARNING), VLOG(ERROR), and - VLOG(FATAL) are not. - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - error: The function to call with any errors found. - """ - line = clean_lines.elided[linenum] - if Search(r'\bVLOG\((INFO|ERROR|WARNING|DFATAL|FATAL)\)', line): - error(filename, linenum, 'runtime/vlog', 5, - 'VLOG() should be used with numeric verbosity level. ' - 'Use LOG() if you want symbolic severity levels.') - - -# Matches invalid increment: *count++, which moves pointer instead of -# incrementing a value. -_RE_PATTERN_INVALID_INCREMENT = re.compile(r'^\s*\*\w+(\+\+|--);') - - -def CheckInvalidIncrement(filename, clean_lines, linenum, error): - """Checks for invalid increment *count++. - - For example following function: - void increment_counter(int* count) { - *count++; - } - is invalid, because it effectively does count++, moving pointer, and should - be replaced with ++*count, (*count)++ or *count += 1. - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - error: The function to call with any errors found. - """ - line = clean_lines.elided[linenum] - if _RE_PATTERN_INVALID_INCREMENT.match(line): - error( - filename, linenum, 'runtime/invalid_increment', 5, - 'Changing pointer instead of value (or unused value of operator*).') - - -def IsMacroDefinition(clean_lines, linenum): - if Search(r'^#define', clean_lines[linenum]): - return True - - if linenum > 0 and Search(r'\\$', clean_lines[linenum - 1]): - return True - - return False - - -def IsForwardClassDeclaration(clean_lines, linenum): - return Match(r'^\s*(\btemplate\b)*.*class\s+\w+;\s*$', clean_lines[linenum]) - - -class _BlockInfo(object): - """Stores information about a generic block of code.""" - - def __init__(self, seen_open_brace): - self.seen_open_brace = seen_open_brace - self.open_parentheses = 0 - self.inline_asm = _NO_ASM - self.check_namespace_indentation = False - - def CheckBegin(self, filename, clean_lines, linenum, error): - """Run checks that applies to text up to the opening brace. - - This is mostly for checking the text after the class identifier - and the "{", usually where the base class is specified. For other - blocks, there isn't much to check, so we always pass. - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - error: The function to call with any errors found. - """ - pass - - def CheckEnd(self, filename, clean_lines, linenum, error): - """Run checks that applies to text after the closing brace. - - This is mostly used for checking end of namespace comments. - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - error: The function to call with any errors found. - """ - pass - - def IsBlockInfo(self): - """Returns true if this block is a _BlockInfo. - - This is convenient for verifying that an object is an instance of - a _BlockInfo, but not an instance of any of the derived classes. - - Returns: - True for this class, False for derived classes. - """ - return self.__class__ == _BlockInfo - - -class _ExternCInfo(_BlockInfo): - """Stores information about an 'extern "C"' block.""" - - def __init__(self): - _BlockInfo.__init__(self, True) - - -class _ClassInfo(_BlockInfo): - """Stores information about a class.""" - - def __init__(self, name, class_or_struct, clean_lines, linenum): - _BlockInfo.__init__(self, False) - self.name = name - self.starting_linenum = linenum - self.is_derived = False - self.check_namespace_indentation = True - if class_or_struct == 'struct': - self.access = 'public' - self.is_struct = True - else: - self.access = 'private' - self.is_struct = False - - # Remember initial indentation level for this class. Using raw_lines here - # instead of elided to account for leading comments. - self.class_indent = GetIndentLevel(clean_lines.raw_lines[linenum]) - - # Try to find the end of the class. This will be confused by things like: - # class A { - # } *x = { ... - # - # But it's still good enough for CheckSectionSpacing. - self.last_line = 0 - depth = 0 - for i in range(linenum, clean_lines.NumLines()): - line = clean_lines.elided[i] - depth += line.count('{') - line.count('}') - if not depth: - self.last_line = i - break - - def CheckBegin(self, filename, clean_lines, linenum, error): - # Look for a bare ':' - if Search('(^|[^:]):($|[^:])', clean_lines.elided[linenum]): - self.is_derived = True - - def CheckEnd(self, filename, clean_lines, linenum, error): - # If there is a DISALLOW macro, it should appear near the end of - # the class. - seen_last_thing_in_class = False - for i in xrange(linenum - 1, self.starting_linenum, -1): - match = Search( - r'\b(DISALLOW_COPY_AND_ASSIGN|DISALLOW_IMPLICIT_CONSTRUCTORS)\(' - + self.name + r'\)', clean_lines.elided[i]) - if match: - if seen_last_thing_in_class: - error(filename, i, 'readability/constructors', 3, - match.group(1) + - ' should be the last thing in the class') - break - - if not Match(r'^\s*$', clean_lines.elided[i]): - seen_last_thing_in_class = True - - # Check that closing brace is aligned with beginning of the class. - # Only do this if the closing brace is indented by only whitespaces. - # This means we will not check single-line class definitions. - indent = Match(r'^( *)\}', clean_lines.elided[linenum]) - if indent and len(indent.group(1)) != self.class_indent: - if self.is_struct: - parent = 'struct ' + self.name - else: - parent = 'class ' + self.name - error(filename, linenum, 'whitespace/indent', 3, - 'Closing brace should be aligned with beginning of %s' % - parent) - - -class _NamespaceInfo(_BlockInfo): - """Stores information about a namespace.""" - - def __init__(self, name, linenum): - _BlockInfo.__init__(self, False) - self.name = name or '' - self.starting_linenum = linenum - self.check_namespace_indentation = True - - def CheckEnd(self, filename, clean_lines, linenum, error): - """Check end of namespace comments.""" - line = clean_lines.raw_lines[linenum] - - # Check how many lines is enclosed in this namespace. Don't issue - # warning for missing namespace comments if there aren't enough - # lines. However, do apply checks if there is already an end of - # namespace comment and it's incorrect. - # - # TODO(unknown): We always want to check end of namespace comments - # if a namespace is large, but sometimes we also want to apply the - # check if a short namespace contained nontrivial things (something - # other than forward declarations). There is currently no logic on - # deciding what these nontrivial things are, so this check is - # triggered by namespace size only, which works most of the time. - if (linenum - self.starting_linenum < 10 and - not Match(r'};*\s*(//|/\*).*\bnamespace\b', line)): - return - - # Look for matching comment at end of namespace. - # - # Note that we accept C style "/* */" comments for terminating - # namespaces, so that code that terminate namespaces inside - # preprocessor macros can be cpplint clean. - # - # We also accept stuff like "// end of namespace ." with the - # period at the end. - # - # Besides these, we don't accept anything else, otherwise we might - # get false negatives when existing comment is a substring of the - # expected namespace. - if self.name: - # Named namespace - if not Match((r'};*\s*(//|/\*).*\bnamespace\s+' + - re.escape(self.name) + r'[\*/\.\\\s]*$'), line): - error(filename, linenum, 'readability/namespace', 5, - 'Namespace should be terminated with "// namespace %s"' % - self.name) - else: - # Anonymous namespace - if not Match(r'};*\s*(//|/\*).*\bnamespace[\*/\.\\\s]*$', line): - # If "// namespace anonymous" or "// anonymous namespace (more text)", - # mention "// anonymous namespace" as an acceptable form - if Match(r'}.*\b(namespace anonymous|anonymous namespace)\b', - line): - error( - filename, linenum, 'readability/namespace', 5, - 'Anonymous namespace should be terminated with "// namespace"' - ' or "// anonymous namespace"') - else: - error( - filename, linenum, 'readability/namespace', 5, - 'Anonymous namespace should be terminated with "// namespace"' - ) - - -class _PreprocessorInfo(object): - """Stores checkpoints of nesting stacks when #if/#else is seen.""" - - def __init__(self, stack_before_if): - # The entire nesting stack before #if - self.stack_before_if = stack_before_if - - # The entire nesting stack up to #else - self.stack_before_else = [] - - # Whether we have already seen #else or #elif - self.seen_else = False - - -class NestingState(object): - """Holds states related to parsing braces.""" - - def __init__(self): - # Stack for tracking all braces. An object is pushed whenever we - # see a "{", and popped when we see a "}". Only 3 types of - # objects are possible: - # - _ClassInfo: a class or struct. - # - _NamespaceInfo: a namespace. - # - _BlockInfo: some other type of block. - self.stack = [] - - # Top of the previous stack before each Update(). - # - # Because the nesting_stack is updated at the end of each line, we - # had to do some convoluted checks to find out what is the current - # scope at the beginning of the line. This check is simplified by - # saving the previous top of nesting stack. - # - # We could save the full stack, but we only need the top. Copying - # the full nesting stack would slow down cpplint by ~10%. - self.previous_stack_top = [] - - # Stack of _PreprocessorInfo objects. - self.pp_stack = [] - - def SeenOpenBrace(self): - """Check if we have seen the opening brace for the innermost block. - - Returns: - True if we have seen the opening brace, False if the innermost - block is still expecting an opening brace. - """ - return (not self.stack) or self.stack[-1].seen_open_brace - - def InNamespaceBody(self): - """Check if we are currently one level inside a namespace body. - - Returns: - True if top of the stack is a namespace block, False otherwise. - """ - return self.stack and isinstance(self.stack[-1], _NamespaceInfo) - - def InExternC(self): - """Check if we are currently one level inside an 'extern "C"' block. - - Returns: - True if top of the stack is an extern block, False otherwise. - """ - return self.stack and isinstance(self.stack[-1], _ExternCInfo) - - def InClassDeclaration(self): - """Check if we are currently one level inside a class or struct declaration. - - Returns: - True if top of the stack is a class/struct, False otherwise. - """ - return self.stack and isinstance(self.stack[-1], _ClassInfo) - - def InAsmBlock(self): - """Check if we are currently one level inside an inline ASM block. - - Returns: - True if the top of the stack is a block containing inline ASM. - """ - return self.stack and self.stack[-1].inline_asm != _NO_ASM - - def InTemplateArgumentList(self, clean_lines, linenum, pos): - """Check if current position is inside template argument list. - - Args: - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - pos: position just after the suspected template argument. - Returns: - True if (linenum, pos) is inside template arguments. - """ - while linenum < clean_lines.NumLines(): - # Find the earliest character that might indicate a template argument - line = clean_lines.elided[linenum] - match = Match(r'^[^{};=\[\]\.<>]*(.)', line[pos:]) - if not match: - linenum += 1 - pos = 0 - continue - token = match.group(1) - pos += len(match.group(0)) - - # These things do not look like template argument list: - # class Suspect { - # class Suspect x; } - if token in ('{', '}', ';'): return False - - # These things look like template argument list: - # template - # template - # template - # template - if token in ('>', '=', '[', ']', '.'): return True - - # Check if token is an unmatched '<'. - # If not, move on to the next character. - if token != '<': - pos += 1 - if pos >= len(line): - linenum += 1 - pos = 0 - continue - - # We can't be sure if we just find a single '<', and need to - # find the matching '>'. - (_, end_line, end_pos) = CloseExpression(clean_lines, linenum, - pos - 1) - if end_pos < 0: - # Not sure if template argument list or syntax error in file - return False - linenum = end_line - pos = end_pos - return False - - def UpdatePreprocessor(self, line): - """Update preprocessor stack. - - We need to handle preprocessors due to classes like this: - #ifdef SWIG - struct ResultDetailsPageElementExtensionPoint { - #else - struct ResultDetailsPageElementExtensionPoint : public Extension { - #endif - - We make the following assumptions (good enough for most files): - - Preprocessor condition evaluates to true from #if up to first - #else/#elif/#endif. - - - Preprocessor condition evaluates to false from #else/#elif up - to #endif. We still perform lint checks on these lines, but - these do not affect nesting stack. - - Args: - line: current line to check. - """ - if Match(r'^\s*#\s*(if|ifdef|ifndef)\b', line): - # Beginning of #if block, save the nesting stack here. The saved - # stack will allow us to restore the parsing state in the #else case. - self.pp_stack.append(_PreprocessorInfo(copy.deepcopy(self.stack))) - elif Match(r'^\s*#\s*(else|elif)\b', line): - # Beginning of #else block - if self.pp_stack: - if not self.pp_stack[-1].seen_else: - # This is the first #else or #elif block. Remember the - # whole nesting stack up to this point. This is what we - # keep after the #endif. - self.pp_stack[-1].seen_else = True - self.pp_stack[-1].stack_before_else = copy.deepcopy( - self.stack) - - # Restore the stack to how it was before the #if - self.stack = copy.deepcopy(self.pp_stack[-1].stack_before_if) - else: - # TODO(unknown): unexpected #else, issue warning? - pass - elif Match(r'^\s*#\s*endif\b', line): - # End of #if or #else blocks. - if self.pp_stack: - # If we saw an #else, we will need to restore the nesting - # stack to its former state before the #else, otherwise we - # will just continue from where we left off. - if self.pp_stack[-1].seen_else: - # Here we can just use a shallow copy since we are the last - # reference to it. - self.stack = self.pp_stack[-1].stack_before_else - # Drop the corresponding #if - self.pp_stack.pop() - else: - # TODO(unknown): unexpected #endif, issue warning? - pass - - # TODO(unknown): Update() is too long, but we will refactor later. - def Update(self, filename, clean_lines, linenum, error): - """Update nesting state with current line. - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - error: The function to call with any errors found. - """ - line = clean_lines.elided[linenum] - - # Remember top of the previous nesting stack. - # - # The stack is always pushed/popped and not modified in place, so - # we can just do a shallow copy instead of copy.deepcopy. Using - # deepcopy would slow down cpplint by ~28%. - if self.stack: - self.previous_stack_top = self.stack[-1] - else: - self.previous_stack_top = None - - # Update pp_stack - self.UpdatePreprocessor(line) - - # Count parentheses. This is to avoid adding struct arguments to - # the nesting stack. - if self.stack: - inner_block = self.stack[-1] - depth_change = line.count('(') - line.count(')') - inner_block.open_parentheses += depth_change - - # Also check if we are starting or ending an inline assembly block. - if inner_block.inline_asm in (_NO_ASM, _END_ASM): - if (depth_change != 0 and inner_block.open_parentheses == 1 and - _MATCH_ASM.match(line)): - # Enter assembly block - inner_block.inline_asm = _INSIDE_ASM - else: - # Not entering assembly block. If previous line was _END_ASM, - # we will now shift to _NO_ASM state. - inner_block.inline_asm = _NO_ASM - elif (inner_block.inline_asm == _INSIDE_ASM and - inner_block.open_parentheses == 0): - # Exit assembly block - inner_block.inline_asm = _END_ASM - - # Consume namespace declaration at the beginning of the line. Do - # this in a loop so that we catch same line declarations like this: - # namespace proto2 { namespace bridge { class MessageSet; } } - while True: - # Match start of namespace. The "\b\s*" below catches namespace - # declarations even if it weren't followed by a whitespace, this - # is so that we don't confuse our namespace checker. The - # missing spaces will be flagged by CheckSpacing. - namespace_decl_match = Match(r'^\s*namespace\b\s*([:\w]+)?(.*)$', - line) - if not namespace_decl_match: - break - - new_namespace = _NamespaceInfo( - namespace_decl_match.group(1), linenum) - self.stack.append(new_namespace) - - line = namespace_decl_match.group(2) - if line.find('{') != -1: - new_namespace.seen_open_brace = True - line = line[line.find('{') + 1:] - - # Look for a class declaration in whatever is left of the line - # after parsing namespaces. The regexp accounts for decorated classes - # such as in: - # class LOCKABLE API Object { - # }; - class_decl_match = Match( - r'^(\s*(?:template\s*<[\w\s<>,:]*>\s*)?' - r'(class|struct)\s+(?:[A-Z_]+\s+)*(\w+(?:::\w+)*))' - r'(.*)$', line) - if (class_decl_match and - (not self.stack or self.stack[-1].open_parentheses == 0)): - # We do not want to accept classes that are actually template arguments: - # template , - # template class Ignore3> - # void Function() {}; - # - # To avoid template argument cases, we scan forward and look for - # an unmatched '>'. If we see one, assume we are inside a - # template argument list. - end_declaration = len(class_decl_match.group(1)) - if not self.InTemplateArgumentList(clean_lines, linenum, - end_declaration): - self.stack.append( - _ClassInfo( - class_decl_match.group(3), - class_decl_match.group(2), clean_lines, linenum)) - line = class_decl_match.group(4) - - # If we have not yet seen the opening brace for the innermost block, - # run checks here. - if not self.SeenOpenBrace(): - self.stack[-1].CheckBegin(filename, clean_lines, linenum, error) - - # Update access control if we are inside a class/struct - if self.stack and isinstance(self.stack[-1], _ClassInfo): - classinfo = self.stack[-1] - access_match = Match( - r'^(.*)\b(public|private|protected|signals)(\s+(?:slots\s*)?)?' - r':(?:[^:]|$)', line) - if access_match: - classinfo.access = access_match.group(2) - - # Check that access keywords are indented +1 space. Skip this - # check if the keywords are not preceded by whitespaces. - indent = access_match.group(1) - if (len(indent) != classinfo.class_indent + 1 and - Match(r'^\s*$', indent)): - if classinfo.is_struct: - parent = 'struct ' + classinfo.name - else: - parent = 'class ' + classinfo.name - slots = '' - if access_match.group(3): - slots = access_match.group(3) - error(filename, linenum, 'whitespace/indent', 3, - '%s%s: should be indented +1 space inside %s' % ( - access_match.group(2), slots, parent)) - - # Consume braces or semicolons from what's left of the line - while True: - # Match first brace, semicolon, or closed parenthesis. - matched = Match(r'^[^{;)}]*([{;)}])(.*)$', line) - if not matched: - break - - token = matched.group(1) - if token == '{': - # If namespace or class hasn't seen a opening brace yet, mark - # namespace/class head as complete. Push a new block onto the - # stack otherwise. - if not self.SeenOpenBrace(): - self.stack[-1].seen_open_brace = True - elif Match(r'^extern\s*"[^"]*"\s*\{', line): - self.stack.append(_ExternCInfo()) - else: - self.stack.append(_BlockInfo(True)) - if _MATCH_ASM.match(line): - self.stack[-1].inline_asm = _BLOCK_ASM - - elif token == ';' or token == ')': - # If we haven't seen an opening brace yet, but we already saw - # a semicolon, this is probably a forward declaration. Pop - # the stack for these. - # - # Similarly, if we haven't seen an opening brace yet, but we - # already saw a closing parenthesis, then these are probably - # function arguments with extra "class" or "struct" keywords. - # Also pop these stack for these. - if not self.SeenOpenBrace(): - self.stack.pop() - else: # token == '}' - # Perform end of block checks and pop the stack. - if self.stack: - self.stack[-1].CheckEnd(filename, clean_lines, linenum, - error) - self.stack.pop() - line = matched.group(2) - - def InnermostClass(self): - """Get class info on the top of the stack. - - Returns: - A _ClassInfo object if we are inside a class, or None otherwise. - """ - for i in range(len(self.stack), 0, -1): - classinfo = self.stack[i - 1] - if isinstance(classinfo, _ClassInfo): - return classinfo - return None - - def CheckCompletedBlocks(self, filename, error): - """Checks that all classes and namespaces have been completely parsed. - - Call this when all lines in a file have been processed. - Args: - filename: The name of the current file. - error: The function to call with any errors found. - """ - # Note: This test can result in false positives if #ifdef constructs - # get in the way of brace matching. See the testBuildClass test in - # cpplint_unittest.py for an example of this. - for obj in self.stack: - if isinstance(obj, _ClassInfo): - error(filename, obj.starting_linenum, 'build/class', 5, - 'Failed to find complete declaration of class %s' % - obj.name) - elif isinstance(obj, _NamespaceInfo): - error(filename, obj.starting_linenum, 'build/namespaces', 5, - 'Failed to find complete declaration of namespace %s' % - obj.name) - - -def CheckForNonStandardConstructs(filename, clean_lines, linenum, nesting_state, - error): - r"""Logs an error if we see certain non-ANSI constructs ignored by gcc-2. - - Complain about several constructs which gcc-2 accepts, but which are - not standard C++. Warning about these in lint is one way to ease the - transition to new compilers. - - put storage class first (e.g. "static const" instead of "const static"). - - "%lld" instead of %qd" in printf-type functions. - - "%1$d" is non-standard in printf-type functions. - - "\%" is an undefined character escape sequence. - - text after #endif is not allowed. - - invalid inner-style forward declaration. - - >? and ?= and )\?=?\s*(\w+|[+-]?\d+)(\.\d*)?', - line): - error( - filename, linenum, 'build/deprecated', 3, - '>? and ))?' - # r'\s*const\s*' + type_name + '\s*&\s*\w+\s*;' - error(filename, linenum, 'runtime/member_string_references', 2, - 'const string& members are dangerous. It is much better to use ' - 'alternatives, such as pointers or simple constants.') - - # Everything else in this function operates on class declarations. - # Return early if the top of the nesting stack is not a class, or if - # the class head is not completed yet. - classinfo = nesting_state.InnermostClass() - if not classinfo or not classinfo.seen_open_brace: - return - - # The class may have been declared with namespace or classname qualifiers. - # The constructor and destructor will not have those qualifiers. - base_classname = classinfo.name.split('::')[-1] - - # Look for single-argument constructors that aren't marked explicit. - # Technically a valid construct, but against style. Also look for - # non-single-argument constructors which are also technically valid, but - # strongly suggest something is wrong. - explicit_constructor_match = Match( - r'\s+(?:inline\s+)?(explicit\s+)?(?:inline\s+)?%s\s*' - r'\(((?:[^()]|\([^()]*\))*)\)' % re.escape(base_classname), line) - - if explicit_constructor_match: - is_marked_explicit = explicit_constructor_match.group(1) - - if not explicit_constructor_match.group(2): - constructor_args = [] - else: - constructor_args = explicit_constructor_match.group(2).split(',') - - # collapse arguments so that commas in template parameter lists and function - # argument parameter lists don't split arguments in two - i = 0 - while i < len(constructor_args): - constructor_arg = constructor_args[i] - while (constructor_arg.count('<') > constructor_arg.count('>') or - constructor_arg.count('(') > constructor_arg.count(')')): - constructor_arg += ',' + constructor_args[i + 1] - del constructor_args[i + 1] - constructor_args[i] = constructor_arg - i += 1 - - defaulted_args = [arg for arg in constructor_args if '=' in arg] - noarg_constructor = ( - not constructor_args or # empty arg list - # 'void' arg specifier - (len(constructor_args) == 1 and - constructor_args[0].strip() == 'void')) - onearg_constructor = ( - ( - len(constructor_args) == 1 and # exactly one arg - not noarg_constructor) or - # all but at most one arg defaulted - (len(constructor_args) >= 1 and not noarg_constructor and - len(defaulted_args) >= len(constructor_args) - 1)) - initializer_list_constructor = bool( - onearg_constructor and - Search(r'\bstd\s*::\s*initializer_list\b', constructor_args[0])) - copy_constructor = bool( - onearg_constructor and - Match(r'(const\s+)?%s(\s*<[^>]*>)?(\s+const)?\s*(?:<\w+>\s*)?&' % - re.escape(base_classname), constructor_args[0].strip())) - - if (not is_marked_explicit and onearg_constructor and - not initializer_list_constructor and not copy_constructor): - if defaulted_args: - error(filename, linenum, 'runtime/explicit', 5, - 'Constructors callable with one argument ' - 'should be marked explicit.') - else: - error( - filename, linenum, 'runtime/explicit', 5, - 'Single-parameter constructors should be marked explicit.') - elif is_marked_explicit and not onearg_constructor: - if noarg_constructor: - error( - filename, linenum, 'runtime/explicit', 5, - 'Zero-parameter constructors should not be marked explicit.') - else: - error(filename, linenum, 'runtime/explicit', 0, - 'Constructors that require multiple arguments ' - 'should not be marked explicit.') - - -def CheckSpacingForFunctionCall(filename, clean_lines, linenum, error): - """Checks for the correctness of various spacing around function calls. - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - error: The function to call with any errors found. - """ - line = clean_lines.elided[linenum] - - # Since function calls often occur inside if/for/while/switch - # expressions - which have their own, more liberal conventions - we - # first see if we should be looking inside such an expression for a - # function call, to which we can apply more strict standards. - fncall = line # if there's no control flow construct, look at whole line - for pattern in (r'\bif\s*\((.*)\)\s*{', r'\bfor\s*\((.*)\)\s*{', - r'\bwhile\s*\((.*)\)\s*[{;]', r'\bswitch\s*\((.*)\)\s*{'): - match = Search(pattern, line) - if match: - fncall = match.group(1) # look inside the parens for function calls - break - - # Except in if/for/while/switch, there should never be space - # immediately inside parens (eg "f( 3, 4 )"). We make an exception - # for nested parens ( (a+b) + c ). Likewise, there should never be - # a space before a ( when it's a function argument. I assume it's a - # function argument when the char before the whitespace is legal in - # a function name (alnum + _) and we're not starting a macro. Also ignore - # pointers and references to arrays and functions coz they're too tricky: - # we use a very simple way to recognize these: - # " (something)(maybe-something)" or - # " (something)(maybe-something," or - # " (something)[something]" - # Note that we assume the contents of [] to be short enough that - # they'll never need to wrap. - if ( # Ignore control structures. - not Search( - r'\b(if|for|while|switch|return|new|delete|catch|sizeof)\b', - fncall) and - # Ignore pointers/references to functions. - not Search(r' \([^)]+\)\([^)]*(\)|,$)', fncall) and - # Ignore pointers/references to arrays. - not Search(r' \([^)]+\)\[[^\]]+\]', fncall)): - if Search(r'\w\s*\(\s(?!\s*\\$)', fncall): # a ( used for a fn call - error(filename, linenum, 'whitespace/parens', 4, - 'Extra space after ( in function call') - elif Search(r'\(\s+(?!(\s*\\)|\()', fncall): - error(filename, linenum, 'whitespace/parens', 2, - 'Extra space after (') - if (Search(r'\w\s+\(', fncall) and - not Search(r'#\s*define|typedef|using\s+\w+\s*=', fncall) and - not Search(r'\w\s+\((\w+::)*\*\w+\)\(', fncall) and - not Search(r'\bcase\s+\(', fncall)): - # TODO(unknown): Space after an operator function seem to be a common - # error, silence those for now by restricting them to highest verbosity. - if Search(r'\boperator_*\b', line): - error(filename, linenum, 'whitespace/parens', 0, - 'Extra space before ( in function call') - else: - error(filename, linenum, 'whitespace/parens', 4, - 'Extra space before ( in function call') - # If the ) is followed only by a newline or a { + newline, assume it's - # part of a control statement (if/while/etc), and don't complain - if Search(r'[^)]\s+\)\s*[^{\s]', fncall): - # If the closing parenthesis is preceded by only whitespaces, - # try to give a more descriptive error message. - if Search(r'^\s+\)', fncall): - error(filename, linenum, 'whitespace/parens', 2, - 'Closing ) should be moved to the previous line') - else: - error(filename, linenum, 'whitespace/parens', 2, - 'Extra space before )') - - -def IsBlankLine(line): - """Returns true if the given line is blank. - - We consider a line to be blank if the line is empty or consists of - only white spaces. - - Args: - line: A line of a string. - - Returns: - True, if the given line is blank. - """ - return not line or line.isspace() - - -def CheckForNamespaceIndentation(filename, nesting_state, clean_lines, line, - error): - is_namespace_indent_item = ( - len(nesting_state.stack) > 1 and - nesting_state.stack[-1].check_namespace_indentation and - isinstance(nesting_state.previous_stack_top, _NamespaceInfo) and - nesting_state.previous_stack_top == nesting_state.stack[-2]) - - if ShouldCheckNamespaceIndentation(nesting_state, is_namespace_indent_item, - clean_lines.elided, line): - CheckItemIndentationInNamespace(filename, clean_lines.elided, line, - error) - - -def CheckForFunctionLengths(filename, clean_lines, linenum, function_state, - error): - """Reports for long function bodies. - - For an overview why this is done, see: - http://google-styleguide.googlecode.com/svn/trunk/cppguide.xml#Write_Short_Functions - - Uses a simplistic algorithm assuming other style guidelines - (especially spacing) are followed. - Only checks unindented functions, so class members are unchecked. - Trivial bodies are unchecked, so constructors with huge initializer lists - may be missed. - Blank/comment lines are not counted so as to avoid encouraging the removal - of vertical space and comments just to get through a lint check. - NOLINT *on the last line of a function* disables this check. - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - function_state: Current function name and lines in body so far. - error: The function to call with any errors found. - """ - lines = clean_lines.lines - line = lines[linenum] - joined_line = '' - - starting_func = False - regexp = r'(\w(\w|::|\*|\&|\s)*)\(' # decls * & space::name( ... - match_result = Match(regexp, line) - if match_result: - # If the name is all caps and underscores, figure it's a macro and - # ignore it, unless it's TEST or TEST_F. - function_name = match_result.group(1).split()[-1] - if function_name == 'TEST' or function_name == 'TEST_F' or ( - not Match(r'[A-Z_]+$', function_name)): - starting_func = True - - if starting_func: - body_found = False - for start_linenum in xrange(linenum, clean_lines.NumLines()): - start_line = lines[start_linenum] - joined_line += ' ' + start_line.lstrip() - if Search(r'(;|})', - start_line): # Declarations and trivial functions - body_found = True - break # ... ignore - elif Search(r'{', start_line): - body_found = True - function = Search(r'((\w|:)*)\(', line).group(1) - if Match(r'TEST', function): # Handle TEST... macros - parameter_regexp = Search(r'(\(.*\))', joined_line) - if parameter_regexp: # Ignore bad syntax - function += parameter_regexp.group(1) - else: - function += '()' - function_state.Begin(function) - break - if not body_found: - # No body for the function (or evidence of a non-function) was found. - error(filename, linenum, 'readability/fn_size', 5, - 'Lint failed to find start of function body.') - elif Match(r'^\}\s*$', line): # function end - function_state.Check(error, filename, linenum) - function_state.End() - elif not Match(r'^\s*$', line): - function_state.Count() # Count non-blank/non-comment lines. - - -_RE_PATTERN_TODO = re.compile(r'^//(\s*)TODO(\(.+?\))?:?(\s|$)?') - - -def CheckComment(line, filename, linenum, next_line_start, error): - """Checks for common mistakes in comments. - - Args: - line: The line in question. - filename: The name of the current file. - linenum: The number of the line to check. - next_line_start: The first non-whitespace column of the next line. - error: The function to call with any errors found. - """ - commentpos = line.find('//') - if commentpos != -1: - # Check if the // may be in quotes. If so, ignore it - # Comparisons made explicit for clarity -- pylint: disable=g-explicit-bool-comparison - if (line.count('"', 0, commentpos) - line.count('\\"', 0, commentpos) - ) % 2 == 0: # not in quotes - # Allow one space for new scopes, two spaces otherwise: - if (not (Match(r'^.*{ *//', line) and next_line_start == commentpos) - and ((commentpos >= 1 and - line[commentpos - 1] not in string.whitespace) or - (commentpos >= 2 and - line[commentpos - 2] not in string.whitespace))): - error(filename, linenum, 'whitespace/comments', 2, - 'At least two spaces is best between code and comments') - - # Checks for common mistakes in TODO comments. - comment = line[commentpos:] - match = _RE_PATTERN_TODO.match(comment) - if match: - # One whitespace is correct; zero whitespace is handled elsewhere. - leading_whitespace = match.group(1) - if len(leading_whitespace) > 1: - error(filename, linenum, 'whitespace/todo', 2, - 'Too many spaces before TODO') - - username = match.group(2) - if not username: - error(filename, linenum, 'readability/todo', 2, - 'Missing username in TODO; it should look like ' - '"// TODO(my_username): Stuff."') - - middle_whitespace = match.group(3) - # Comparisons made explicit for correctness -- pylint: disable=g-explicit-bool-comparison - if middle_whitespace != ' ' and middle_whitespace != '': - error(filename, linenum, 'whitespace/todo', 2, - 'TODO(my_username) should be followed by a space') - - # If the comment contains an alphanumeric character, there - # should be a space somewhere between it and the // unless - # it's a /// or //! Doxygen comment. - if (Match(r'//[^ ]*\w', comment) and - not Match(r'(///|//\!)(\s+|$)', comment)): - error(filename, linenum, 'whitespace/comments', 4, - 'Should have a space between // and comment') - - -def CheckAccess(filename, clean_lines, linenum, nesting_state, error): - """Checks for improper use of DISALLOW* macros. - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - nesting_state: A NestingState instance which maintains information about - the current stack of nested blocks being parsed. - error: The function to call with any errors found. - """ - line = clean_lines.elided[linenum] # get rid of comments and strings - - matched = Match((r'\s*(DISALLOW_COPY_AND_ASSIGN|' - r'DISALLOW_IMPLICIT_CONSTRUCTORS)'), line) - if not matched: - return - if nesting_state.stack and isinstance(nesting_state.stack[-1], _ClassInfo): - if nesting_state.stack[-1].access != 'private': - error(filename, linenum, 'readability/constructors', 3, - '%s must be in the private: section' % matched.group(1)) - - else: - # Found DISALLOW* macro outside a class declaration, or perhaps it - # was used inside a function when it should have been part of the - # class declaration. We could issue a warning here, but it - # probably resulted in a compiler error already. - pass - - -def CheckSpacing(filename, clean_lines, linenum, nesting_state, error): - """Checks for the correctness of various spacing issues in the code. - - Things we check for: spaces around operators, spaces after - if/for/while/switch, no spaces around parens in function calls, two - spaces between code and comment, don't start a block with a blank - line, don't end a function with a blank line, don't add a blank line - after public/protected/private, don't have too many blank lines in a row. - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - nesting_state: A NestingState instance which maintains information about - the current stack of nested blocks being parsed. - error: The function to call with any errors found. - """ - - # Don't use "elided" lines here, otherwise we can't check commented lines. - # Don't want to use "raw" either, because we don't want to check inside C++11 - # raw strings, - raw = clean_lines.lines_without_raw_strings - line = raw[linenum] - - # Before nixing comments, check if the line is blank for no good - # reason. This includes the first line after a block is opened, and - # blank lines at the end of a function (ie, right before a line like '}' - # - # Skip all the blank line checks if we are immediately inside a - # namespace body. In other words, don't issue blank line warnings - # for this block: - # namespace { - # - # } - # - # A warning about missing end of namespace comments will be issued instead. - # - # Also skip blank line checks for 'extern "C"' blocks, which are formatted - # like namespaces. - if (IsBlankLine(line) and not nesting_state.InNamespaceBody() and - not nesting_state.InExternC()): - elided = clean_lines.elided - prev_line = elided[linenum - 1] - prevbrace = prev_line.rfind('{') - # TODO(unknown): Don't complain if line before blank line, and line after, - # both start with alnums and are indented the same amount. - # This ignores whitespace at the start of a namespace block - # because those are not usually indented. - if prevbrace != -1 and prev_line[prevbrace:].find('}') == -1: - # OK, we have a blank line at the start of a code block. Before we - # complain, we check if it is an exception to the rule: The previous - # non-empty line has the parameters of a function header that are indented - # 4 spaces (because they did not fit in a 80 column line when placed on - # the same line as the function name). We also check for the case where - # the previous line is indented 6 spaces, which may happen when the - # initializers of a constructor do not fit into a 80 column line. - exception = False - if Match(r' {6}\w', prev_line): # Initializer list? - # We are looking for the opening column of initializer list, which - # should be indented 4 spaces to cause 6 space indentation afterwards. - search_position = linenum - 2 - while (search_position >= 0 and - Match(r' {6}\w', elided[search_position])): - search_position -= 1 - exception = (search_position >= 0 and - elided[search_position][:5] == ' :') - else: - # Search for the function arguments or an initializer list. We use a - # simple heuristic here: If the line is indented 4 spaces; and we have a - # closing paren, without the opening paren, followed by an opening brace - # or colon (for initializer lists) we assume that it is the last line of - # a function header. If we have a colon indented 4 spaces, it is an - # initializer list. - exception = (Match(r' {4}\w[^\(]*\)\s*(const\s*)?(\{\s*$|:)', - prev_line) or Match(r' {4}:', prev_line)) - - if not exception: - error(filename, linenum, 'whitespace/blank_line', 2, - 'Redundant blank line at the start of a code block ' - 'should be deleted.') - # Ignore blank lines at the end of a block in a long if-else - # chain, like this: - # if (condition1) { - # // Something followed by a blank line - # - # } else if (condition2) { - # // Something else - # } - if linenum + 1 < clean_lines.NumLines(): - next_line = raw[linenum + 1] - if (next_line and Match(r'\s*}', next_line) and - next_line.find('} else ') == -1): - error(filename, linenum, 'whitespace/blank_line', 3, - 'Redundant blank line at the end of a code block ' - 'should be deleted.') - - matched = Match(r'\s*(public|protected|private):', prev_line) - if matched: - error(filename, linenum, 'whitespace/blank_line', 3, - 'Do not leave a blank line after "%s:"' % matched.group(1)) - - # Next, check comments - next_line_start = 0 - if linenum + 1 < clean_lines.NumLines(): - next_line = raw[linenum + 1] - next_line_start = len(next_line) - len(next_line.lstrip()) - CheckComment(line, filename, linenum, next_line_start, error) - - # get rid of comments and strings - line = clean_lines.elided[linenum] - - # You shouldn't have spaces before your brackets, except maybe after - # 'delete []' or 'return []() {};' - if Search(r'\w\s+\[', line) and not Search(r'(?:delete|return)\s+\[', line): - error(filename, linenum, 'whitespace/braces', 5, 'Extra space before [') - - # In range-based for, we wanted spaces before and after the colon, but - # not around "::" tokens that might appear. - if (Search(r'for *\(.*[^:]:[^: ]', line) or - Search(r'for *\(.*[^: ]:[^:]', line)): - error(filename, linenum, 'whitespace/forcolon', 2, - 'Missing space around colon in range-based for loop') - - -def CheckOperatorSpacing(filename, clean_lines, linenum, error): - """Checks for horizontal spacing around operators. - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - error: The function to call with any errors found. - """ - line = clean_lines.elided[linenum] - - # Don't try to do spacing checks for operator methods. Do this by - # replacing the troublesome characters with something else, - # preserving column position for all other characters. - # - # The replacement is done repeatedly to avoid false positives from - # operators that call operators. - while True: - match = Match(r'^(.*\boperator\b)(\S+)(\s*\(.*)$', line) - if match: - line = match.group(1) + ('_' * len(match.group(2))) + match.group(3) - else: - break - - # We allow no-spaces around = within an if: "if ( (a=Foo()) == 0 )". - # Otherwise not. Note we only check for non-spaces on *both* sides; - # sometimes people put non-spaces on one side when aligning ='s among - # many lines (not that this is behavior that I approve of...) - if ((Search(r'[\w.]=', line) or - Search(r'=[\w.]', line)) and not Search(r'\b(if|while|for) ', line) - # Operators taken from [lex.operators] in C++11 standard. - and - not Search(r'(>=|<=|==|!=|&=|\^=|\|=|\+=|\*=|\/=|\%=)', line) and - not Search(r'operator=', line)): - error(filename, linenum, 'whitespace/operators', 4, - 'Missing spaces around =') - - # It's ok not to have spaces around binary operators like + - * /, but if - # there's too little whitespace, we get concerned. It's hard to tell, - # though, so we punt on this one for now. TODO. - - # You should always have whitespace around binary operators. - # - # Check <= and >= first to avoid false positives with < and >, then - # check non-include lines for spacing around < and >. - # - # If the operator is followed by a comma, assume it's be used in a - # macro context and don't do any checks. This avoids false - # positives. - # - # Note that && is not included here. Those are checked separately - # in CheckRValueReference - match = Search(r'[^<>=!\s](==|!=|<=|>=|\|\|)[^<>=!\s,;\)]', line) - if match: - error(filename, linenum, 'whitespace/operators', 3, - 'Missing spaces around %s' % match.group(1)) - elif not Match(r'#.*include', line): - # Look for < that is not surrounded by spaces. This is only - # triggered if both sides are missing spaces, even though - # technically should should flag if at least one side is missing a - # space. This is done to avoid some false positives with shifts. - match = Match(r'^(.*[^\s<])<[^\s=<,]', line) - if match: - (_, _, end_pos) = CloseExpression(clean_lines, linenum, - len(match.group(1))) - if end_pos <= -1: - error(filename, linenum, 'whitespace/operators', 3, - 'Missing spaces around <') - - # Look for > that is not surrounded by spaces. Similar to the - # above, we only trigger if both sides are missing spaces to avoid - # false positives with shifts. - match = Match(r'^(.*[^-\s>])>[^\s=>,]', line) - if match: - (_, _, start_pos) = ReverseCloseExpression(clean_lines, linenum, - len(match.group(1))) - if start_pos <= -1: - error(filename, linenum, 'whitespace/operators', 3, - 'Missing spaces around >') - - # We allow no-spaces around << when used like this: 10<<20, but - # not otherwise (particularly, not when used as streams) - # - # We also allow operators following an opening parenthesis, since - # those tend to be macros that deal with operators. - match = Search(r'(operator|[^\s(<])(?:L|UL|ULL|l|ul|ull)?<<([^\s,=<])', - line) - if (match and - not (match.group(1).isdigit() and match.group(2).isdigit()) and - not (match.group(1) == 'operator' and match.group(2) == ';')): - error(filename, linenum, 'whitespace/operators', 3, - 'Missing spaces around <<') - - # We allow no-spaces around >> for almost anything. This is because - # C++11 allows ">>" to close nested templates, which accounts for - # most cases when ">>" is not followed by a space. - # - # We still warn on ">>" followed by alpha character, because that is - # likely due to ">>" being used for right shifts, e.g.: - # value >> alpha - # - # When ">>" is used to close templates, the alphanumeric letter that - # follows would be part of an identifier, and there should still be - # a space separating the template type and the identifier. - # type> alpha - match = Search(r'>>[a-zA-Z_]', line) - if match: - error(filename, linenum, 'whitespace/operators', 3, - 'Missing spaces around >>') - - # There shouldn't be space around unary operators - match = Search(r'(!\s|~\s|[\s]--[\s;]|[\s]\+\+[\s;])', line) - if match: - error(filename, linenum, 'whitespace/operators', 4, - 'Extra space for operator %s' % match.group(1)) - - -def CheckParenthesisSpacing(filename, clean_lines, linenum, error): - """Checks for horizontal spacing around parentheses. - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - error: The function to call with any errors found. - """ - line = clean_lines.elided[linenum] - - # No spaces after an if, while, switch, or for - match = Search(r' (if\(|for\(|while\(|switch\()', line) - if match: - error(filename, linenum, 'whitespace/parens', 5, - 'Missing space before ( in %s' % match.group(1)) - - # For if/for/while/switch, the left and right parens should be - # consistent about how many spaces are inside the parens, and - # there should either be zero or one spaces inside the parens. - # We don't want: "if ( foo)" or "if ( foo )". - # Exception: "for ( ; foo; bar)" and "for (foo; bar; )" are allowed. - match = Search(r'\b(if|for|while|switch)\s*' - r'\(([ ]*)(.).*[^ ]+([ ]*)\)\s*{\s*$', line) - if match: - if len(match.group(2)) != len(match.group(4)): - if not (match.group(3) == ';' and - len(match.group(2)) == 1 + len(match.group(4)) or - not match.group(2) and Search(r'\bfor\s*\(.*; \)', line)): - error(filename, linenum, 'whitespace/parens', 5, - 'Mismatching spaces inside () in %s' % match.group(1)) - if len(match.group(2)) not in [0, 1]: - error(filename, linenum, 'whitespace/parens', 5, - 'Should have zero or one spaces inside ( and ) in %s' % - match.group(1)) - - -def CheckCommaSpacing(filename, clean_lines, linenum, error): - """Checks for horizontal spacing near commas and semicolons. - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - error: The function to call with any errors found. - """ - raw = clean_lines.lines_without_raw_strings - line = clean_lines.elided[linenum] - - # You should always have a space after a comma (either as fn arg or operator) - # - # This does not apply when the non-space character following the - # comma is another comma, since the only time when that happens is - # for empty macro arguments. - # - # We run this check in two passes: first pass on elided lines to - # verify that lines contain missing whitespaces, second pass on raw - # lines to confirm that those missing whitespaces are not due to - # elided comments. - if (Search(r',[^,\s]', ReplaceAll(r'\boperator\s*,\s*\(', 'F(', line)) and - Search(r',[^,\s]', raw[linenum])): - error(filename, linenum, 'whitespace/comma', 3, 'Missing space after ,') - - # You should always have a space after a semicolon - # except for few corner cases - # TODO(unknown): clarify if 'if (1) { return 1;}' is requires one more - # space after ; - if Search(r';[^\s};\\)/]', line): - error(filename, linenum, 'whitespace/semicolon', 3, - 'Missing space after ;') - - -def CheckBracesSpacing(filename, clean_lines, linenum, error): - """Checks for horizontal spacing near commas. - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - error: The function to call with any errors found. - """ - line = clean_lines.elided[linenum] - - # Except after an opening paren, or after another opening brace (in case of - # an initializer list, for instance), you should have spaces before your - # braces. And since you should never have braces at the beginning of a line, - # this is an easy test. - match = Match(r'^(.*[^ ({>]){', line) - if match: - # Try a bit harder to check for brace initialization. This - # happens in one of the following forms: - # Constructor() : initializer_list_{} { ... } - # Constructor{}.MemberFunction() - # Type variable{}; - # FunctionCall(type{}, ...); - # LastArgument(..., type{}); - # LOG(INFO) << type{} << " ..."; - # map_of_type[{...}] = ...; - # ternary = expr ? new type{} : nullptr; - # OuterTemplate{}> - # - # We check for the character following the closing brace, and - # silence the warning if it's one of those listed above, i.e. - # "{.;,)<>]:". - # - # To account for nested initializer list, we allow any number of - # closing braces up to "{;,)<". We can't simply silence the - # warning on first sight of closing brace, because that would - # cause false negatives for things that are not initializer lists. - # Silence this: But not this: - # Outer{ if (...) { - # Inner{...} if (...){ // Missing space before { - # }; } - # - # There is a false negative with this approach if people inserted - # spurious semicolons, e.g. "if (cond){};", but we will catch the - # spurious semicolon with a separate check. - (endline, endlinenum, endpos) = CloseExpression(clean_lines, linenum, - len(match.group(1))) - trailing_text = '' - if endpos > -1: - trailing_text = endline[endpos:] - for offset in xrange(endlinenum + 1, - min(endlinenum + 3, clean_lines.NumLines() - 1)): - trailing_text += clean_lines.elided[offset] - if not Match(r'^[\s}]*[{.;,)<>\]:]', trailing_text): - error(filename, linenum, 'whitespace/braces', 5, - 'Missing space before {') - - # Make sure '} else {' has spaces. - if Search(r'}else', line): - error(filename, linenum, 'whitespace/braces', 5, - 'Missing space before else') - - # You shouldn't have a space before a semicolon at the end of the line. - # There's a special case for "for" since the style guide allows space before - # the semicolon there. - if Search(r':\s*;\s*$', line): - error(filename, linenum, 'whitespace/semicolon', 5, - 'Semicolon defining empty statement. Use {} instead.') - elif Search(r'^\s*;\s*$', line): - error( - filename, linenum, 'whitespace/semicolon', 5, - 'Line contains only semicolon. If this should be an empty statement, ' - 'use {} instead.') - elif (Search(r'\s+;\s*$', line) and not Search(r'\bfor\b', line)): - error(filename, linenum, 'whitespace/semicolon', 5, - 'Extra space before last semicolon. If this should be an empty ' - 'statement, use {} instead.') - - -def IsDecltype(clean_lines, linenum, column): - """Check if the token ending on (linenum, column) is decltype(). - - Args: - clean_lines: A CleansedLines instance containing the file. - linenum: the number of the line to check. - column: end column of the token to check. - Returns: - True if this token is decltype() expression, False otherwise. - """ - (text, _, start_col) = ReverseCloseExpression(clean_lines, linenum, column) - if start_col < 0: - return False - if Search(r'\bdecltype\s*$', text[0:start_col]): - return True - return False - - -def IsTemplateParameterList(clean_lines, linenum, column): - """Check if the token ending on (linenum, column) is the end of template<>. - - Args: - clean_lines: A CleansedLines instance containing the file. - linenum: the number of the line to check. - column: end column of the token to check. - Returns: - True if this token is end of a template parameter list, False otherwise. - """ - (_, startline, startpos) = ReverseCloseExpression(clean_lines, linenum, - column) - if (startpos > -1 and Search(r'\btemplate\s*$', - clean_lines.elided[startline][0:startpos])): - return True - return False - - -def IsRValueType(typenames, clean_lines, nesting_state, linenum, column): - """Check if the token ending on (linenum, column) is a type. - - Assumes that text to the right of the column is "&&" or a function - name. - - Args: - typenames: set of type names from template-argument-list. - clean_lines: A CleansedLines instance containing the file. - nesting_state: A NestingState instance which maintains information about - the current stack of nested blocks being parsed. - linenum: the number of the line to check. - column: end column of the token to check. - Returns: - True if this token is a type, False if we are not sure. - """ - prefix = clean_lines.elided[linenum][0:column] - - # Get one word to the left. If we failed to do so, this is most - # likely not a type, since it's unlikely that the type name and "&&" - # would be split across multiple lines. - match = Match(r'^(.*)(\b\w+|[>*)&])\s*$', prefix) - if not match: - return False - - # Check text following the token. If it's "&&>" or "&&," or "&&...", it's - # most likely a rvalue reference used inside a template. - suffix = clean_lines.elided[linenum][column:] - if Match(r'&&\s*(?:[>,]|\.\.\.)', suffix): - return True - - # Check for known types and end of templates: - # int&& variable - # vector&& variable - # - # Because this function is called recursively, we also need to - # recognize pointer and reference types: - # int* Function() - # int& Function() - if (match.group(2) in typenames or match.group(2) in [ - 'char', 'char16_t', 'char32_t', 'wchar_t', 'bool', 'short', 'int', - 'long', 'signed', 'unsigned', 'float', 'double', 'void', 'auto', - '>', '*', '&' - ]): - return True - - # If we see a close parenthesis, look for decltype on the other side. - # decltype would unambiguously identify a type, anything else is - # probably a parenthesized expression and not a type. - if match.group(2) == ')': - return IsDecltype(clean_lines, linenum, - len(match.group(1)) + len(match.group(2)) - 1) - - # Check for casts and cv-qualifiers. - # match.group(1) remainder - # -------------- --------- - # const_cast< type&& - # const type&& - # type const&& - if Search(r'\b(?:const_cast\s*<|static_cast\s*<|dynamic_cast\s*<|' - r'reinterpret_cast\s*<|\w+\s)\s*$', match.group(1)): - return True - - # Look for a preceding symbol that might help differentiate the context. - # These are the cases that would be ambiguous: - # match.group(1) remainder - # -------------- --------- - # Call ( expression && - # Declaration ( type&& - # sizeof ( type&& - # if ( expression && - # while ( expression && - # for ( type&& - # for( ; expression && - # statement ; type&& - # block { type&& - # constructor { expression && - start = linenum - line = match.group(1) - match_symbol = None - while start >= 0: - # We want to skip over identifiers and commas to get to a symbol. - # Commas are skipped so that we can find the opening parenthesis - # for function parameter lists. - match_symbol = Match(r'^(.*)([^\w\s,])[\w\s,]*$', line) - if match_symbol: - break - start -= 1 - line = clean_lines.elided[start] - - if not match_symbol: - # Probably the first statement in the file is an rvalue reference - return True - - if match_symbol.group(2) == '}': - # Found closing brace, probably an indicate of this: - # block{} type&& - return True - - if match_symbol.group(2) == ';': - # Found semicolon, probably one of these: - # for(; expression && - # statement; type&& - - # Look for the previous 'for(' in the previous lines. - before_text = match_symbol.group(1) - for i in xrange(start - 1, max(start - 6, 0), -1): - before_text = clean_lines.elided[i] + before_text - if Search(r'for\s*\([^{};]*$', before_text): - # This is the condition inside a for-loop - return False - - # Did not find a for-init-statement before this semicolon, so this - # is probably a new statement and not a condition. - return True - - if match_symbol.group(2) == '{': - # Found opening brace, probably one of these: - # block{ type&& = ... ; } - # constructor{ expression && expression } - - # Look for a closing brace or a semicolon. If we see a semicolon - # first, this is probably a rvalue reference. - line = clean_lines.elided[start][0:len(match_symbol.group(1)) + 1] - end = start - depth = 1 - while True: - for ch in line: - if ch == ';': - return True - elif ch == '{': - depth += 1 - elif ch == '}': - depth -= 1 - if depth == 0: - return False - end += 1 - if end >= clean_lines.NumLines(): - break - line = clean_lines.elided[end] - # Incomplete program? - return False - - if match_symbol.group(2) == '(': - # Opening parenthesis. Need to check what's to the left of the - # parenthesis. Look back one extra line for additional context. - before_text = match_symbol.group(1) - if linenum > 1: - before_text = clean_lines.elided[linenum - 1] + before_text - before_text = match_symbol.group(1) - - # Patterns that are likely to be types: - # [](type&& - # for (type&& - # sizeof(type&& - # operator=(type&& - # - if Search(r'(?:\]|\bfor|\bsizeof|\boperator\s*\S+\s*)\s*$', - before_text): - return True - - # Patterns that are likely to be expressions: - # if (expression && - # while (expression && - # : initializer(expression && - # , initializer(expression && - # ( FunctionCall(expression && - # + FunctionCall(expression && - # + (expression && - # - # The last '+' represents operators such as '+' and '-'. - if Search(r'(?:\bif|\bwhile|[-+=%^(]*>)?\s*$', - match_symbol.group(1)) - if match_func: - # Check for constructors, which don't have return types. - if Search(r'\b(?:explicit|inline)$', match_func.group(1)): - return True - implicit_constructor = Match(r'\s*(\w+)\((?:const\s+)?(\w+)', - prefix) - if (implicit_constructor and implicit_constructor.group(1) == - implicit_constructor.group(2)): - return True - return IsRValueType(typenames, clean_lines, nesting_state, linenum, - len(match_func.group(1))) - - # Nothing before the function name. If this is inside a block scope, - # this is probably a function call. - return not (nesting_state.previous_stack_top and - nesting_state.previous_stack_top.IsBlockInfo()) - - if match_symbol.group(2) == '>': - # Possibly a closing bracket, check that what's on the other side - # looks like the start of a template. - return IsTemplateParameterList(clean_lines, start, - len(match_symbol.group(1))) - - # Some other symbol, usually something like "a=b&&c". This is most - # likely not a type. - return False - - -def IsDeletedOrDefault(clean_lines, linenum): - """Check if current constructor or operator is deleted or default. - - Args: - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - Returns: - True if this is a deleted or default constructor. - """ - open_paren = clean_lines.elided[linenum].find('(') - if open_paren < 0: - return False - (close_line, _, close_paren) = CloseExpression(clean_lines, linenum, - open_paren) - if close_paren < 0: - return False - return Match(r'\s*=\s*(?:delete|default)\b', close_line[close_paren:]) - - -def IsRValueAllowed(clean_lines, linenum, typenames): - """Check if RValue reference is allowed on a particular line. - - Args: - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - typenames: set of type names from template-argument-list. - Returns: - True if line is within the region where RValue references are allowed. - """ - # Allow region marked by PUSH/POP macros - for i in xrange(linenum, 0, -1): - line = clean_lines.elided[i] - if Match(r'GOOGLE_ALLOW_RVALUE_REFERENCES_(?:PUSH|POP)', line): - if not line.endswith('PUSH'): - return False - for j in xrange(linenum, clean_lines.NumLines(), 1): - line = clean_lines.elided[j] - if Match(r'GOOGLE_ALLOW_RVALUE_REFERENCES_(?:PUSH|POP)', line): - return line.endswith('POP') - - # Allow operator= - line = clean_lines.elided[linenum] - if Search(r'\boperator\s*=\s*\(', line): - return IsDeletedOrDefault(clean_lines, linenum) - - # Allow constructors - match = Match(r'\s*(?:[\w<>]+::)*([\w<>]+)\s*::\s*([\w<>]+)\s*\(', line) - if match and match.group(1) == match.group(2): - return IsDeletedOrDefault(clean_lines, linenum) - if Search(r'\b(?:explicit|inline)\s+[\w<>]+\s*\(', line): - return IsDeletedOrDefault(clean_lines, linenum) - - if Match(r'\s*[\w<>]+\s*\(', line): - previous_line = 'ReturnType' - if linenum > 0: - previous_line = clean_lines.elided[linenum - 1] - if Match(r'^\s*$', previous_line) or Search(r'[{}:;]\s*$', - previous_line): - return IsDeletedOrDefault(clean_lines, linenum) - - # Reject types not mentioned in template-argument-list - while line: - match = Match(r'^.*?(\w+)\s*&&(.*)$', line) - if not match: - break - if match.group(1) not in typenames: - return False - line = match.group(2) - - # All RValue types that were in template-argument-list should have - # been removed by now. Those were allowed, assuming that they will - # be forwarded. - # - # If there are no remaining RValue types left (i.e. types that were - # not found in template-argument-list), flag those as not allowed. - return line.find('&&') < 0 - - -def GetTemplateArgs(clean_lines, linenum): - """Find list of template arguments associated with this function declaration. - - Args: - clean_lines: A CleansedLines instance containing the file. - linenum: Line number containing the start of the function declaration, - usually one line after the end of the template-argument-list. - Returns: - Set of type names, or empty set if this does not appear to have - any template parameters. - """ - # Find start of function - func_line = linenum - while func_line > 0: - line = clean_lines.elided[func_line] - if Match(r'^\s*$', line): - return set() - if line.find('(') >= 0: - break - func_line -= 1 - if func_line == 0: - return set() - - # Collapse template-argument-list into a single string - argument_list = '' - match = Match(r'^(\s*template\s*)<', clean_lines.elided[func_line]) - if match: - # template-argument-list on the same line as function name - start_col = len(match.group(1)) - _, end_line, end_col = CloseExpression(clean_lines, func_line, - start_col) - if end_col > -1 and end_line == func_line: - start_col += 1 # Skip the opening bracket - argument_list = clean_lines.elided[func_line][start_col:end_col] - - elif func_line > 1: - # template-argument-list one line before function name - match = Match(r'^(.*)>\s*$', clean_lines.elided[func_line - 1]) - if match: - end_col = len(match.group(1)) - _, start_line, start_col = ReverseCloseExpression( - clean_lines, func_line - 1, end_col) - if start_col > -1: - start_col += 1 # Skip the opening bracket - while start_line < func_line - 1: - argument_list += clean_lines.elided[start_line][start_col:] - start_col = 0 - start_line += 1 - argument_list += clean_lines.elided[func_line - 1][start_col: - end_col] - - if not argument_list: - return set() - - # Extract type names - typenames = set() - while True: - match = Match(r'^[,\s]*(?:typename|class)(?:\.\.\.)?\s+(\w+)(.*)$', - argument_list) - if not match: - break - typenames.add(match.group(1)) - argument_list = match.group(2) - return typenames - - -def CheckRValueReference(filename, clean_lines, linenum, nesting_state, error): - """Check for rvalue references. - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - nesting_state: A NestingState instance which maintains information about - the current stack of nested blocks being parsed. - error: The function to call with any errors found. - """ - # Find lines missing spaces around &&. - # TODO(unknown): currently we don't check for rvalue references - # with spaces surrounding the && to avoid false positives with - # boolean expressions. - line = clean_lines.elided[linenum] - match = Match(r'^(.*\S)&&', line) - if not match: - match = Match(r'(.*)&&\S', line) - if (not match) or '(&&)' in line or Search(r'\boperator\s*$', - match.group(1)): - return - - # Either poorly formed && or an rvalue reference, check the context - # to get a more accurate error message. Mostly we want to determine - # if what's to the left of "&&" is a type or not. - typenames = GetTemplateArgs(clean_lines, linenum) - and_pos = len(match.group(1)) - if IsRValueType(typenames, clean_lines, nesting_state, linenum, and_pos): - if not IsRValueAllowed(clean_lines, linenum, typenames): - error(filename, linenum, 'build/c++11', 3, - 'RValue references are an unapproved C++ feature.') - else: - error(filename, linenum, 'whitespace/operators', 3, - 'Missing spaces around &&') - - -def CheckSectionSpacing(filename, clean_lines, class_info, linenum, error): - """Checks for additional blank line issues related to sections. - - Currently the only thing checked here is blank line before protected/private. - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - class_info: A _ClassInfo objects. - linenum: The number of the line to check. - error: The function to call with any errors found. - """ - # Skip checks if the class is small, where small means 25 lines or less. - # 25 lines seems like a good cutoff since that's the usual height of - # terminals, and any class that can't fit in one screen can't really - # be considered "small". - # - # Also skip checks if we are on the first line. This accounts for - # classes that look like - # class Foo { public: ... }; - # - # If we didn't find the end of the class, last_line would be zero, - # and the check will be skipped by the first condition. - if (class_info.last_line - class_info.starting_linenum <= 24 or - linenum <= class_info.starting_linenum): - return - - matched = Match(r'\s*(public|protected|private):', - clean_lines.lines[linenum]) - if matched: - # Issue warning if the line before public/protected/private was - # not a blank line, but don't do this if the previous line contains - # "class" or "struct". This can happen two ways: - # - We are at the beginning of the class. - # - We are forward-declaring an inner class that is semantically - # private, but needed to be public for implementation reasons. - # Also ignores cases where the previous line ends with a backslash as can be - # common when defining classes in C macros. - prev_line = clean_lines.lines[linenum - 1] - if (not IsBlankLine(prev_line) and - not Search(r'\b(class|struct)\b', prev_line) and - not Search(r'\\$', prev_line)): - # Try a bit harder to find the beginning of the class. This is to - # account for multi-line base-specifier lists, e.g.: - # class Derived - # : public Base { - end_class_head = class_info.starting_linenum - for i in range(class_info.starting_linenum, linenum): - if Search(r'\{\s*$', clean_lines.lines[i]): - end_class_head = i - break - if end_class_head < linenum - 1: - error(filename, linenum, 'whitespace/blank_line', 3, - '"%s:" should be preceded by a blank line' % - matched.group(1)) - - -def GetPreviousNonBlankLine(clean_lines, linenum): - """Return the most recent non-blank line and its line number. - - Args: - clean_lines: A CleansedLines instance containing the file contents. - linenum: The number of the line to check. - - Returns: - A tuple with two elements. The first element is the contents of the last - non-blank line before the current line, or the empty string if this is the - first non-blank line. The second is the line number of that line, or -1 - if this is the first non-blank line. - """ - - prevlinenum = linenum - 1 - while prevlinenum >= 0: - prevline = clean_lines.elided[prevlinenum] - if not IsBlankLine(prevline): # if not a blank line... - return (prevline, prevlinenum) - prevlinenum -= 1 - return ('', -1) - - -def CheckBraces(filename, clean_lines, linenum, error): - """Looks for misplaced braces (e.g. at the end of line). - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - error: The function to call with any errors found. - """ - - line = clean_lines.elided[linenum] # get rid of comments and strings - - if Match(r'\s*{\s*$', line): - # We allow an open brace to start a line in the case where someone is using - # braces in a block to explicitly create a new scope, which is commonly used - # to control the lifetime of stack-allocated variables. Braces are also - # used for brace initializers inside function calls. We don't detect this - # perfectly: we just don't complain if the last non-whitespace character on - # the previous non-blank line is ',', ';', ':', '(', '{', or '}', or if the - # previous line starts a preprocessor block. - prevline = GetPreviousNonBlankLine(clean_lines, linenum)[0] - if (not Search(r'[,;:}{(]\s*$', prevline) and - not Match(r'\s*#', prevline)): - error(filename, linenum, 'whitespace/braces', 4, - '{ should almost always be at the end of the previous line') - - # An else clause should be on the same line as the preceding closing brace. - if Match(r'\s*else\b\s*(?:if\b|\{|$)', line): - prevline = GetPreviousNonBlankLine(clean_lines, linenum)[0] - if Match(r'\s*}\s*$', prevline): - error(filename, linenum, 'whitespace/newline', 4, - 'An else should appear on the same line as the preceding }') - - # If braces come on one side of an else, they should be on both. - # However, we have to worry about "else if" that spans multiple lines! - if Search(r'else if\s*\(', line): # could be multi-line if - brace_on_left = bool(Search(r'}\s*else if\s*\(', line)) - # find the ( after the if - pos = line.find('else if') - pos = line.find('(', pos) - if pos > 0: - (endline, _, endpos) = CloseExpression(clean_lines, linenum, pos) - brace_on_right = endline[endpos:].find('{') != -1 - if brace_on_left != brace_on_right: # must be brace after if - error( - filename, linenum, 'readability/braces', 5, - 'If an else has a brace on one side, it should have it on both' - ) - elif Search(r'}\s*else[^{]*$', line) or Match(r'[^}]*else\s*{', line): - error(filename, linenum, 'readability/braces', 5, - 'If an else has a brace on one side, it should have it on both') - - # Likewise, an else should never have the else clause on the same line - if Search(r'\belse [^\s{]', line) and not Search(r'\belse if\b', line): - error(filename, linenum, 'whitespace/newline', 4, - 'Else clause should never be on same line as else (use 2 lines)') - - # In the same way, a do/while should never be on one line - if Match(r'\s*do [^\s{]', line): - error(filename, linenum, 'whitespace/newline', 4, - 'do/while clauses should not be on a single line') - - # Check single-line if/else bodies. The style guide says 'curly braces are not - # required for single-line statements'. We additionally allow multi-line, - # single statements, but we reject anything with more than one semicolon in - # it. This means that the first semicolon after the if should be at the end of - # its line, and the line after that should have an indent level equal to or - # lower than the if. We also check for ambiguous if/else nesting without - # braces. - if_else_match = Search(r'\b(if\s*\(|else\b)', line) - if if_else_match and not Match(r'\s*#', line): - if_indent = GetIndentLevel(line) - endline, endlinenum, endpos = line, linenum, if_else_match.end() - if_match = Search(r'\bif\s*\(', line) - if if_match: - # This could be a multiline if condition, so find the end first. - pos = if_match.end() - 1 - (endline, endlinenum, endpos) = CloseExpression(clean_lines, - linenum, pos) - # Check for an opening brace, either directly after the if or on the next - # line. If found, this isn't a single-statement conditional. - if (not Match(r'\s*{', endline[endpos:]) and - not (Match(r'\s*$', endline[endpos:]) and endlinenum < - (len(clean_lines.elided) - 1) and - Match(r'\s*{', clean_lines.elided[endlinenum + 1]))): - while (endlinenum < len(clean_lines.elided) and - ';' not in clean_lines.elided[endlinenum][endpos:]): - endlinenum += 1 - endpos = 0 - if endlinenum < len(clean_lines.elided): - endline = clean_lines.elided[endlinenum] - # We allow a mix of whitespace and closing braces (e.g. for one-liner - # methods) and a single \ after the semicolon (for macros) - endpos = endline.find(';') - if not Match(r';[\s}]*(\\?)$', endline[endpos:]): - # Semicolon isn't the last character, there's something trailing. - # Output a warning if the semicolon is not contained inside - # a lambda expression. - if not Match( - r'^[^{};]*\[[^\[\]]*\][^{}]*\{[^{}]*\}\s*\)*[;,]\s*$', - endline): - error( - filename, linenum, 'readability/braces', 4, - 'If/else bodies with multiple statements require braces' - ) - elif endlinenum < len(clean_lines.elided) - 1: - # Make sure the next line is dedented - next_line = clean_lines.elided[endlinenum + 1] - next_indent = GetIndentLevel(next_line) - # With ambiguous nested if statements, this will error out on the - # if that *doesn't* match the else, regardless of whether it's the - # inner one or outer one. - if (if_match and Match(r'\s*else\b', next_line) and - next_indent != if_indent): - error( - filename, linenum, 'readability/braces', 4, - 'Else clause should be indented at the same level as if. ' - 'Ambiguous nested if/else chains require braces.') - elif next_indent > if_indent: - error( - filename, linenum, 'readability/braces', 4, - 'If/else bodies with multiple statements require braces' - ) - - -def CheckTrailingSemicolon(filename, clean_lines, linenum, error): - """Looks for redundant trailing semicolon. - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - error: The function to call with any errors found. - """ - - line = clean_lines.elided[linenum] - - # Block bodies should not be followed by a semicolon. Due to C++11 - # brace initialization, there are more places where semicolons are - # required than not, so we use a whitelist approach to check these - # rather than a blacklist. These are the places where "};" should - # be replaced by just "}": - # 1. Some flavor of block following closing parenthesis: - # for (;;) {}; - # while (...) {}; - # switch (...) {}; - # Function(...) {}; - # if (...) {}; - # if (...) else if (...) {}; - # - # 2. else block: - # if (...) else {}; - # - # 3. const member function: - # Function(...) const {}; - # - # 4. Block following some statement: - # x = 42; - # {}; - # - # 5. Block at the beginning of a function: - # Function(...) { - # {}; - # } - # - # Note that naively checking for the preceding "{" will also match - # braces inside multi-dimensional arrays, but this is fine since - # that expression will not contain semicolons. - # - # 6. Block following another block: - # while (true) {} - # {}; - # - # 7. End of namespaces: - # namespace {}; - # - # These semicolons seems far more common than other kinds of - # redundant semicolons, possibly due to people converting classes - # to namespaces. For now we do not warn for this case. - # - # Try matching case 1 first. - match = Match(r'^(.*\)\s*)\{', line) - if match: - # Matched closing parenthesis (case 1). Check the token before the - # matching opening parenthesis, and don't warn if it looks like a - # macro. This avoids these false positives: - # - macro that defines a base class - # - multi-line macro that defines a base class - # - macro that defines the whole class-head - # - # But we still issue warnings for macros that we know are safe to - # warn, specifically: - # - TEST, TEST_F, TEST_P, MATCHER, MATCHER_P - # - TYPED_TEST - # - INTERFACE_DEF - # - EXCLUSIVE_LOCKS_REQUIRED, SHARED_LOCKS_REQUIRED, LOCKS_EXCLUDED: - # - # We implement a whitelist of safe macros instead of a blacklist of - # unsafe macros, even though the latter appears less frequently in - # google code and would have been easier to implement. This is because - # the downside for getting the whitelist wrong means some extra - # semicolons, while the downside for getting the blacklist wrong - # would result in compile errors. - # - # In addition to macros, we also don't want to warn on - # - Compound literals - # - Lambdas - # - alignas specifier with anonymous structs: - closing_brace_pos = match.group(1).rfind(')') - opening_parenthesis = ReverseCloseExpression(clean_lines, linenum, - closing_brace_pos) - if opening_parenthesis[2] > -1: - line_prefix = opening_parenthesis[0][0:opening_parenthesis[2]] - macro = Search(r'\b([A-Z_]+)\s*$', line_prefix) - func = Match(r'^(.*\])\s*$', line_prefix) - if ((macro and macro.group(1) not in - ('TEST', 'TEST_F', 'MATCHER', 'MATCHER_P', 'TYPED_TEST', - 'EXCLUSIVE_LOCKS_REQUIRED', 'SHARED_LOCKS_REQUIRED', - 'LOCKS_EXCLUDED', 'INTERFACE_DEF')) or - (func and not Search(r'\boperator\s*\[\s*\]', func.group(1))) or - Search(r'\b(?:struct|union)\s+alignas\s*$', line_prefix) or - Search(r'\s+=\s*$', line_prefix)): - match = None - if (match and opening_parenthesis[1] > 1 and Search( - r'\]\s*$', clean_lines.elided[opening_parenthesis[1] - 1])): - # Multi-line lambda-expression - match = None - - else: - # Try matching cases 2-3. - match = Match(r'^(.*(?:else|\)\s*const)\s*)\{', line) - if not match: - # Try matching cases 4-6. These are always matched on separate lines. - # - # Note that we can't simply concatenate the previous line to the - # current line and do a single match, otherwise we may output - # duplicate warnings for the blank line case: - # if (cond) { - # // blank line - # } - prevline = GetPreviousNonBlankLine(clean_lines, linenum)[0] - if prevline and Search(r'[;{}]\s*$', prevline): - match = Match(r'^(\s*)\{', line) - - # Check matching closing brace - if match: - (endline, endlinenum, endpos) = CloseExpression(clean_lines, linenum, - len(match.group(1))) - if endpos > -1 and Match(r'^\s*;', endline[endpos:]): - # Current {} pair is eligible for semicolon check, and we have found - # the redundant semicolon, output warning here. - # - # Note: because we are scanning forward for opening braces, and - # outputting warnings for the matching closing brace, if there are - # nested blocks with trailing semicolons, we will get the error - # messages in reversed order. - error(filename, endlinenum, 'readability/braces', 4, - "You don't need a ; after a }") - - -def CheckEmptyBlockBody(filename, clean_lines, linenum, error): - """Look for empty loop/conditional body with only a single semicolon. - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - error: The function to call with any errors found. - """ - - # Search for loop keywords at the beginning of the line. Because only - # whitespaces are allowed before the keywords, this will also ignore most - # do-while-loops, since those lines should start with closing brace. - # - # We also check "if" blocks here, since an empty conditional block - # is likely an error. - line = clean_lines.elided[linenum] - matched = Match(r'\s*(for|while|if)\s*\(', line) - if matched: - # Find the end of the conditional expression - (end_line, end_linenum, end_pos) = CloseExpression(clean_lines, linenum, - line.find('(')) - - # Output warning if what follows the condition expression is a semicolon. - # No warning for all other cases, including whitespace or newline, since we - # have a separate check for semicolons preceded by whitespace. - if end_pos >= 0 and Match(r';', end_line[end_pos:]): - if matched.group(1) == 'if': - error(filename, end_linenum, - 'whitespace/empty_conditional_body', 5, - 'Empty conditional bodies should use {}') - else: - error(filename, end_linenum, 'whitespace/empty_loop_body', 5, - 'Empty loop bodies should use {} or continue') - - -def FindCheckMacro(line): - """Find a replaceable CHECK-like macro. - - Args: - line: line to search on. - Returns: - (macro name, start position), or (None, -1) if no replaceable - macro is found. - """ - for macro in _CHECK_MACROS: - i = line.find(macro) - if i >= 0: - # Find opening parenthesis. Do a regular expression match here - # to make sure that we are matching the expected CHECK macro, as - # opposed to some other macro that happens to contain the CHECK - # substring. - matched = Match(r'^(.*\b' + macro + r'\s*)\(', line) - if not matched: - continue - return (macro, len(matched.group(1))) - return (None, -1) - - -def CheckCheck(filename, clean_lines, linenum, error): - """Checks the use of CHECK and EXPECT macros. - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - error: The function to call with any errors found. - """ - - # Decide the set of replacement macros that should be suggested - lines = clean_lines.elided - (check_macro, start_pos) = FindCheckMacro(lines[linenum]) - if not check_macro: - return - - # Find end of the boolean expression by matching parentheses - (last_line, end_line, end_pos) = CloseExpression(clean_lines, linenum, - start_pos) - if end_pos < 0: - return - - # If the check macro is followed by something other than a - # semicolon, assume users will log their own custom error messages - # and don't suggest any replacements. - if not Match(r'\s*;', last_line[end_pos:]): - return - - if linenum == end_line: - expression = lines[linenum][start_pos + 1:end_pos - 1] - else: - expression = lines[linenum][start_pos + 1:] - for i in xrange(linenum + 1, end_line): - expression += lines[i] - expression += last_line[0:end_pos - 1] - - # Parse expression so that we can take parentheses into account. - # This avoids false positives for inputs like "CHECK((a < 4) == b)", - # which is not replaceable by CHECK_LE. - lhs = '' - rhs = '' - operator = None - while expression: - matched = Match(r'^\s*(<<|<<=|>>|>>=|->\*|->|&&|\|\||' - r'==|!=|>=|>|<=|<|\()(.*)$', expression) - if matched: - token = matched.group(1) - if token == '(': - # Parenthesized operand - expression = matched.group(2) - (end, _) = FindEndOfExpressionInLine(expression, 0, ['(']) - if end < 0: - return # Unmatched parenthesis - lhs += '(' + expression[0:end] - expression = expression[end:] - elif token in ('&&', '||'): - # Logical and/or operators. This means the expression - # contains more than one term, for example: - # CHECK(42 < a && a < b); - # - # These are not replaceable with CHECK_LE, so bail out early. - return - elif token in ('<<', '<<=', '>>', '>>=', '->*', '->'): - # Non-relational operator - lhs += token - expression = matched.group(2) - else: - # Relational operator - operator = token - rhs = matched.group(2) - break - else: - # Unparenthesized operand. Instead of appending to lhs one character - # at a time, we do another regular expression match to consume several - # characters at once if possible. Trivial benchmark shows that this - # is more efficient when the operands are longer than a single - # character, which is generally the case. - matched = Match(r'^([^-=!<>()&|]+)(.*)$', expression) - if not matched: - matched = Match(r'^(\s*\S)(.*)$', expression) - if not matched: - break - lhs += matched.group(1) - expression = matched.group(2) - - # Only apply checks if we got all parts of the boolean expression - if not (lhs and operator and rhs): - return - - # Check that rhs do not contain logical operators. We already know - # that lhs is fine since the loop above parses out && and ||. - if rhs.find('&&') > -1 or rhs.find('||') > -1: - return - - # At least one of the operands must be a constant literal. This is - # to avoid suggesting replacements for unprintable things like - # CHECK(variable != iterator) - # - # The following pattern matches decimal, hex integers, strings, and - # characters (in that order). - lhs = lhs.strip() - rhs = rhs.strip() - match_constant = r'^([-+]?(\d+|0[xX][0-9a-fA-F]+)[lLuU]{0,3}|".*"|\'.*\')$' - if Match(match_constant, lhs) or Match(match_constant, rhs): - # Note: since we know both lhs and rhs, we can provide a more - # descriptive error message like: - # Consider using CHECK_EQ(x, 42) instead of CHECK(x == 42) - # Instead of: - # Consider using CHECK_EQ instead of CHECK(a == b) - # - # We are still keeping the less descriptive message because if lhs - # or rhs gets long, the error message might become unreadable. - error(filename, linenum, 'readability/check', 2, - 'Consider using %s instead of %s(a %s b)' % - (_CHECK_REPLACEMENT[check_macro][operator], check_macro, - operator)) - - -def CheckAltTokens(filename, clean_lines, linenum, error): - """Check alternative keywords being used in boolean expressions. - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - error: The function to call with any errors found. - """ - line = clean_lines.elided[linenum] - - # Avoid preprocessor lines - if Match(r'^\s*#', line): - return - - # Last ditch effort to avoid multi-line comments. This will not help - # if the comment started before the current line or ended after the - # current line, but it catches most of the false positives. At least, - # it provides a way to workaround this warning for people who use - # multi-line comments in preprocessor macros. - # - # TODO(unknown): remove this once cpplint has better support for - # multi-line comments. - if line.find('/*') >= 0 or line.find('*/') >= 0: - return - - for match in _ALT_TOKEN_REPLACEMENT_PATTERN.finditer(line): - error(filename, linenum, 'readability/alt_tokens', 2, - 'Use operator %s instead of %s' % ( - _ALT_TOKEN_REPLACEMENT[match.group(1)], match.group(1))) - - -def GetLineWidth(line): - """Determines the width of the line in column positions. - - Args: - line: A string, which may be a Unicode string. - - Returns: - The width of the line in column positions, accounting for Unicode - combining characters and wide characters. - """ - if isinstance(line, unicode): - width = 0 - for uc in unicodedata.normalize('NFC', line): - if unicodedata.east_asian_width(uc) in ('W', 'F'): - width += 2 - elif not unicodedata.combining(uc): - width += 1 - return width - else: - return len(line) - - -def CheckStyle(filename, clean_lines, linenum, file_extension, nesting_state, - error): - """Checks rules from the 'C++ style rules' section of cppguide.html. - - Most of these rules are hard to test (naming, comment style), but we - do what we can. In particular we check for 2-space indents, line lengths, - tab usage, spaces inside code, etc. - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - file_extension: The extension (without the dot) of the filename. - nesting_state: A NestingState instance which maintains information about - the current stack of nested blocks being parsed. - error: The function to call with any errors found. - """ - - # Don't use "elided" lines here, otherwise we can't check commented lines. - # Don't want to use "raw" either, because we don't want to check inside C++11 - # raw strings, - raw_lines = clean_lines.lines_without_raw_strings - line = raw_lines[linenum] - - if line.find('\t') != -1: - error(filename, linenum, 'whitespace/tab', 1, - 'Tab found; better to use spaces') - - # One or three blank spaces at the beginning of the line is weird; it's - # hard to reconcile that with 2-space indents. - # NOTE: here are the conditions rob pike used for his tests. Mine aren't - # as sophisticated, but it may be worth becoming so: RLENGTH==initial_spaces - # if(RLENGTH > 20) complain = 0; - # if(match($0, " +(error|private|public|protected):")) complain = 0; - # if(match(prev, "&& *$")) complain = 0; - # if(match(prev, "\\|\\| *$")) complain = 0; - # if(match(prev, "[\",=><] *$")) complain = 0; - # if(match($0, " <<")) complain = 0; - # if(match(prev, " +for \\(")) complain = 0; - # if(prevodd && match(prevprev, " +for \\(")) complain = 0; - scope_or_label_pattern = r'\s*\w+\s*:\s*\\?$' - classinfo = nesting_state.InnermostClass() - initial_spaces = 0 - cleansed_line = clean_lines.elided[linenum] - while initial_spaces < len(line) and line[initial_spaces] == ' ': - initial_spaces += 1 - if line and line[-1].isspace(): - error(filename, linenum, 'whitespace/end_of_line', 4, - 'Line ends in whitespace. Consider deleting these extra spaces.') - # There are certain situations we allow one space, notably for - # section labels, and also lines containing multi-line raw strings. - elif ((initial_spaces == 1 or initial_spaces == 3) and - not Match(scope_or_label_pattern, cleansed_line) and - not (clean_lines.raw_lines[linenum] != line and - Match(r'^\s*""', line))): - error(filename, linenum, 'whitespace/indent', 3, - 'Weird number of spaces at line-start. ' - 'Are you using a 2-space indent?') - - # Check if the line is a header guard. - is_header_guard = False - if file_extension == 'h': - cppvar = GetHeaderGuardCPPVariable(filename) - if (line.startswith('#ifndef %s' % cppvar) or - line.startswith('#define %s' % cppvar) or - line.startswith('#endif // %s' % cppvar)): - is_header_guard = True - # #include lines and header guards can be long, since there's no clean way to - # split them. - # - # URLs can be long too. It's possible to split these, but it makes them - # harder to cut&paste. - # - # The "$Id:...$" comment may also get very long without it being the - # developers fault. - if (not line.startswith('#include') and not is_header_guard and - not Match(r'^\s*//.*http(s?)://\S*$', line) and - not Match(r'^// \$Id:.*#[0-9]+ \$$', line)): - line_width = GetLineWidth(line) - extended_length = int((_line_length * 1.25)) - if line_width > extended_length: - error(filename, linenum, 'whitespace/line_length', 4, - 'Lines should very rarely be longer than %i characters' % - extended_length) - elif line_width > _line_length: - error(filename, linenum, 'whitespace/line_length', 2, - 'Lines should be <= %i characters long' % _line_length) - - if (cleansed_line.count(';') > 1 and - # for loops are allowed two ;'s (and may run over two lines). - cleansed_line.find('for') == -1 and - (GetPreviousNonBlankLine(clean_lines, linenum)[0].find('for') == -1 or - GetPreviousNonBlankLine(clean_lines, linenum)[0].find(';') != -1) and - # It's ok to have many commands in a switch case that fits in 1 line - not ((cleansed_line.find('case ') != -1 or - cleansed_line.find('default:') != -1) and - cleansed_line.find('break;') != -1)): - error(filename, linenum, 'whitespace/newline', 0, - 'More than one command on the same line') - - # Some more style checks - CheckBraces(filename, clean_lines, linenum, error) - CheckTrailingSemicolon(filename, clean_lines, linenum, error) - CheckEmptyBlockBody(filename, clean_lines, linenum, error) - CheckAccess(filename, clean_lines, linenum, nesting_state, error) - CheckSpacing(filename, clean_lines, linenum, nesting_state, error) - CheckOperatorSpacing(filename, clean_lines, linenum, error) - CheckParenthesisSpacing(filename, clean_lines, linenum, error) - CheckCommaSpacing(filename, clean_lines, linenum, error) - CheckBracesSpacing(filename, clean_lines, linenum, error) - CheckSpacingForFunctionCall(filename, clean_lines, linenum, error) - CheckRValueReference(filename, clean_lines, linenum, nesting_state, error) - CheckCheck(filename, clean_lines, linenum, error) - CheckAltTokens(filename, clean_lines, linenum, error) - classinfo = nesting_state.InnermostClass() - if classinfo: - CheckSectionSpacing(filename, clean_lines, classinfo, linenum, error) - - -_RE_PATTERN_INCLUDE = re.compile(r'^\s*#\s*include\s*([<"])([^>"]*)[>"].*$') -# Matches the first component of a filename delimited by -s and _s. That is: -# _RE_FIRST_COMPONENT.match('foo').group(0) == 'foo' -# _RE_FIRST_COMPONENT.match('foo.cc').group(0) == 'foo' -# _RE_FIRST_COMPONENT.match('foo-bar_baz.cc').group(0) == 'foo' -# _RE_FIRST_COMPONENT.match('foo_bar-baz.cc').group(0) == 'foo' -_RE_FIRST_COMPONENT = re.compile(r'^[^-_.]+') - - -def _DropCommonSuffixes(filename): - """Drops common suffixes like _test.cc or -inl.h from filename. - - For example: - >>> _DropCommonSuffixes('foo/foo-inl.h') - 'foo/foo' - >>> _DropCommonSuffixes('foo/bar/foo.cc') - 'foo/bar/foo' - >>> _DropCommonSuffixes('foo/foo_internal.h') - 'foo/foo' - >>> _DropCommonSuffixes('foo/foo_unusualinternal.h') - 'foo/foo_unusualinternal' - - Args: - filename: The input filename. - - Returns: - The filename with the common suffix removed. - """ - for suffix in ('test.cc', 'regtest.cc', 'unittest.cc', 'inl.h', 'impl.h', - 'internal.h'): - if (filename.endswith(suffix) and len(filename) > len(suffix) and - filename[-len(suffix) - 1] in ('-', '_')): - return filename[:-len(suffix) - 1] - return os.path.splitext(filename)[0] - - -def _IsTestFilename(filename): - """Determines if the given filename has a suffix that identifies it as a test. - - Args: - filename: The input filename. - - Returns: - True if 'filename' looks like a test, False otherwise. - """ - if (filename.endswith('_test.cc') or filename.endswith('_unittest.cc') or - filename.endswith('_regtest.cc')): - return True - else: - return False - - -def _ClassifyInclude(fileinfo, include, is_system): - """Figures out what kind of header 'include' is. - - Args: - fileinfo: The current file cpplint is running over. A FileInfo instance. - include: The path to a #included file. - is_system: True if the #include used <> rather than "". - - Returns: - One of the _XXX_HEADER constants. - - For example: - >>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'stdio.h', True) - _C_SYS_HEADER - >>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'string', True) - _CPP_SYS_HEADER - >>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'foo/foo.h', False) - _LIKELY_MY_HEADER - >>> _ClassifyInclude(FileInfo('foo/foo_unknown_extension.cc'), - ... 'bar/foo_other_ext.h', False) - _POSSIBLE_MY_HEADER - >>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'foo/bar.h', False) - _OTHER_HEADER - """ - # This is a list of all standard c++ header files, except - # those already checked for above. - is_cpp_h = include in _CPP_HEADERS - - if is_system: - if is_cpp_h: - return _CPP_SYS_HEADER - else: - return _C_SYS_HEADER - - # If the target file and the include we're checking share a - # basename when we drop common extensions, and the include - # lives in . , then it's likely to be owned by the target file. - target_dir, target_base = ( - os.path.split(_DropCommonSuffixes(fileinfo.RepositoryName()))) - include_dir, include_base = os.path.split(_DropCommonSuffixes(include)) - if target_base == include_base and ( - include_dir == target_dir or - include_dir == os.path.normpath(target_dir + '/../public')): - return _LIKELY_MY_HEADER - - # If the target and include share some initial basename - # component, it's possible the target is implementing the - # include, so it's allowed to be first, but we'll never - # complain if it's not there. - target_first_component = _RE_FIRST_COMPONENT.match(target_base) - include_first_component = _RE_FIRST_COMPONENT.match(include_base) - if (target_first_component and include_first_component and - target_first_component.group(0) == - include_first_component.group(0)): - return _POSSIBLE_MY_HEADER - - return _OTHER_HEADER - - -def CheckIncludeLine(filename, clean_lines, linenum, include_state, error): - """Check rules that are applicable to #include lines. - - Strings on #include lines are NOT removed from elided line, to make - certain tasks easier. However, to prevent false positives, checks - applicable to #include lines in CheckLanguage must be put here. - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - include_state: An _IncludeState instance in which the headers are inserted. - error: The function to call with any errors found. - """ - fileinfo = FileInfo(filename) - line = clean_lines.lines[linenum] - - # "include" should use the new style "foo/bar.h" instead of just "bar.h" - # Only do this check if the included header follows google naming - # conventions. If not, assume that it's a 3rd party API that - # requires special include conventions. - # - # We also make an exception for Lua headers, which follow google - # naming convention but not the include convention. - match = Match(r'#include\s*"([^/]+\.h)"', line) - if match and not _THIRD_PARTY_HEADERS_PATTERN.match(match.group(1)): - error(filename, linenum, 'build/include', 4, - 'Include the directory when naming .h files') - - # we shouldn't include a file more than once. actually, there are a - # handful of instances where doing so is okay, but in general it's - # not. - match = _RE_PATTERN_INCLUDE.search(line) - if match: - include = match.group(2) - is_system = (match.group(1) == '<') - duplicate_line = include_state.FindHeader(include) - if duplicate_line >= 0: - error(filename, linenum, 'build/include', 4, - '"%s" already included at %s:%s' % - (include, filename, duplicate_line)) - elif (include.endswith('.cc') and - os.path.dirname(fileinfo.RepositoryName()) != - os.path.dirname(include)): - error(filename, linenum, 'build/include', 4, - 'Do not include .cc files from other packages') - elif not _THIRD_PARTY_HEADERS_PATTERN.match(include): - include_state.include_list[-1].append((include, linenum)) - - # We want to ensure that headers appear in the right order: - # 1) for foo.cc, foo.h (preferred location) - # 2) c system files - # 3) cpp system files - # 4) for foo.cc, foo.h (deprecated location) - # 5) other google headers - # - # We classify each include statement as one of those 5 types - # using a number of techniques. The include_state object keeps - # track of the highest type seen, and complains if we see a - # lower type after that. - error_message = include_state.CheckNextIncludeOrder( - _ClassifyInclude(fileinfo, include, is_system)) - if error_message: - error(filename, linenum, 'build/include_order', 4, - '%s. Should be: %s.h, c system, c++ system, other.' % - (error_message, fileinfo.BaseName())) - canonical_include = include_state.CanonicalizeAlphabeticalOrder( - include) - if not include_state.IsInAlphabeticalOrder(clean_lines, linenum, - canonical_include): - error(filename, linenum, 'build/include_alpha', 4, - 'Include "%s" not in alphabetical order' % include) - include_state.SetLastHeader(canonical_include) - - -def _GetTextInside(text, start_pattern): - r"""Retrieves all the text between matching open and close parentheses. - - Given a string of lines and a regular expression string, retrieve all the text - following the expression and between opening punctuation symbols like - (, [, or {, and the matching close-punctuation symbol. This properly nested - occurrences of the punctuations, so for the text like - printf(a(), b(c())); - a call to _GetTextInside(text, r'printf\(') will return 'a(), b(c())'. - start_pattern must match string having an open punctuation symbol at the end. - - Args: - text: The lines to extract text. Its comments and strings must be elided. - It can be single line and can span multiple lines. - start_pattern: The regexp string indicating where to start extracting - the text. - Returns: - The extracted text. - None if either the opening string or ending punctuation could not be found. - """ - # TODO(unknown): Audit cpplint.py to see what places could be profitably - # rewritten to use _GetTextInside (and use inferior regexp matching today). - - # Give opening punctuations to get the matching close-punctuations. - matching_punctuation = {'(': ')', '{': '}', '[': ']'} - closing_punctuation = set(matching_punctuation.itervalues()) - - # Find the position to start extracting text. - match = re.search(start_pattern, text, re.M) - if not match: # start_pattern not found in text. - return None - start_position = match.end(0) - - assert start_position > 0, ( - 'start_pattern must ends with an opening punctuation.') - assert text[start_position - 1] in matching_punctuation, ( - 'start_pattern must ends with an opening punctuation.') - # Stack of closing punctuations we expect to have in text after position. - punctuation_stack = [matching_punctuation[text[start_position - 1]]] - position = start_position - while punctuation_stack and position < len(text): - if text[position] == punctuation_stack[-1]: - punctuation_stack.pop() - elif text[position] in closing_punctuation: - # A closing punctuation without matching opening punctuations. - return None - elif text[position] in matching_punctuation: - punctuation_stack.append(matching_punctuation[text[position]]) - position += 1 - if punctuation_stack: - # Opening punctuations left without matching close-punctuations. - return None - # punctuations match. - return text[start_position:position - 1] - - -# Patterns for matching call-by-reference parameters. -# -# Supports nested templates up to 2 levels deep using this messy pattern: -# < (?: < (?: < [^<>]* -# > -# | [^<>] )* -# > -# | [^<>] )* -# > -_RE_PATTERN_IDENT = r'[_a-zA-Z]\w*' # =~ [[:alpha:]][[:alnum:]]* -_RE_PATTERN_TYPE = ( - r'(?:const\s+)?(?:typename\s+|class\s+|struct\s+|union\s+|enum\s+)?' - r'(?:\w|' - r'\s*<(?:<(?:<[^<>]*>|[^<>])*>|[^<>])*>|' - r'::)+') -# A call-by-reference parameter ends with '& identifier'. -_RE_PATTERN_REF_PARAM = re.compile( - r'(' + _RE_PATTERN_TYPE + r'(?:\s*(?:\bconst\b|[*]))*\s*' - r'&\s*' + _RE_PATTERN_IDENT + r')\s*(?:=[^,()]+)?[,)]') -# A call-by-const-reference parameter either ends with 'const& identifier' -# or looks like 'const type& identifier' when 'type' is atomic. -_RE_PATTERN_CONST_REF_PARAM = ( - r'(?:.*\s*\bconst\s*&\s*' + _RE_PATTERN_IDENT + r'|const\s+' + - _RE_PATTERN_TYPE + r'\s*&\s*' + _RE_PATTERN_IDENT + r')') - - -def CheckLanguage(filename, clean_lines, linenum, file_extension, include_state, - nesting_state, error): - """Checks rules from the 'C++ language rules' section of cppguide.html. - - Some of these rules are hard to test (function overloading, using - uint32 inappropriately), but we do the best we can. - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - file_extension: The extension (without the dot) of the filename. - include_state: An _IncludeState instance in which the headers are inserted. - nesting_state: A NestingState instance which maintains information about - the current stack of nested blocks being parsed. - error: The function to call with any errors found. - """ - # If the line is empty or consists of entirely a comment, no need to - # check it. - line = clean_lines.elided[linenum] - if not line: - return - - match = _RE_PATTERN_INCLUDE.search(line) - if match: - CheckIncludeLine(filename, clean_lines, linenum, include_state, error) - return - - # Reset include state across preprocessor directives. This is meant - # to silence warnings for conditional includes. - match = Match(r'^\s*#\s*(if|ifdef|ifndef|elif|else|endif)\b', line) - if match: - include_state.ResetSection(match.group(1)) - - # Make Windows paths like Unix. - fullname = os.path.abspath(filename).replace('\\', '/') - - # Perform other checks now that we are sure that this is not an include line - CheckCasts(filename, clean_lines, linenum, error) - CheckGlobalStatic(filename, clean_lines, linenum, error) - CheckPrintf(filename, clean_lines, linenum, error) - - if file_extension == 'h': - # TODO(unknown): check that 1-arg constructors are explicit. - # How to tell it's a constructor? - # (handled in CheckForNonStandardConstructs for now) - # TODO(unknown): check that classes declare or disable copy/assign - # (level 1 error) - pass - - # Check if people are using the verboten C basic types. The only exception - # we regularly allow is "unsigned short port" for port. - if Search(r'\bshort port\b', line): - if not Search(r'\bunsigned short port\b', line): - error(filename, linenum, 'runtime/int', 4, - 'Use "unsigned short" for ports, not "short"') - else: - match = Search(r'\b(short|long(?! +double)|long long)\b', line) - if match: - error(filename, linenum, 'runtime/int', 4, - 'Use int16/int64/etc, rather than the C type %s' % - match.group(1)) - - # Check if some verboten operator overloading is going on - # TODO(unknown): catch out-of-line unary operator&: - # class X {}; - # int operator&(const X& x) { return 42; } // unary operator& - # The trick is it's hard to tell apart from binary operator&: - # class Y { int operator&(const Y& x) { return 23; } }; // binary operator& - if Search(r'\boperator\s*&\s*\(\s*\)', line): - error(filename, linenum, 'runtime/operator', 4, - 'Unary operator& is dangerous. Do not use it.') - - # Check for suspicious usage of "if" like - # } if (a == b) { - if Search(r'\}\s*if\s*\(', line): - error(filename, linenum, 'readability/braces', 4, - 'Did you mean "else if"? If not, start a new line for "if".') - - # Check for potential format string bugs like printf(foo). - # We constrain the pattern not to pick things like DocidForPrintf(foo). - # Not perfect but it can catch printf(foo.c_str()) and printf(foo->c_str()) - # TODO(unknown): Catch the following case. Need to change the calling - # convention of the whole function to process multiple line to handle it. - # printf( - # boy_this_is_a_really_long_variable_that_cannot_fit_on_the_prev_line); - printf_args = _GetTextInside(line, r'(?i)\b(string)?printf\s*\(') - if printf_args: - match = Match(r'([\w.\->()]+)$', printf_args) - if match and match.group(1) != '__VA_ARGS__': - function_name = re.search(r'\b((?:string)?printf)\s*\(', line, - re.I).group(1) - error(filename, linenum, 'runtime/printf', 4, - 'Potential format string bug. Do %s("%%s", %s) instead.' % - (function_name, match.group(1))) - - # Check for potential memset bugs like memset(buf, sizeof(buf), 0). - match = Search(r'memset\s*\(([^,]*),\s*([^,]*),\s*0\s*\)', line) - if match and not Match(r"^''|-?[0-9]+|0x[0-9A-Fa-f]$", match.group(2)): - error(filename, linenum, 'runtime/memset', 4, - 'Did you mean "memset(%s, 0, %s)"?' % - (match.group(1), match.group(2))) - - if Search(r'\busing namespace\b', line): - error(filename, linenum, 'build/namespaces', 5, - 'Do not use namespace using-directives. ' - 'Use using-declarations instead.') - - # Detect variable-length arrays. - match = Match(r'\s*(.+::)?(\w+) [a-z]\w*\[(.+)];', line) - if (match and match.group(2) != 'return' and match.group(2) != 'delete' and - match.group(3).find(']') == -1): - # Split the size using space and arithmetic operators as delimiters. - # If any of the resulting tokens are not compile time constants then - # report the error. - tokens = re.split(r'\s|\+|\-|\*|\/|<<|>>]', match.group(3)) - is_const = True - skip_next = False - for tok in tokens: - if skip_next: - skip_next = False - continue - - if Search(r'sizeof\(.+\)', tok): continue - if Search(r'arraysize\(\w+\)', tok): continue - - tok = tok.lstrip('(') - tok = tok.rstrip(')') - if not tok: continue - if Match(r'\d+', tok): continue - if Match(r'0[xX][0-9a-fA-F]+', tok): continue - if Match(r'k[A-Z0-9]\w*', tok): continue - if Match(r'(.+::)?k[A-Z0-9]\w*', tok): continue - if Match(r'(.+::)?[A-Z][A-Z0-9_]*', tok): continue - # A catch all for tricky sizeof cases, including 'sizeof expression', - # 'sizeof(*type)', 'sizeof(const type)', 'sizeof(struct StructName)' - # requires skipping the next token because we split on ' ' and '*'. - if tok.startswith('sizeof'): - skip_next = True - continue - is_const = False - break - if not is_const: - error( - filename, linenum, 'runtime/arrays', 1, - 'Do not use variable-length arrays. Use an appropriately named ' - "('k' followed by CamelCase) compile-time constant for the size." - ) - - # Check for use of unnamed namespaces in header files. Registration - # macros are typically OK, so we allow use of "namespace {" on lines - # that end with backslashes. - if (file_extension == 'h' and Search(r'\bnamespace\s*{', line) and - line[-1] != '\\'): - error( - filename, linenum, 'build/namespaces', 4, - 'Do not use unnamed namespaces in header files. See ' - 'http://google-styleguide.googlecode.com/svn/trunk/cppguide.xml#Namespaces' - ' for more information.') - - -def CheckGlobalStatic(filename, clean_lines, linenum, error): - """Check for unsafe global or static objects. - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - error: The function to call with any errors found. - """ - line = clean_lines.elided[linenum] - - # Match two lines at a time to support multiline declarations - if linenum + 1 < clean_lines.NumLines() and not Search(r'[;({]', line): - line += clean_lines.elided[linenum + 1].strip() - - # Check for people declaring static/global STL strings at the top level. - # This is dangerous because the C++ language does not guarantee that - # globals with constructors are initialized before the first access. - match = Match(r'((?:|static +)(?:|const +))string +([a-zA-Z0-9_:]+)\b(.*)', - line) - - # Remove false positives: - # - String pointers (as opposed to values). - # string *pointer - # const string *pointer - # string const *pointer - # string *const pointer - # - # - Functions and template specializations. - # string Function(... - # string Class::Method(... - # - # - Operators. These are matched separately because operator names - # cross non-word boundaries, and trying to match both operators - # and functions at the same time would decrease accuracy of - # matching identifiers. - # string Class::operator*() - if (match and - not Search(r'\bstring\b(\s+const)?\s*\*\s*(const\s+)?\w', line) and - not Search(r'\boperator\W', line) and not Match( - r'\s*(<.*>)?(::[a-zA-Z0-9_]+)*\s*\(([^"]|$)', match.group(3))): - error( - filename, linenum, 'runtime/string', 4, - 'For a static/global string constant, use a C style string instead: ' - '"%schar %s[]".' % (match.group(1), match.group(2))) - - if Search(r'\b([A-Za-z0-9_]*_)\(\1\)', line): - error(filename, linenum, 'runtime/init', 4, - 'You seem to be initializing a member variable with itself.') - - -def CheckPrintf(filename, clean_lines, linenum, error): - """Check for printf related issues. - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - error: The function to call with any errors found. - """ - line = clean_lines.elided[linenum] - - # When snprintf is used, the second argument shouldn't be a literal. - match = Search(r'snprintf\s*\(([^,]*),\s*([0-9]*)\s*,', line) - if match and match.group(2) != '0': - # If 2nd arg is zero, snprintf is used to calculate size. - error(filename, linenum, 'runtime/printf', 3, - 'If you can, use sizeof(%s) instead of %s as the 2nd arg ' - 'to snprintf.' % (match.group(1), match.group(2))) - - # Check if some verboten C functions are being used. - if Search(r'\bsprintf\s*\(', line): - error(filename, linenum, 'runtime/printf', 5, - 'Never use sprintf. Use snprintf instead.') - match = Search(r'\b(strcpy|strcat)\s*\(', line) - if match: - error(filename, linenum, 'runtime/printf', 4, - 'Almost always, snprintf is better than %s' % match.group(1)) - - -def IsDerivedFunction(clean_lines, linenum): - """Check if current line contains an inherited function. - - Args: - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - Returns: - True if current line contains a function with "override" - virt-specifier. - """ - # Scan back a few lines for start of current function - for i in xrange(linenum, max(-1, linenum - 10), -1): - match = Match(r'^([^()]*\w+)\(', clean_lines.elided[i]) - if match: - # Look for "override" after the matching closing parenthesis - line, _, closing_paren = CloseExpression(clean_lines, i, - len(match.group(1))) - return (closing_paren >= 0 and - Search(r'\boverride\b', line[closing_paren:])) - return False - - -def IsOutOfLineMethodDefinition(clean_lines, linenum): - """Check if current line contains an out-of-line method definition. - - Args: - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - Returns: - True if current line contains an out-of-line method definition. - """ - # Scan back a few lines for start of current function - for i in xrange(linenum, max(-1, linenum - 10), -1): - if Match(r'^([^()]*\w+)\(', clean_lines.elided[i]): - return Match(r'^[^()]*\w+::\w+\(', - clean_lines.elided[i]) is not None - return False - - -def IsInitializerList(clean_lines, linenum): - """Check if current line is inside constructor initializer list. - - Args: - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - Returns: - True if current line appears to be inside constructor initializer - list, False otherwise. - """ - for i in xrange(linenum, 1, -1): - line = clean_lines.elided[i] - if i == linenum: - remove_function_body = Match(r'^(.*)\{\s*$', line) - if remove_function_body: - line = remove_function_body.group(1) - - if Search(r'\s:\s*\w+[({]', line): - # A lone colon tend to indicate the start of a constructor - # initializer list. It could also be a ternary operator, which - # also tend to appear in constructor initializer lists as - # opposed to parameter lists. - return True - if Search(r'\}\s*,\s*$', line): - # A closing brace followed by a comma is probably the end of a - # brace-initialized member in constructor initializer list. - return True - if Search(r'[{};]\s*$', line): - # Found one of the following: - # - A closing brace or semicolon, probably the end of the previous - # function. - # - An opening brace, probably the start of current class or namespace. - # - # Current line is probably not inside an initializer list since - # we saw one of those things without seeing the starting colon. - return False - - # Got to the beginning of the file without seeing the start of - # constructor initializer list. - return False - - -def CheckForNonConstReference(filename, clean_lines, linenum, nesting_state, - error): - """Check for non-const references. - - Separate from CheckLanguage since it scans backwards from current - line, instead of scanning forward. - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - nesting_state: A NestingState instance which maintains information about - the current stack of nested blocks being parsed. - error: The function to call with any errors found. - """ - # Do nothing if there is no '&' on current line. - line = clean_lines.elided[linenum] - if '&' not in line: - return - - # If a function is inherited, current function doesn't have much of - # a choice, so any non-const references should not be blamed on - # derived function. - if IsDerivedFunction(clean_lines, linenum): - return - - # Don't warn on out-of-line method definitions, as we would warn on the - # in-line declaration, if it isn't marked with 'override'. - if IsOutOfLineMethodDefinition(clean_lines, linenum): - return - - # Long type names may be broken across multiple lines, usually in one - # of these forms: - # LongType - # ::LongTypeContinued &identifier - # LongType:: - # LongTypeContinued &identifier - # LongType< - # ...>::LongTypeContinued &identifier - # - # If we detected a type split across two lines, join the previous - # line to current line so that we can match const references - # accordingly. - # - # Note that this only scans back one line, since scanning back - # arbitrary number of lines would be expensive. If you have a type - # that spans more than 2 lines, please use a typedef. - if linenum > 1: - previous = None - if Match(r'\s*::(?:[\w<>]|::)+\s*&\s*\S', line): - # previous_line\n + ::current_line - previous = Search(r'\b((?:const\s*)?(?:[\w<>]|::)+[\w<>])\s*$', - clean_lines.elided[linenum - 1]) - elif Match(r'\s*[a-zA-Z_]([\w<>]|::)+\s*&\s*\S', line): - # previous_line::\n + current_line - previous = Search(r'\b((?:const\s*)?(?:[\w<>]|::)+::)\s*$', - clean_lines.elided[linenum - 1]) - if previous: - line = previous.group(1) + line.lstrip() - else: - # Check for templated parameter that is split across multiple lines - endpos = line.rfind('>') - if endpos > -1: - (_, startline, startpos) = ReverseCloseExpression( - clean_lines, linenum, endpos) - if startpos > -1 and startline < linenum: - # Found the matching < on an earlier line, collect all - # pieces up to current line. - line = '' - for i in xrange(startline, linenum + 1): - line += clean_lines.elided[i].strip() - - # Check for non-const references in function parameters. A single '&' may - # found in the following places: - # inside expression: binary & for bitwise AND - # inside expression: unary & for taking the address of something - # inside declarators: reference parameter - # We will exclude the first two cases by checking that we are not inside a - # function body, including one that was just introduced by a trailing '{'. - # TODO(unknown): Doesn't account for 'catch(Exception& e)' [rare]. - if (nesting_state.previous_stack_top and - not (isinstance(nesting_state.previous_stack_top, _ClassInfo) or - isinstance(nesting_state.previous_stack_top, _NamespaceInfo))): - # Not at toplevel, not within a class, and not within a namespace - return - - # Avoid initializer lists. We only need to scan back from the - # current line for something that starts with ':'. - # - # We don't need to check the current line, since the '&' would - # appear inside the second set of parentheses on the current line as - # opposed to the first set. - if linenum > 0: - for i in xrange(linenum - 1, max(0, linenum - 10), -1): - previous_line = clean_lines.elided[i] - if not Search(r'[),]\s*$', previous_line): - break - if Match(r'^\s*:\s+\S', previous_line): - return - - # Avoid preprocessors - if Search(r'\\\s*$', line): - return - - # Avoid constructor initializer lists - if IsInitializerList(clean_lines, linenum): - return - - # We allow non-const references in a few standard places, like functions - # called "swap()" or iostream operators like "<<" or ">>". Do not check - # those function parameters. - # - # We also accept & in static_assert, which looks like a function but - # it's actually a declaration expression. - whitelisted_functions = (r'(?:[sS]wap(?:<\w:+>)?|' - r'operator\s*[<>][<>]|' - r'static_assert|COMPILE_ASSERT' - r')\s*\(') - if Search(whitelisted_functions, line): - return - elif not Search(r'\S+\([^)]*$', line): - # Don't see a whitelisted function on this line. Actually we - # didn't see any function name on this line, so this is likely a - # multi-line parameter list. Try a bit harder to catch this case. - for i in xrange(2): - if (linenum > i and Search(whitelisted_functions, - clean_lines.elided[linenum - i - 1])): - return - - decls = ReplaceAll(r'{[^}]*}', ' ', line) # exclude function body - for parameter in re.findall(_RE_PATTERN_REF_PARAM, decls): - if not Match(_RE_PATTERN_CONST_REF_PARAM, parameter): - error(filename, linenum, 'runtime/references', 2, - 'Is this a non-const reference? ' - 'If so, make const or use a pointer: ' + ReplaceAll( - ' *<', '<', parameter)) - - -def CheckCasts(filename, clean_lines, linenum, error): - """Various cast related checks. - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - error: The function to call with any errors found. - """ - line = clean_lines.elided[linenum] - - # Check to see if they're using an conversion function cast. - # I just try to capture the most common basic types, though there are more. - # Parameterless conversion functions, such as bool(), are allowed as they are - # probably a member operator declaration or default constructor. - match = Search(r'(\bnew\s+|\S<\s*(?:const\s+)?)?\b' - r'(int|float|double|bool|char|int32|uint32|int64|uint64)' - r'(\([^)].*)', line) - expecting_function = ExpectingFunctionArgs(clean_lines, linenum) - if match and not expecting_function: - matched_type = match.group(2) - - # matched_new_or_template is used to silence two false positives: - # - New operators - # - Template arguments with function types - # - # For template arguments, we match on types immediately following - # an opening bracket without any spaces. This is a fast way to - # silence the common case where the function type is the first - # template argument. False negative with less-than comparison is - # avoided because those operators are usually followed by a space. - # - # function // bracket + no space = false positive - # value < double(42) // bracket + space = true positive - matched_new_or_template = match.group(1) - - # Avoid arrays by looking for brackets that come after the closing - # parenthesis. - if Match(r'\([^()]+\)\s*\[', match.group(3)): - return - - # Other things to ignore: - # - Function pointers - # - Casts to pointer types - # - Placement new - # - Alias declarations - matched_funcptr = match.group(3) - if (matched_new_or_template is None and not (matched_funcptr and (Match( - r'\((?:[^() ]+::\s*\*\s*)?[^() ]+\)\s*\(', - matched_funcptr) or matched_funcptr.startswith('(*)'))) and - not Match(r'\s*using\s+\S+\s*=\s*' + matched_type, line) and - not Search(r'new\(\S+\)\s*' + matched_type, line)): - error(filename, linenum, 'readability/casting', 4, - 'Using deprecated casting style. ' - 'Use static_cast<%s>(...) instead' % matched_type) - - if not expecting_function: - CheckCStyleCast(filename, clean_lines, linenum, 'static_cast', - r'\((int|float|double|bool|char|u?int(16|32|64))\)', - error) - - # This doesn't catch all cases. Consider (const char * const)"hello". - # - # (char *) "foo" should always be a const_cast (reinterpret_cast won't - # compile). - if CheckCStyleCast(filename, clean_lines, linenum, 'const_cast', - r'\((char\s?\*+\s?)\)\s*"', error): - pass - else: - # Check pointer casts for other than string constants - CheckCStyleCast(filename, clean_lines, linenum, 'reinterpret_cast', - r'\((\w+\s?\*+\s?)\)', error) - - # In addition, we look for people taking the address of a cast. This - # is dangerous -- casts can assign to temporaries, so the pointer doesn't - # point where you think. - # - # Some non-identifier character is required before the '&' for the - # expression to be recognized as a cast. These are casts: - # expression = &static_cast(temporary()); - # function(&(int*)(temporary())); - # - # This is not a cast: - # reference_type&(int* function_param); - match = Search(r'(?:[^\w]&\(([^)*][^)]*)\)[\w(])|' - r'(?:[^\w]&(static|dynamic|down|reinterpret)_cast\b)', line) - if match: - # Try a better error message when the & is bound to something - # dereferenced by the casted pointer, as opposed to the casted - # pointer itself. - parenthesis_error = False - match = Match(r'^(.*&(?:static|dynamic|down|reinterpret)_cast\b)<', - line) - if match: - _, y1, x1 = CloseExpression(clean_lines, linenum, - len(match.group(1))) - if x1 >= 0 and clean_lines.elided[y1][x1] == '(': - _, y2, x2 = CloseExpression(clean_lines, y1, x1) - if x2 >= 0: - extended_line = clean_lines.elided[y2][x2:] - if y2 < clean_lines.NumLines() - 1: - extended_line += clean_lines.elided[y2 + 1] - if Match(r'\s*(?:->|\[)', extended_line): - parenthesis_error = True - - if parenthesis_error: - error(filename, linenum, 'readability/casting', 4, - ('Are you taking an address of something dereferenced ' - 'from a cast? Wrapping the dereferenced expression in ' - 'parentheses will make the binding more obvious')) - else: - error(filename, linenum, 'runtime/casting', 4, - ('Are you taking an address of a cast? ' - 'This is dangerous: could be a temp var. ' - 'Take the address before doing the cast, rather than after')) - - -def CheckCStyleCast(filename, clean_lines, linenum, cast_type, pattern, error): - """Checks for a C-style cast by looking for the pattern. - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - cast_type: The string for the C++ cast to recommend. This is either - reinterpret_cast, static_cast, or const_cast, depending. - pattern: The regular expression used to find C-style casts. - error: The function to call with any errors found. - - Returns: - True if an error was emitted. - False otherwise. - """ - line = clean_lines.elided[linenum] - match = Search(pattern, line) - if not match: - return False - - # Exclude lines with keywords that tend to look like casts - context = line[0:match.start(1) - 1] - if Match(r'.*\b(?:sizeof|alignof|alignas|[_A-Z][_A-Z0-9]*)\s*$', context): - return False - - # Try expanding current context to see if we one level of - # parentheses inside a macro. - if linenum > 0: - for i in xrange(linenum - 1, max(0, linenum - 5), -1): - context = clean_lines.elided[i] + context - if Match(r'.*\b[_A-Z][_A-Z0-9]*\s*\((?:\([^()]*\)|[^()])*$', context): - return False - - # operator++(int) and operator--(int) - if context.endswith(' operator++') or context.endswith(' operator--'): - return False - - # A single unnamed argument for a function tends to look like old - # style cast. If we see those, don't issue warnings for deprecated - # casts, instead issue warnings for unnamed arguments where - # appropriate. - # - # These are things that we want warnings for, since the style guide - # explicitly require all parameters to be named: - # Function(int); - # Function(int) { - # ConstMember(int) const; - # ConstMember(int) const { - # ExceptionMember(int) throw (...); - # ExceptionMember(int) throw (...) { - # PureVirtual(int) = 0; - # [](int) -> bool { - # - # These are functions of some sort, where the compiler would be fine - # if they had named parameters, but people often omit those - # identifiers to reduce clutter: - # (FunctionPointer)(int); - # (FunctionPointer)(int) = value; - # Function((function_pointer_arg)(int)) - # Function((function_pointer_arg)(int), int param) - # ; - # <(FunctionPointerTemplateArgument)(int)>; - remainder = line[match.end(0):] - if Match(r'^\s*(?:;|const\b|throw\b|final\b|override\b|[=>{),]|->)', - remainder): - # Looks like an unnamed parameter. - - # Don't warn on any kind of template arguments. - if Match(r'^\s*>', remainder): - return False - - # Don't warn on assignments to function pointers, but keep warnings for - # unnamed parameters to pure virtual functions. Note that this pattern - # will also pass on assignments of "0" to function pointers, but the - # preferred values for those would be "nullptr" or "NULL". - matched_zero = Match(r'^\s=\s*(\S+)\s*;', remainder) - if matched_zero and matched_zero.group(1) != '0': - return False - - # Don't warn on function pointer declarations. For this we need - # to check what came before the "(type)" string. - if Match(r'.*\)\s*$', line[0:match.start(0)]): - return False - - # Don't warn if the parameter is named with block comments, e.g.: - # Function(int /*unused_param*/); - raw_line = clean_lines.raw_lines[linenum] - if '/*' in raw_line: - return False - - # Passed all filters, issue warning here. - error(filename, linenum, 'readability/function', 3, - 'All parameters should be named in a function') - return True - - # At this point, all that should be left is actual casts. - error(filename, linenum, 'readability/casting', 4, - 'Using C-style cast. Use %s<%s>(...) instead' % - (cast_type, match.group(1))) - - return True - - -def ExpectingFunctionArgs(clean_lines, linenum): - """Checks whether where function type arguments are expected. - - Args: - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - - Returns: - True if the line at 'linenum' is inside something that expects arguments - of function types. - """ - line = clean_lines.elided[linenum] - return (Match(r'^\s*MOCK_(CONST_)?METHOD\d+(_T)?\(', line) or - (linenum >= 2 and - (Match(r'^\s*MOCK_(?:CONST_)?METHOD\d+(?:_T)?\((?:\S+,)?\s*$', - clean_lines.elided[linenum - 1]) or - Match(r'^\s*MOCK_(?:CONST_)?METHOD\d+(?:_T)?\(\s*$', - clean_lines.elided[linenum - 2]) or - Search(r'\bstd::m?function\s*\<\s*$', - clean_lines.elided[linenum - 1])))) - - -_HEADERS_CONTAINING_TEMPLATES = ( - ('', ('deque', )), - ('', ( - 'unary_function', - 'binary_function', - 'plus', - 'minus', - 'multiplies', - 'divides', - 'modulus', - 'negate', - 'equal_to', - 'not_equal_to', - 'greater', - 'less', - 'greater_equal', - 'less_equal', - 'logical_and', - 'logical_or', - 'logical_not', - 'unary_negate', - 'not1', - 'binary_negate', - 'not2', - 'bind1st', - 'bind2nd', - 'pointer_to_unary_function', - 'pointer_to_binary_function', - 'ptr_fun', - 'mem_fun_t', - 'mem_fun', - 'mem_fun1_t', - 'mem_fun1_ref_t', - 'mem_fun_ref_t', - 'const_mem_fun_t', - 'const_mem_fun1_t', - 'const_mem_fun_ref_t', - 'const_mem_fun1_ref_t', - 'mem_fun_ref', )), - ('', ('numeric_limits', )), - ('', ('list', )), - ('', ( - 'map', - 'multimap', )), - ('', ('allocator', )), - ('', ( - 'queue', - 'priority_queue', )), - ('', ( - 'set', - 'multiset', )), - ('', ('stack', )), - ('', ( - 'char_traits', - 'basic_string', )), - ('', ('tuple', )), - ('', ('pair', )), - ('', ('vector', )), - - # gcc extensions. - # Note: std::hash is their hash, ::hash is our hash - ('', ( - 'hash_map', - 'hash_multimap', )), - ('', ( - 'hash_set', - 'hash_multiset', )), - ('', ('slist', )), ) - -_RE_PATTERN_STRING = re.compile(r'\bstring\b') - -_re_pattern_algorithm_header = [] -for _template in ('copy', 'max', 'min', 'min_element', 'sort', 'swap', - 'transform'): - # Match max(..., ...), max(..., ...), but not foo->max, foo.max or - # type::max(). - _re_pattern_algorithm_header.append( - (re.compile(r'[^>.]\b' + _template + r'(<.*?>)?\([^\)]'), _template, - '')) - -_re_pattern_templates = [] -for _header, _templates in _HEADERS_CONTAINING_TEMPLATES: - for _template in _templates: - _re_pattern_templates.append( - (re.compile(r'(\<|\b)' + _template + r'\s*\<'), _template + '<>', - _header)) - - -def FilesBelongToSameModule(filename_cc, filename_h): - """Check if these two filenames belong to the same module. - - The concept of a 'module' here is a as follows: - foo.h, foo-inl.h, foo.cc, foo_test.cc and foo_unittest.cc belong to the - same 'module' if they are in the same directory. - some/path/public/xyzzy and some/path/internal/xyzzy are also considered - to belong to the same module here. - - If the filename_cc contains a longer path than the filename_h, for example, - '/absolute/path/to/base/sysinfo.cc', and this file would include - 'base/sysinfo.h', this function also produces the prefix needed to open the - header. This is used by the caller of this function to more robustly open the - header file. We don't have access to the real include paths in this context, - so we need this guesswork here. - - Known bugs: tools/base/bar.cc and base/bar.h belong to the same module - according to this implementation. Because of this, this function gives - some false positives. This should be sufficiently rare in practice. - - Args: - filename_cc: is the path for the .cc file - filename_h: is the path for the header path - - Returns: - Tuple with a bool and a string: - bool: True if filename_cc and filename_h belong to the same module. - string: the additional prefix needed to open the header file. - """ - - if not filename_cc.endswith('.cc'): - return (False, '') - filename_cc = filename_cc[:-len('.cc')] - if filename_cc.endswith('_unittest'): - filename_cc = filename_cc[:-len('_unittest')] - elif filename_cc.endswith('_test'): - filename_cc = filename_cc[:-len('_test')] - filename_cc = filename_cc.replace('/public/', '/') - filename_cc = filename_cc.replace('/internal/', '/') - - if not filename_h.endswith('.h'): - return (False, '') - filename_h = filename_h[:-len('.h')] - if filename_h.endswith('-inl'): - filename_h = filename_h[:-len('-inl')] - filename_h = filename_h.replace('/public/', '/') - filename_h = filename_h.replace('/internal/', '/') - - files_belong_to_same_module = filename_cc.endswith(filename_h) - common_path = '' - if files_belong_to_same_module: - common_path = filename_cc[:-len(filename_h)] - return files_belong_to_same_module, common_path - - -def UpdateIncludeState(filename, include_dict, io=codecs): - """Fill up the include_dict with new includes found from the file. - - Args: - filename: the name of the header to read. - include_dict: a dictionary in which the headers are inserted. - io: The io factory to use to read the file. Provided for testability. - - Returns: - True if a header was successfully added. False otherwise. - """ - headerfile = None - try: - headerfile = io.open(filename, 'r', 'utf8', 'replace') - except IOError: - return False - linenum = 0 - for line in headerfile: - linenum += 1 - clean_line = CleanseComments(line) - match = _RE_PATTERN_INCLUDE.search(clean_line) - if match: - include = match.group(2) - include_dict.setdefault(include, linenum) - return True - - -def CheckForIncludeWhatYouUse(filename, - clean_lines, - include_state, - error, - io=codecs): - """Reports for missing stl includes. - - This function will output warnings to make sure you are including the headers - necessary for the stl containers and functions that you use. We only give one - reason to include a header. For example, if you use both equal_to<> and - less<> in a .h file, only one (the latter in the file) of these will be - reported as a reason to include the . - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - include_state: An _IncludeState instance. - error: The function to call with any errors found. - io: The IO factory to use to read the header file. Provided for unittest - injection. - """ - required = {} # A map of header name to linenumber and the template entity. - # Example of required: { '': (1219, 'less<>') } - - for linenum in xrange(clean_lines.NumLines()): - line = clean_lines.elided[linenum] - if not line or line[0] == '#': - continue - - # String is special -- it is a non-templatized type in STL. - matched = _RE_PATTERN_STRING.search(line) - if matched: - # Don't warn about strings in non-STL namespaces: - # (We check only the first match per line; good enough.) - prefix = line[:matched.start()] - if prefix.endswith('std::') or not prefix.endswith('::'): - required[''] = (linenum, 'string') - - for pattern, template, header in _re_pattern_algorithm_header: - if pattern.search(line): - required[header] = (linenum, template) - - # The following function is just a speed up, no semantics are changed. - if not '<' in line: # Reduces the cpu time usage by skipping lines. - continue - - for pattern, template, header in _re_pattern_templates: - if pattern.search(line): - required[header] = (linenum, template) - - # The policy is that if you #include something in foo.h you don't need to - # include it again in foo.cc. Here, we will look at possible includes. - # Let's flatten the include_state include_list and copy it into a dictionary. - include_dict = dict( - [item for sublist in include_state.include_list for item in sublist]) - - # Did we find the header for this file (if any) and successfully load it? - header_found = False - - # Use the absolute path so that matching works properly. - abs_filename = FileInfo(filename).FullName() - - # For Emacs's flymake. - # If cpplint is invoked from Emacs's flymake, a temporary file is generated - # by flymake and that file name might end with '_flymake.cc'. In that case, - # restore original file name here so that the corresponding header file can be - # found. - # e.g. If the file name is 'foo_flymake.cc', we should search for 'foo.h' - # instead of 'foo_flymake.h' - abs_filename = re.sub(r'_flymake\.cc$', '.cc', abs_filename) - - # include_dict is modified during iteration, so we iterate over a copy of - # the keys. - header_keys = include_dict.keys() - for header in header_keys: - (same_module, common_path) = FilesBelongToSameModule(abs_filename, - header) - fullpath = common_path + header - if same_module and UpdateIncludeState(fullpath, include_dict, io): - header_found = True - - # If we can't find the header file for a .cc, assume it's because we don't - # know where to look. In that case we'll give up as we're not sure they - # didn't include it in the .h file. - # TODO(unknown): Do a better job of finding .h files so we are confident that - # not having the .h file means there isn't one. - if filename.endswith('.cc') and not header_found: - return - - # All the lines have been processed, report the errors found. - for required_header_unstripped in required: - template = required[required_header_unstripped][1] - if required_header_unstripped.strip('<>"') not in include_dict: - error(filename, required[required_header_unstripped][0], - 'build/include_what_you_use', 4, 'Add #include ' + - required_header_unstripped + ' for ' + template) - - -_RE_PATTERN_EXPLICIT_MAKEPAIR = re.compile(r'\bmake_pair\s*<') - - -def CheckMakePairUsesDeduction(filename, clean_lines, linenum, error): - """Check that make_pair's template arguments are deduced. - - G++ 4.6 in C++11 mode fails badly if make_pair's template arguments are - specified explicitly, and such use isn't intended in any case. - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - error: The function to call with any errors found. - """ - line = clean_lines.elided[linenum] - match = _RE_PATTERN_EXPLICIT_MAKEPAIR.search(line) - if match: - error( - filename, - linenum, - 'build/explicit_make_pair', - 4, # 4 = high confidence - 'For C++11-compatibility, omit template arguments from make_pair' - ' OR use pair directly OR if appropriate, construct a pair directly') - - -def CheckDefaultLambdaCaptures(filename, clean_lines, linenum, error): - """Check that default lambda captures are not used. - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - error: The function to call with any errors found. - """ - line = clean_lines.elided[linenum] - - # A lambda introducer specifies a default capture if it starts with "[=" - # or if it starts with "[&" _not_ followed by an identifier. - match = Match(r'^(.*)\[\s*(?:=|&[^\w])', line) - if match: - # Found a potential error, check what comes after the lambda-introducer. - # If it's not open parenthesis (for lambda-declarator) or open brace - # (for compound-statement), it's not a lambda. - line, _, pos = CloseExpression(clean_lines, linenum, - len(match.group(1))) - if pos >= 0 and Match(r'^\s*[{(]', line[pos:]): - error( - filename, - linenum, - 'build/c++11', - 4, # 4 = high confidence - 'Default lambda captures are an unapproved C++ feature.') - - -def CheckRedundantVirtual(filename, clean_lines, linenum, error): - """Check if line contains a redundant "virtual" function-specifier. - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - error: The function to call with any errors found. - """ - # Look for "virtual" on current line. - line = clean_lines.elided[linenum] - virtual = Match(r'^(.*)(\bvirtual\b)(.*)$', line) - if not virtual: return - - # Ignore "virtual" keywords that are near access-specifiers. These - # are only used in class base-specifier and do not apply to member - # functions. - if (Search(r'\b(public|protected|private)\s+$', virtual.group(1)) or - Match(r'^\s+(public|protected|private)\b', virtual.group(3))): - return - - # Ignore the "virtual" keyword from virtual base classes. Usually - # there is a column on the same line in these cases (virtual base - # classes are rare in google3 because multiple inheritance is rare). - if Match(r'^.*[^:]:[^:].*$', line): return - - # Look for the next opening parenthesis. This is the start of the - # parameter list (possibly on the next line shortly after virtual). - # TODO(unknown): doesn't work if there are virtual functions with - # decltype() or other things that use parentheses, but csearch suggests - # that this is rare. - end_col = -1 - end_line = -1 - start_col = len(virtual.group(2)) - for start_line in xrange(linenum, min(linenum + 3, clean_lines.NumLines())): - line = clean_lines.elided[start_line][start_col:] - parameter_list = Match(r'^([^(]*)\(', line) - if parameter_list: - # Match parentheses to find the end of the parameter list - (_, end_line, end_col) = CloseExpression( - clean_lines, start_line, - start_col + len(parameter_list.group(1))) - break - start_col = 0 - - if end_col < 0: - return # Couldn't find end of parameter list, give up - - # Look for "override" or "final" after the parameter list - # (possibly on the next few lines). - for i in xrange(end_line, min(end_line + 3, clean_lines.NumLines())): - line = clean_lines.elided[i][end_col:] - match = Search(r'\b(override|final)\b', line) - if match: - error(filename, linenum, 'readability/inheritance', 4, - ('"virtual" is redundant since function is ' - 'already declared as "%s"' % match.group(1))) - - # Set end_col to check whole lines after we are done with the - # first line. - end_col = 0 - if Search(r'[^\w]\s*$', line): - break - - -def CheckRedundantOverrideOrFinal(filename, clean_lines, linenum, error): - """Check if line contains a redundant "override" or "final" virt-specifier. - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - error: The function to call with any errors found. - """ - # Look for closing parenthesis nearby. We need one to confirm where - # the declarator ends and where the virt-specifier starts to avoid - # false positives. - line = clean_lines.elided[linenum] - declarator_end = line.rfind(')') - if declarator_end >= 0: - fragment = line[declarator_end:] - else: - if linenum > 1 and clean_lines.elided[linenum - 1].rfind(')') >= 0: - fragment = line - else: - return - - # Check that at most one of "override" or "final" is present, not both - if Search(r'\boverride\b', fragment) and Search(r'\bfinal\b', fragment): - error(filename, linenum, 'readability/inheritance', 4, - ('"override" is redundant since function is ' - 'already declared as "final"')) - - -# Returns true if we are at a new block, and it is directly -# inside of a namespace. -def IsBlockInNameSpace(nesting_state, is_forward_declaration): - """Checks that the new block is directly in a namespace. - - Args: - nesting_state: The _NestingState object that contains info about our state. - is_forward_declaration: If the class is a forward declared class. - Returns: - Whether or not the new block is directly in a namespace. - """ - if is_forward_declaration: - if len(nesting_state.stack) >= 1 and ( - isinstance(nesting_state.stack[-1], _NamespaceInfo)): - return True - else: - return False - - return (len(nesting_state.stack) > 1 and - nesting_state.stack[-1].check_namespace_indentation and - isinstance(nesting_state.stack[-2], _NamespaceInfo)) - - -def ShouldCheckNamespaceIndentation(nesting_state, is_namespace_indent_item, - raw_lines_no_comments, linenum): - """This method determines if we should apply our namespace indentation check. - - Args: - nesting_state: The current nesting state. - is_namespace_indent_item: If we just put a new class on the stack, True. - If the top of the stack is not a class, or we did not recently - add the class, False. - raw_lines_no_comments: The lines without the comments. - linenum: The current line number we are processing. - - Returns: - True if we should apply our namespace indentation check. Currently, it - only works for classes and namespaces inside of a namespace. - """ - - is_forward_declaration = IsForwardClassDeclaration(raw_lines_no_comments, - linenum) - - if not (is_namespace_indent_item or is_forward_declaration): - return False - - # If we are in a macro, we do not want to check the namespace indentation. - if IsMacroDefinition(raw_lines_no_comments, linenum): - return False - - return IsBlockInNameSpace(nesting_state, is_forward_declaration) - - -# Call this method if the line is directly inside of a namespace. -# If the line above is blank (excluding comments) or the start of -# an inner namespace, it cannot be indented. -def CheckItemIndentationInNamespace(filename, raw_lines_no_comments, linenum, - error): - line = raw_lines_no_comments[linenum] - if Match(r'^\s+', line): - error(filename, linenum, 'runtime/indentation_namespace', 4, - 'Do not indent within a namespace') - - -def ProcessLine(filename, - file_extension, - clean_lines, - line, - include_state, - function_state, - nesting_state, - error, - extra_check_functions=[]): - """Processes a single line in the file. - - Args: - filename: Filename of the file that is being processed. - file_extension: The extension (dot not included) of the file. - clean_lines: An array of strings, each representing a line of the file, - with comments stripped. - line: Number of line being processed. - include_state: An _IncludeState instance in which the headers are inserted. - function_state: A _FunctionState instance which counts function lines, etc. - nesting_state: A NestingState instance which maintains information about - the current stack of nested blocks being parsed. - error: A callable to which errors are reported, which takes 4 arguments: - filename, line number, error level, and message - extra_check_functions: An array of additional check functions that will be - run on each source line. Each function takes 4 - arguments: filename, clean_lines, line, error - """ - raw_lines = clean_lines.raw_lines - ParseNolintSuppressions(filename, raw_lines[line], line, error) - nesting_state.Update(filename, clean_lines, line, error) - CheckForNamespaceIndentation(filename, nesting_state, clean_lines, line, - error) - if nesting_state.InAsmBlock(): return - CheckForFunctionLengths(filename, clean_lines, line, function_state, error) - CheckForMultilineCommentsAndStrings(filename, clean_lines, line, error) - CheckStyle(filename, clean_lines, line, file_extension, nesting_state, - error) - CheckLanguage(filename, clean_lines, line, file_extension, include_state, - nesting_state, error) - CheckForNonConstReference(filename, clean_lines, line, nesting_state, error) - CheckForNonStandardConstructs(filename, clean_lines, line, nesting_state, - error) - CheckVlogArguments(filename, clean_lines, line, error) - CheckPosixThreading(filename, clean_lines, line, error) - CheckInvalidIncrement(filename, clean_lines, line, error) - CheckMakePairUsesDeduction(filename, clean_lines, line, error) - CheckDefaultLambdaCaptures(filename, clean_lines, line, error) - CheckRedundantVirtual(filename, clean_lines, line, error) - CheckRedundantOverrideOrFinal(filename, clean_lines, line, error) - for check_fn in extra_check_functions: - check_fn(filename, clean_lines, line, error) - - -def FlagCxx11Features(filename, clean_lines, linenum, error): - """Flag those c++11 features that we only allow in certain places. - - Args: - filename: The name of the current file. - clean_lines: A CleansedLines instance containing the file. - linenum: The number of the line to check. - error: The function to call with any errors found. - """ - line = clean_lines.elided[linenum] - - # Flag unapproved C++11 headers. - include = Match(r'\s*#\s*include\s+[<"]([^<"]+)[">]', line) - if include and include.group(1) in ( - 'cfenv', - 'condition_variable', - 'fenv.h', - 'future', - 'mutex', - 'thread', - 'chrono', - 'ratio', - 'regex', - 'system_error', ): - error(filename, linenum, 'build/c++11', 5, - ('<%s> is an unapproved C++11 header.') % include.group(1)) - - # The only place where we need to worry about C++11 keywords and library - # features in preprocessor directives is in macro definitions. - if Match(r'\s*#', line) and not Match(r'\s*#\s*define\b', line): return - - # These are classes and free functions. The classes are always - # mentioned as std::*, but we only catch the free functions if - # they're not found by ADL. They're alphabetical by header. - for top_name in ( - # type_traits - 'alignment_of', - 'aligned_union', ): - if Search(r'\bstd::%s\b' % top_name, line): - error(filename, linenum, 'build/c++11', 5, ( - 'std::%s is an unapproved C++11 class or function. Send c-style ' - 'an example of where it would make your code more readable, and ' - 'they may let you use it.') % top_name) - - -def ProcessFileData(filename, - file_extension, - lines, - error, - extra_check_functions=[]): - """Performs lint checks and reports any errors to the given error function. - - Args: - filename: Filename of the file that is being processed. - file_extension: The extension (dot not included) of the file. - lines: An array of strings, each representing a line of the file, with the - last element being empty if the file is terminated with a newline. - error: A callable to which errors are reported, which takes 4 arguments: - filename, line number, error level, and message - extra_check_functions: An array of additional check functions that will be - run on each source line. Each function takes 4 - arguments: filename, clean_lines, line, error - """ - lines = (['// marker so line numbers and indices both start at 1'] + lines + - ['// marker so line numbers end in a known way']) - - include_state = _IncludeState() - function_state = _FunctionState() - nesting_state = NestingState() - - ResetNolintSuppressions() - - CheckForCopyright(filename, lines, error) - - RemoveMultiLineComments(filename, lines, error) - clean_lines = CleansedLines(lines) - - if file_extension == 'h': - CheckForHeaderGuard(filename, clean_lines, error) - - for line in xrange(clean_lines.NumLines()): - ProcessLine(filename, file_extension, clean_lines, line, include_state, - function_state, nesting_state, error, extra_check_functions) - FlagCxx11Features(filename, clean_lines, line, error) - nesting_state.CheckCompletedBlocks(filename, error) - - CheckForIncludeWhatYouUse(filename, clean_lines, include_state, error) - - # Check that the .cc file has included its header if it exists. - if file_extension == 'cc': - CheckHeaderFileIncluded(filename, include_state, error) - - # We check here rather than inside ProcessLine so that we see raw - # lines rather than "cleaned" lines. - CheckForBadCharacters(filename, lines, error) - - CheckForNewlineAtEOF(filename, lines, error) - - -def ProcessConfigOverrides(filename): - """ Loads the configuration files and processes the config overrides. - - Args: - filename: The name of the file being processed by the linter. - - Returns: - False if the current |filename| should not be processed further. - """ - - abs_filename = os.path.abspath(filename) - cfg_filters = [] - keep_looking = True - while keep_looking: - abs_path, base_name = os.path.split(abs_filename) - if not base_name: - break # Reached the root directory. - - cfg_file = os.path.join(abs_path, "CPPLINT.cfg") - abs_filename = abs_path - if not os.path.isfile(cfg_file): - continue - - try: - with open(cfg_file) as file_handle: - for line in file_handle: - line, _, _ = line.partition('#') # Remove comments. - if not line.strip(): - continue - - name, _, val = line.partition('=') - name = name.strip() - val = val.strip() - if name == 'set noparent': - keep_looking = False - elif name == 'filter': - cfg_filters.append(val) - elif name == 'exclude_files': - # When matching exclude_files pattern, use the base_name of - # the current file name or the directory name we are processing. - # For example, if we are checking for lint errors in /foo/bar/baz.cc - # and we found the .cfg file at /foo/CPPLINT.cfg, then the config - # file's "exclude_files" filter is meant to be checked against "bar" - # and not "baz" nor "bar/baz.cc". - if base_name: - pattern = re.compile(val) - if pattern.match(base_name): - sys.stderr.write( - 'Ignoring "%s": file excluded by "%s". ' - 'File path component "%s" matches ' - 'pattern "%s"\n' % - (filename, cfg_file, base_name, val)) - return False - elif name == 'linelength': - global _line_length - try: - _line_length = int(val) - except ValueError: - sys.stderr.write('Line length must be numeric.') - else: - sys.stderr.write( - 'Invalid configuration option (%s) in file %s\n' % - (name, cfg_file)) - - except IOError: - sys.stderr.write( - "Skipping config file '%s': Can't open for reading\n" % - cfg_file) - keep_looking = False - - # Apply all the accumulated filters in reverse order (top-level directory - # config options having the least priority). - for filter in reversed(cfg_filters): - _AddFilters(filter) - - return True - - -def ProcessFile(filename, vlevel, extra_check_functions=[]): - """Does google-lint on a single file. - - Args: - filename: The name of the file to parse. - - vlevel: The level of errors to report. Every error of confidence - >= verbose_level will be reported. 0 is a good default. - - extra_check_functions: An array of additional check functions that will be - run on each source line. Each function takes 4 - arguments: filename, clean_lines, line, error - """ - - _SetVerboseLevel(vlevel) - _BackupFilters() - - if not ProcessConfigOverrides(filename): - _RestoreFilters() - return - - lf_lines = [] - crlf_lines = [] - try: - # Support the UNIX convention of using "-" for stdin. Note that - # we are not opening the file with universal newline support - # (which codecs doesn't support anyway), so the resulting lines do - # contain trailing '\r' characters if we are reading a file that - # has CRLF endings. - # If after the split a trailing '\r' is present, it is removed - # below. - if filename == '-': - lines = codecs.StreamReaderWriter(sys.stdin, - codecs.getreader('utf8'), - codecs.getwriter('utf8'), - 'replace').read().split('\n') - else: - lines = codecs.open(filename, 'r', 'utf8', - 'replace').read().split('\n') - - # Remove trailing '\r'. - # The -1 accounts for the extra trailing blank line we get from split() - for linenum in range(len(lines) - 1): - if lines[linenum].endswith('\r'): - lines[linenum] = lines[linenum].rstrip('\r') - crlf_lines.append(linenum + 1) - else: - lf_lines.append(linenum + 1) - - except IOError: - sys.stderr.write("Skipping input '%s': Can't open for reading\n" % - filename) - _RestoreFilters() - return - - # Note, if no dot is found, this will give the entire filename as the ext. - file_extension = filename[filename.rfind('.') + 1:] - - # When reading from stdin, the extension is unknown, so no cpplint tests - # should rely on the extension. - if filename != '-' and file_extension not in _valid_extensions: - sys.stderr.write('Ignoring %s; not a valid file name ' - '(%s)\n' % (filename, ', '.join(_valid_extensions))) - else: - ProcessFileData(filename, file_extension, lines, Error, - extra_check_functions) - - # If end-of-line sequences are a mix of LF and CR-LF, issue - # warnings on the lines with CR. - # - # Don't issue any warnings if all lines are uniformly LF or CR-LF, - # since critique can handle these just fine, and the style guide - # doesn't dictate a particular end of line sequence. - # - # We can't depend on os.linesep to determine what the desired - # end-of-line sequence should be, since that will return the - # server-side end-of-line sequence. - if lf_lines and crlf_lines: - # Warn on every line with CR. An alternative approach might be to - # check whether the file is mostly CRLF or just LF, and warn on the - # minority, we bias toward LF here since most tools prefer LF. - for linenum in crlf_lines: - Error(filename, linenum, 'whitespace/newline', 1, - 'Unexpected \\r (^M) found; better to use only \\n') - - sys.stdout.write('Done processing %s\n' % filename) - _RestoreFilters() - - -def PrintUsage(message): - """Prints a brief usage string and exits, optionally with an error message. - - Args: - message: The optional error message. - """ - sys.stderr.write(_USAGE) - if message: - sys.exit('\nFATAL ERROR: ' + message) - else: - sys.exit(1) - - -def PrintCategories(): - """Prints a list of all the error-categories used by error messages. - - These are the categories used to filter messages via --filter. - """ - sys.stderr.write(''.join(' %s\n' % cat for cat in _ERROR_CATEGORIES)) - sys.exit(0) - - -def ParseArguments(args): - """Parses the command line arguments. - - This may set the output format and verbosity level as side-effects. - - Args: - args: The command line arguments: - - Returns: - The list of filenames to lint. - """ - try: - (opts, filenames) = getopt.getopt(args, '', [ - 'help', 'output=', 'verbose=', 'counting=', 'filter=', 'root=', - 'linelength=', 'extensions=', 'write-success=' - ]) - except getopt.GetoptError: - PrintUsage('Invalid arguments.') - - verbosity = _VerboseLevel() - output_format = _OutputFormat() - filters = '' - counting_style = '' - - for (opt, val) in opts: - if opt == '--help': - PrintUsage(None) - elif opt == '--output': - if val not in ('emacs', 'vs7', 'eclipse'): - PrintUsage( - 'The only allowed output formats are emacs, vs7 and eclipse.' - ) - output_format = val - elif opt == '--verbose': - verbosity = int(val) - elif opt == '--filter': - filters = val - if not filters: - PrintCategories() - elif opt == '--counting': - if val not in ('total', 'toplevel', 'detailed'): - PrintUsage( - 'Valid counting options are total, toplevel, and detailed') - counting_style = val - elif opt == '--root': - global _root - _root = val - elif opt == '--linelength': - global _line_length - try: - _line_length = int(val) - except ValueError: - PrintUsage('Line length must be digits.') - elif opt == '--extensions': - global _valid_extensions - try: - _valid_extensions = set(val.split(',')) - except ValueError: - PrintUsage('Extensions must be comma seperated list.') - elif opt == '--write-success': - global _write_success - _write_success = val - - if not filenames: - PrintUsage('No files were specified.') - - _SetOutputFormat(output_format) - _SetVerboseLevel(verbosity) - _SetFilters(filters) - _SetCountingStyle(counting_style) - - return filenames - - -def main(): - filenames = ParseArguments(sys.argv[1:]) - - # Change stderr to write with replacement characters so we don't die - # if we try to print something containing non-ASCII characters. - sys.stderr = codecs.StreamReaderWriter(sys.stderr, - codecs.getreader('utf8'), - codecs.getwriter('utf8'), 'replace') - - _cpplint_state.ResetErrorCounts() - for filename in filenames: - ProcessFile(filename, _cpplint_state.verbose_level) - _cpplint_state.PrintErrorCounts() - - if _cpplint_state.error_count == 0 and _write_success is not None: - with open(_write_success, 'a'): - os.utime(_write_success, None) - - sys.exit(_cpplint_state.error_count > 0) - - -if __name__ == '__main__': - main() diff --git a/paddle/scripts/paddle_build.sh b/paddle/scripts/paddle_build.sh index 33e0ec4ee226126374413382fe8fcbdebdf50f9e..9899eee8841147a509b7997fd905a1b68bc098da 100755 --- a/paddle/scripts/paddle_build.sh +++ b/paddle/scripts/paddle_build.sh @@ -259,6 +259,7 @@ function check_style() { eval "$(GIMME_GO_VERSION=1.8.3 gimme)" fi + pip install cpplint # set up go environment for running gometalinter mkdir -p $GOPATH/src/github.com/PaddlePaddle/ ln -sf ${PADDLE_ROOT} $GOPATH/src/github.com/PaddlePaddle/Paddle @@ -414,10 +415,11 @@ function assert_api_not_changed() { source .env/bin/activate pip install ${PADDLE_ROOT}/build/python/dist/*whl python ${PADDLE_ROOT}/tools/print_signatures.py paddle.fluid,paddle.reader > new.spec + if [ "$1" == "cp35-cp35m" ] || [ "$1" == "cp36-cp36m" ] || [ "$1" == "cp37-cp37m" ]; then # Use sed to make python2 and python3 sepc keeps the same sed -i 's/arg0: str/arg0: unicode/g' new.spec - sed -i "s/\(.*Transpiler.*\).__init__ ArgSpec(args=\['self'].*/\1.__init__ /g" new.spec + sed -i "s/\(.*Transpiler.*\).__init__ (ArgSpec(args=\['self'].*/\1.__init__ /g" new.spec fi # ComposeNotAligned has significant difference between py2 and py3 sed -i '/.*ComposeNotAligned.*/d' new.spec @@ -431,8 +433,8 @@ function assert_api_spec_approvals() { BRANCH="develop" fi - API_FILES=("cmake/external" - "paddle/fluid/API.spec" + API_FILES=("paddle/fluid/API.spec" + "python/paddle/fluid/parallel_executor.py" "paddle/fluid/framework/operator.h" "paddle/fluid/framework/tensor.h" "paddle/fluid/framework/lod_tensor.h" @@ -451,12 +453,21 @@ function assert_api_spec_approvals() { echo "checking ${API_FILE} change, PR: ${GIT_PR_ID}, changes: ${API_CHANGE}" if [ ${API_CHANGE} ] && [ "${GIT_PR_ID}" != "" ]; then # NOTE: per_page=10000 should be ok for all cases, a PR review > 10000 is not human readable. - APPROVALS=`curl -H "Authorization: token ${GITHUB_API_TOKEN}" https://api.github.com/repos/PaddlePaddle/Paddle/pulls/${GIT_PR_ID}/reviews?per_page=10000 | \ - python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 2887803` + if [ "$API_FILE" == "paddle/fluid/API.spec" ];then + APPROVALS=`curl -H "Authorization: token ${GITHUB_API_TOKEN}" https://api.github.com/repos/PaddlePaddle/Paddle/pulls/${GIT_PR_ID}/reviews?per_page=10000 | \ + python ${PADDLE_ROOT}/tools/check_pr_approval.py 2 2887803 35982308` + else + APPROVALS=`curl -H "Authorization: token ${GITHUB_API_TOKEN}" https://api.github.com/repos/PaddlePaddle/Paddle/pulls/${GIT_PR_ID}/reviews?per_page=10000 | \ + python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 2887803` + fi echo "current pr ${GIT_PR_ID} got approvals: ${APPROVALS}" if [ "${APPROVALS}" == "FALSE" ]; then + if [ "$API_FILE" == "paddle/fluid/API.spec" ];then + echo "You must have panyx0718 and shanyi15 approval for the api change! ${API_FILE}" + else echo "You must have panyx0718 approval for the api change! ${API_FILE}" - exit 1 + fi + exit 1 fi fi done @@ -471,19 +482,6 @@ function assert_api_spec_approvals() { exit 1 fi fi - - pip install ${PADDLE_ROOT}/build/opt/paddle/share/wheels/*.whl - CHECK_DOCK_MD5=`python ${PADDLE_ROOT}/tools/check_doc_approval.py` - if [ "True" != ${CHECK_DOCK_MD5} ]; then - APPROVALS=`curl -H "Authorization: token ${GITHUB_API_TOKEN}" https://api.github.com/repos/PaddlePaddle/Paddle/pulls/${GIT_PR_ID}/reviews?per_page=10000 | \ - python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 35982308` - echo "current pr ${GIT_PR_ID} got approvals: ${APPROVALS}" - if [ "${APPROVALS}" == "FALSE" ]; then - echo "You must have shanyi15 approval for the api doc change! " - exit 1 - fi - echo ${CHECK_DOCK_MD5} >/root/.cache/doc_md5.txt - fi } diff --git a/python/paddle/fluid/__init__.py b/python/paddle/fluid/__init__.py index a9c92efb7218213e1865d4757f1bda2a19b07e93..d12f04a6abefecbb8e3e43fd2f0b87e43264b07f 100644 --- a/python/paddle/fluid/__init__.py +++ b/python/paddle/fluid/__init__.py @@ -125,14 +125,13 @@ def __bootstrap__(): os.environ['OMP_NUM_THREADS'] = str(num_threads) sysstr = platform.system() read_env_flags = [ - 'check_nan_inf', 'benchmark', 'eager_delete_scope', 'use_mkldnn', - 'use_ngraph', 'initial_cpu_memory_in_mb', 'init_allocated_mem', - 'free_idle_memory', 'paddle_num_threads', "dist_threadpool_size", - 'eager_delete_tensor_gb', 'fast_eager_deletion_mode', - 'allocator_strategy', 'reader_queue_speed_test_mode', - 'print_sub_graph_dir', 'pe_profile_fname', 'warpctc_dir', - 'inner_op_parallelism', 'enable_parallel_graph', - 'multiple_of_cupti_buffer_size' + 'check_nan_inf', 'benchmark', 'eager_delete_scope', 'use_ngraph', + 'initial_cpu_memory_in_mb', 'init_allocated_mem', 'free_idle_memory', + 'paddle_num_threads', "dist_threadpool_size", 'eager_delete_tensor_gb', + 'fast_eager_deletion_mode', 'allocator_strategy', + 'reader_queue_speed_test_mode', 'print_sub_graph_dir', + 'pe_profile_fname', 'warpctc_dir', 'inner_op_parallelism', + 'enable_parallel_graph', 'multiple_of_cupti_buffer_size' ] if 'Darwin' not in sysstr: read_env_flags.append('use_pinned_memory') @@ -140,6 +139,9 @@ def __bootstrap__(): if os.name != 'nt': read_env_flags.append('cpu_deterministic') + if core.is_compiled_with_mkldnn(): + read_env_flags.append('use_mkldnn') + if core.is_compiled_with_dist(): read_env_flags.append('rpc_deadline') read_env_flags.append('rpc_server_profile_path') diff --git a/python/paddle/fluid/compiler.py b/python/paddle/fluid/compiler.py index ab4011383824c67a81feba7f2a20d3aff5f4fc8f..1b7bdfc336a6851d189795a6e65a42b3e92834e9 100644 --- a/python/paddle/fluid/compiler.py +++ b/python/paddle/fluid/compiler.py @@ -17,7 +17,6 @@ import os import six import sys from .. import compat as cpt -from . import framework from . import core from . import framework @@ -36,6 +35,30 @@ def _place_obj(place): return p +def _is_pserver_mode(main_program): + main = main_program if main_program \ + else default_main_program() + for op in main.global_block().ops: + if op.type in ["send", "recv"]: + return True + return False + + +def get_available_places(use_cuda): + if use_cuda: + gpus_env = os.getenv("FLAGS_selected_gpus") + if gpus_env: + gpus = [int(s) for s in gpus_env.split(",")] + else: + gpus = [i for i in six.moves.range(core.get_cuda_device_count())] + places = [core.CUDAPlace(i) for i in gpus] + else: + cpu_num = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count())) + places = [core.CPUPlace() for _ in six.moves.range(cpu_num)] + assert places, "no place for execution" + return places + + class CompiledProgram(object): """ Compiles to Graph for execution. @@ -127,8 +150,7 @@ class CompiledProgram(object): self._exec_strategy = ExecutionStrategy() if self._build_strategy is None: self._build_strategy = BuildStrategy() - self._build_strategy.is_distribution = framework.is_pserver_mode( - self._program) + self._build_strategy.is_distribution = _is_pserver_mode(self._program) return self def with_inference_optimize(self, config): @@ -153,9 +175,9 @@ class CompiledProgram(object): def _with_distributed(self): raise NotImplementedError() - def _compile_data_parallel(self): + def _compile_data_parallel(self, use_cuda=False, scope=None): if self._share_vars_from: - if self._scope: + if scope: sys.stderr.write("share_vars_from is set, scope is ignored.\n") if not self._share_vars_from._is_data_parallel: raise ValueError("share_vars_from is not data parallel. Cannot " @@ -166,23 +188,11 @@ class CompiledProgram(object): "var to share.") self._local_scopes = self._share_vars_from._executor.local_scopes() else: + assert scope is not None, "" self._local_scopes = [] - self._exec_strategy.use_cuda = isinstance(self._place, core.CUDAPlace) - if self._exec_strategy.use_cuda: - gpus_env = os.getenv("FLAGS_selected_gpus") - if gpus_env: - gpus = [int(s) for s in gpus_env.split(",")] - else: - gpus = [ - i for i in six.moves.range(core.get_cuda_device_count()) - ] - self._places = [core.CUDAPlace(i) for i in gpus] - else: - cpu_num = int( - os.environ.get('CPU_NUM', multiprocessing.cpu_count())) - self._places = [core.CPUPlace() for _ in six.moves.range(cpu_num)] - assert self._places, "no place for execution" + self._exec_strategy.use_cuda = use_cuda + self._places = get_available_places(self._exec_strategy.use_cuda) if self._exec_strategy.num_threads == 0: if self._exec_strategy.use_cuda: @@ -197,9 +207,11 @@ class CompiledProgram(object): # FIXME(dzhwinter): enable_inplace should be after memory_optimize # if turn on python memory optimize, turn off the inplace_pass. if self._build_strategy.memory_optimize is None: - self._build_strategy.memory_optimize = False if self._program and self._program._is_mem_optimized else True + self._build_strategy.memory_optimize = False \ + if self._program and self._program._is_mem_optimized else True if self._build_strategy.enable_inplace is None: - self._build_strategy.enable_inplace = False if self._program and self._program._is_mem_optimized else True + self._build_strategy.enable_inplace = False \ + if self._program and self._program._is_mem_optimized else True # TODO(wuyi): trainer endpoings should be passed in through # build_strategy, not program.xxx. @@ -221,12 +233,12 @@ class CompiledProgram(object): places = list(map(_place_obj, self._places)) - return core.ParallelExecutor( - places, - set(self._persistable_vars), - cpt.to_text(self._loss_name) - if self._loss_name else six.u(''), self._scope, self._local_scopes, - self._exec_strategy, self._build_strategy, self._graph) + return core.ParallelExecutor(places, + set(self._persistable_vars), + cpt.to_text(self._loss_name) + if self._loss_name else six.u(''), scope, + self._local_scopes, self._exec_strategy, + self._build_strategy, self._graph) def _compile_inference(self): return core.create_paddle_predictor(self._infer_config) @@ -253,7 +265,9 @@ class CompiledProgram(object): self._scope = scope self._place = place if self._is_data_parallel: - self._executor = self._compile_data_parallel() + self._executor = self._compile_data_parallel( + use_cuda=isinstance(self._place, core.CUDAPlace), + scope=self._scope) elif self._is_inference: self._executor = self._compile_inference() else: diff --git a/python/paddle/fluid/contrib/slim/quantization/quantization_pass.py b/python/paddle/fluid/contrib/slim/quantization/quantization_pass.py index 18b58e6f388bbe9495333b12f32d63b74fddcb3a..622add48430c63a0c4293457127a49dd8d851e35 100644 --- a/python/paddle/fluid/contrib/slim/quantization/quantization_pass.py +++ b/python/paddle/fluid/contrib/slim/quantization/quantization_pass.py @@ -17,7 +17,9 @@ import numpy as np import six from ..... import compat as cpt from .... import core +from .... import Executor from ....framework import IrGraph +from ....framework import IrNode from ....framework import Program from ....initializer import Constant from .... import unique_name @@ -31,7 +33,7 @@ __all__ = [ class QuantizationTransformPass(object): def __init__(self, scope=None, - program_exe=None, + place=None, weight_bits=8, activation_bits=8, activation_quantize_type='abs_max', @@ -45,7 +47,7 @@ class QuantizationTransformPass(object): scope(fluid.Scope): When activation use 'range_abs_max' as the quantize type, this pass will create some new parameters. The scope is used to initialize these new parameters. - program_exe(fluid.Executor): program_exe is used to initialize new + place(fluid.CPUPlace|fluid.CUDAPlace): place is used to initialize new parameters described above. weight_bits (int): quantization bit number for weights, the bias is not quantized. @@ -71,13 +73,13 @@ class QuantizationTransformPass(object): from paddle.fluid import core graph = IrGraph(core.Graph(program.desc), for_test=False) - exe = fluid.Executor(fluid.CPUPlace()) + place = fluid.CPUPlace() transform_pass = QuantizationTransformPass(fluid.global_scope(), - exe) + place) transform_pass.apply(graph) """ self._scope = scope - self._program_exe = program_exe + self._place = place self._weight_bits = weight_bits self._activation_bits = activation_bits @@ -118,7 +120,7 @@ class QuantizationTransformPass(object): self._is_test = graph.is_test() # marked the variable which has been dequantized. dequantized_vars = collections.OrderedDict() - persistable_vars = [p.name() for p in graph.all_persistable_vars()] + persistable_vars = [p.name() for p in graph.all_persistable_nodes()] def _transform_forward(graph, op): for var_node in op.inputs: @@ -149,7 +151,7 @@ class QuantizationTransformPass(object): if not self._is_test: self._create_global_step(graph) - ops = graph.all_ops() + ops = graph.all_op_nodes() # The process of _transform_forward and _transform_backward is needed in two for loops. # The loop for transforming the forward graph: for op in ops: @@ -163,8 +165,8 @@ class QuantizationTransformPass(object): if len(self._need_initialized) > 0: assert self._scope is not None, \ 'The scope cannot be set None when activation_quantize_type equals to range_abs_max.' - assert self._program_exe is not None, \ - 'The program_exe cannot be set None when activation_quantize_type equals to range_abs_max.' + assert self._place is not None, \ + 'The place cannot be set None when activation_quantize_type equals to range_abs_max.' init_program = Program() for var_desc, initializer in six.iteritems(self._need_initialized): var = init_program.global_block().create_var( @@ -175,7 +177,8 @@ class QuantizationTransformPass(object): lod_level=var_desc.lod_level(), persistable=var_desc.persistable()) initializer(var, init_program.global_block()) - self._program_exe.run(program=init_program, scope=self._scope) + exe = Executor(self._place) + exe.run(program=init_program, scope=self._scope) return graph @@ -183,11 +186,11 @@ class QuantizationTransformPass(object): if self._weight_quantize_type == 'range_abs_max' or \ self._activation_quantize_type == 'range_abs_max': counter_name = cpt.to_text('@STEP_COUNTER@') - for node in graph.all_vars(): + for node in graph.all_var_nodes(): if node.name() == counter_name: self._global_step = node if self._global_step is None: - global_step_in = graph.create_param_node( + global_step_in = graph.create_persistable_node( name=counter_name, var_type=core.VarDesc.VarType.LOD_TENSOR, shape=[1], @@ -228,14 +231,14 @@ class QuantizationTransformPass(object): quant_var_node = graph.create_var_node( name=self._quantized_var_name(var_node.name()), - var_type=var_node.var().type(), - shape=var_node.var().shape(), - var_dtype=var_node.var().dtype()) + var_type=var_node.type(), + shape=var_node.shape(), + var_dtype=var_node.dtype()) scale_var_node = graph.create_var_node( name=self._quantized_scale_name(var_node.name()), - var_type=var_node.var().type(), - shape=var_node.var().shape(), - var_dtype=var_node.var().dtype()) + var_type=var_node.type(), + shape=var_node.shape(), + var_dtype=var_node.dtype()) quant_op_node = graph.create_op_node( op_type='fake_quantize_abs_max', attrs={ @@ -258,15 +261,15 @@ class QuantizationTransformPass(object): quant_var_node = graph.create_var_node( name=self._quantized_var_name(var_node.name()), - var_type=var_node.var().type(), - shape=var_node.var().shape(), - var_dtype=var_node.var().dtype()) + var_type=var_node.type(), + shape=var_node.shape(), + var_dtype=var_node.dtype()) - scale_in_node = graph.create_param_node( + scale_in_node = graph.create_persistable_node( name=self._quantized_scale_name(var_node.name()), var_type=core.VarDesc.VarType.LOD_TENSOR, shape=[1], - var_dtype=var_node.var().dtype()) + var_dtype=var_node.dtype()) self._need_initialized[scale_in_node.var()] = Constant(value=0.001) scale_out_node = graph.create_var_node_from_desc(scale_in_node.var()) @@ -275,11 +278,11 @@ class QuantizationTransformPass(object): if not self._is_test: # The name of scales_var_node maybe 'scales_0', 'scales_1', etc. - scales_node = graph.create_param_node( + scales_node = graph.create_persistable_node( name=unique_name.generate('scales'), var_type=core.VarDesc.VarType.LOD_TENSOR, shape=[self._window_size], - var_dtype=var_node.var().dtype()) + var_dtype=var_node.dtype()) self._need_initialized[scales_node.var()] = Constant(value=0) inputs['Iter'] = self._global_step outputs['OutScales'] = scales_node @@ -314,9 +317,9 @@ class QuantizationTransformPass(object): dequant_var_node = graph.create_var_node( name=self._dequantized_var_name(var_node.name()), - var_type=var_node.var().type(), - shape=var_node.var().shape(), - var_dtype=var_node.var().dtype()) + var_type=var_node.type(), + shape=var_node.shape(), + var_dtype=var_node.dtype()) max_range = (1 << (quant_bits - 1)) - 1 dequant_op_node = graph.create_op_node( op_type='fake_dequantize_max_abs', @@ -400,22 +403,22 @@ class QuantizationFreezePass(object): Args: graph(IrGraph): the applied graph. """ - persistable_vars = [p.name() for p in graph.all_persistable_vars()] - ops = graph.all_ops() + persistable_vars = [p.name() for p in graph.all_persistable_nodes()] + ops = graph.all_op_nodes() for op_node in ops: op_name = op_node.name() if op_name in self._fake_quant_op_names: - input_arg_name = op_node.op().input('X')[0] + input_arg_name = op_node.input('X')[0] if input_arg_name in persistable_vars: if self._weight_quantize_type == 'abs_max': param = self._load_var(input_arg_name) scale_v = np.max(np.abs(param)) else: - scale_v = self._load_var(op_node.op().output('OutScale') - [0])[0] + scale_v = self._load_var( + op_node.output('OutScale')[0])[0] self._var_scale_map[input_arg_name] = scale_v else: - scale_v = graph.var_node(op_node.op().output('OutScale')[0]) + scale_v = graph.var_node(op_node.output('OutScale')[0]) self._var_scale_map[input_arg_name] = scale_v if input_arg_name in persistable_vars: self._remove_fake_quant_and_dequant_op(graph, op_node) @@ -425,13 +428,13 @@ class QuantizationFreezePass(object): self._weight_bits) self._restore_var(input_arg_name, quantized_param_v) - ops = graph.all_ops() + ops = graph.all_op_nodes() for op_node in ops: op_name = op_node.name() if op_name in self._fake_dequant_op_names: self._remove_fake_quant_and_dequant_op(graph, op_node) - ops = graph.all_ops() + ops = graph.all_op_nodes() for op_node in ops: op_name = op_node.name() if op_name in self._quantizable_ops: @@ -451,8 +454,8 @@ class QuantizationFreezePass(object): return graph def _remove_fake_quant_and_dequant_op(self, graph, op_node): - k = op_node.op().output('Out')[0] - v = op_node.op().input('X')[0] + k = op_node.output('Out')[0] + v = op_node.input('X')[0] if v not in self._op_input_rename_map: self._op_input_rename_map[k] = v else: @@ -462,7 +465,7 @@ class QuantizationFreezePass(object): def _insert_post_dequant_op(self, graph, op_node): max_range = None scale_var_node = None - persistable_vars = [p.name() for p in graph.all_persistable_vars()] + persistable_vars = [p.name() for p in graph.all_persistable_nodes()] for var_node in op_node.inputs: name = var_node.name() if name in self._op_input_rename_map: @@ -480,7 +483,7 @@ class QuantizationFreezePass(object): original_var_name) max_range = param_range * act_range / scale_v else: - assert isinstance(scale_v, core.Node) + assert isinstance(scale_v, IrNode) scale_var_node = self._var_scale_map[original_var_name] if len(op_node.outputs) != 1: @@ -490,9 +493,9 @@ class QuantizationFreezePass(object): output_var_node = op_node.outputs[0] dequant_var_node = graph.create_var_node( name=self._dequantized_var_name(output_var_node.name()), - var_type=output_var_node.var().type(), - shape=output_var_node.var().shape(), - var_dtype=output_var_node.var().dtype()) + var_type=output_var_node.type(), + shape=output_var_node.shape(), + var_dtype=output_var_node.dtype()) dequant_op_node = graph.create_op_node( op_type='fake_dequantize_max_abs', attrs={ @@ -517,14 +520,19 @@ class QuantizationFreezePass(object): def _remove_unused_var_nodes(self, graph): all_used_vars = set() - ops = graph.all_ops() + ops = graph.all_op_nodes() for op_node in ops: for input_node in op_node.inputs: all_used_vars.add(input_node) for output_node in op_node.outputs: all_used_vars.add(output_node) - all_unused_vars = graph.all_vars() - all_used_vars + all_used_vars = {n.node for n in all_used_vars} + all_unused_vars = { + n + for n in filter(lambda node: node.node not in all_used_vars, + graph.all_var_nodes()) + } graph.safe_remove_nodes(all_unused_vars) def _original_var_name(self, var_name): @@ -583,8 +591,8 @@ class ConvertToInt8Pass(object): Args: graph(IrGraph): the applied graph. """ - persistable_vars = [p.name() for p in graph.all_persistable_vars()] - ops = graph.all_ops() + persistable_vars = [p.name() for p in graph.all_persistable_nodes()] + ops = graph.all_op_nodes() input_map = {} for op_node in ops: op_name = op_node.name() @@ -605,10 +613,10 @@ class ConvertToInt8Pass(object): def _convert_to_int8(self, graph, var_node): int8_var_node_name = var_node.name() + ".int8" - int8_var_node = graph.create_param_node( + int8_var_node = graph.create_persistable_node( name=cpt.to_text(int8_var_node_name), - var_type=var_node.var().type(), - shape=var_node.var().shape(), + var_type=var_node.type(), + shape=var_node.shape(), var_dtype=core.VarDesc.VarType.INT8) array = self._load_var(var_node.name()) self._scope.var(int8_var_node_name) @@ -624,14 +632,19 @@ class ConvertToInt8Pass(object): def _remove_unused_var_nodes(self, graph): all_used_vars = set() - ops = graph.all_ops() + ops = graph.all_op_nodes() for op_node in ops: for input_node in op_node.inputs: all_used_vars.add(input_node) for output_node in op_node.outputs: all_used_vars.add(output_node) - all_unused_vars = graph.all_vars() - all_used_vars + all_used_vars = {n.node for n in all_used_vars} + all_unused_vars = { + n + for n in filter(lambda node: node.node not in all_used_vars, + graph.all_var_nodes()) + } graph.safe_remove_nodes(all_unused_vars) @@ -655,11 +668,11 @@ class TransformForMobilePass(object): Args: graph(IrGraph): the graph will be transformed. """ - ops = graph.all_ops() + ops = graph.all_op_nodes() for op_node in ops: name = op_node.name() if name in self._fake_quant_op_names: - op_node.op().set_type('quantize') + op_node.set_type('quantize') quant_node = graph.create_op_node_from_desc(op_node.op()) for input_node in op_node.inputs: graph.link_to(input_node, quant_node) @@ -667,7 +680,7 @@ class TransformForMobilePass(object): graph.link_to(quant_node, output_node) graph.safe_remove_nodes(op_node) if name in self._fake_dequant_op_names: - op_node.op().set_type('dequantize') + op_node.set_type('dequantize') dequant_node = graph.create_op_node_from_desc(op_node.op()) for input_node in op_node.inputs: graph.link_to(input_node, dequant_node) diff --git a/python/paddle/fluid/contrib/slim/tests/test_graph.py b/python/paddle/fluid/contrib/slim/tests/test_graph.py index 75e0c95b5c3cc06d66eab9de0b85e5d7ed110837..2d2f1384dec65ee19dcade8a46f80bd3f9eb7013 100644 --- a/python/paddle/fluid/contrib/slim/tests/test_graph.py +++ b/python/paddle/fluid/contrib/slim/tests/test_graph.py @@ -61,16 +61,16 @@ class TestGraph(unittest.TestCase): opt.minimize(loss) graph = IrGraph(core.Graph(main.desc), for_test=False) marked_nodes = set() - for op in graph.all_ops(): + for op in graph.all_op_nodes(): if op.name().find('conv2d') > -1: marked_nodes.add(op) graph.draw('.', 'residual', marked_nodes) self.assertFalse(graph.has_circle()) self.assertEqual(graph.graph_num(), 1) nodes = graph.topology_sort() - self.assertEqual(len(nodes), len(graph.all_ops())) + self.assertEqual(len(nodes), len(graph.all_op_nodes())) nodes_map = graph.build_adjacency_list() - self.assertEqual(len(nodes_map), len(graph.all_ops())) + self.assertEqual(len(nodes_map), len(graph.all_op_nodes())) nodes_num = len(graph.all_nodes()) graph.safe_remove_nodes(marked_nodes) self.assertEqual(len(graph.all_nodes()), nodes_num - len(marked_nodes)) diff --git a/python/paddle/fluid/contrib/slim/tests/test_quantization_pass.py b/python/paddle/fluid/contrib/slim/tests/test_quantization_pass.py index 2f291132f3049af21420f863972792c1a862b9ad..254b73a124734f3693f4757801f0f544d6aa6f27 100644 --- a/python/paddle/fluid/contrib/slim/tests/test_quantization_pass.py +++ b/python/paddle/fluid/contrib/slim/tests/test_quantization_pass.py @@ -130,15 +130,16 @@ class TestQuantizationTransformPass(unittest.TestCase): loss = linear_fc(3) opt = fluid.optimizer.Adam(learning_rate=0.001) opt.minimize(loss) - exe = fluid.Executor(fluid.CPUPlace()) + place = fluid.CPUPlace() + exe = fluid.Executor(place) graph = IrGraph(core.Graph(main.desc), for_test=False) transform_pass = QuantizationTransformPass( scope=fluid.global_scope(), - program_exe=exe, + place=place, activation_quantize_type=quant_type) transform_pass.apply(graph) marked_nodes = set() - for op in graph.all_ops(): + for op in graph.all_op_nodes(): if op.name().find('quantize') > -1: marked_nodes.add(op) graph.draw('.', 'quantize_fc_' + quant_type, marked_nodes) @@ -146,7 +147,7 @@ class TestQuantizationTransformPass(unittest.TestCase): self.check_program(transform_pass, program) val_graph = IrGraph(core.Graph(program.desc), for_test=False) val_marked_nodes = set() - for op in val_graph.all_ops(): + for op in val_graph.all_op_nodes(): if op.name().find('quantize') > -1: val_marked_nodes.add(op) val_graph.draw('.', 'val_fc_' + quant_type, val_marked_nodes) @@ -166,15 +167,16 @@ class TestQuantizationTransformPass(unittest.TestCase): loss = residual_block(2) opt = fluid.optimizer.Adam(learning_rate=0.001) opt.minimize(loss) - exe = fluid.Executor(fluid.CPUPlace()) + place = fluid.CPUPlace() + exe = fluid.Executor(place) graph = IrGraph(core.Graph(main.desc), for_test=False) transform_pass = QuantizationTransformPass( scope=fluid.global_scope(), - program_exe=exe, + place=place, activation_quantize_type=quant_type) transform_pass.apply(graph) marked_nodes = set() - for op in graph.all_ops(): + for op in graph.all_op_nodes(): if op.name().find('quantize') > -1: marked_nodes.add(op) graph.draw('.', 'quantize_residual_' + quant_type, marked_nodes) @@ -182,7 +184,7 @@ class TestQuantizationTransformPass(unittest.TestCase): self.check_program(transform_pass, program) val_graph = IrGraph(core.Graph(program.desc), for_test=False) val_marked_nodes = set() - for op in val_graph.all_ops(): + for op in val_graph.all_op_nodes(): if op.name().find('quantize') > -1: val_marked_nodes.add(op) val_graph.draw('.', 'val_residual_' + quant_type, val_marked_nodes) @@ -231,17 +233,17 @@ class TestQuantizationFreezePass(unittest.TestCase): with fluid.scope_guard(scope): exe.run(startup) transform_pass = QuantizationTransformPass( - scope=scope, program_exe=exe, activation_quantize_type=quant_type) + scope=scope, place=place, activation_quantize_type=quant_type) transform_pass.apply(main_graph) transform_pass.apply(test_graph) dev_name = '_gpu_' if use_cuda else '_cpu_' marked_nodes = set() - for op in main_graph.all_ops(): + for op in main_graph.all_op_nodes(): if op.name().find('quantize') > -1: marked_nodes.add(op) main_graph.draw('.', 'main' + dev_name + quant_type, marked_nodes) marked_nodes = set() - for op in test_graph.all_ops(): + for op in test_graph.all_op_nodes(): if op.name().find('quantize') > -1: marked_nodes.add(op) test_graph.draw('.', 'test' + dev_name + quant_type, marked_nodes) @@ -251,11 +253,6 @@ class TestQuantizationFreezePass(unittest.TestCase): iters = 5 batch_size = 8 - #train_exe = fluid.ParallelExecutor( - # main_program=quantized_main_program, - # use_cuda=bool(use_cuda), - # loss_name=loss.name, - # scope=scope) train_reader = paddle.batch( paddle.reader.shuffle( paddle.dataset.mnist.train(), buf_size=500), @@ -269,9 +266,7 @@ class TestQuantizationFreezePass(unittest.TestCase): loss_v = exe.run(program=quantized_main_program, feed=feeder.feed(data), fetch_list=[loss]) - #loss_v = train_exe.run(feed=feeder.feed(data), - # fetch_list=[loss.name]) - #print('{}: {}'.format('loss' + dev_name + quant_type, loss_v)) + print('{}: {}'.format('loss' + dev_name + quant_type, loss_v)) test_data = next(test_reader()) with fluid.program_guard(quantized_test_program): @@ -287,7 +282,7 @@ class TestQuantizationFreezePass(unittest.TestCase): freeze_pass = QuantizationFreezePass(scope=scope, place=place) freeze_pass.apply(test_graph) marked_nodes = set() - for op in test_graph.all_ops(): + for op in test_graph.all_op_nodes(): if op.name().find('quantize') > -1: marked_nodes.add(op) test_graph.draw('.', 'test_freeze' + dev_name + quant_type, @@ -299,21 +294,21 @@ class TestQuantizationFreezePass(unittest.TestCase): feed=feeder.feed(test_data), fetch_list=[loss]) self.assertAlmostEqual(test_loss1, test_loss2, delta=5e-3) - #print('{}: {}'.format('test_loss1' + dev_name + quant_type, test_loss1)) - #print('{}: {}'.format('test_loss2' + dev_name + quant_type, test_loss2)) + print('{}: {}'.format('test_loss1' + dev_name + quant_type, test_loss1)) + print('{}: {}'.format('test_loss2' + dev_name + quant_type, test_loss2)) w_freeze = np.array(scope.find_var('conv2d_1.w_0').get_tensor()) # Maybe failed, this is due to the calculation precision # self.assertAlmostEqual(np.sum(w_freeze), np.sum(w_quant)) - #print('{}: {}'.format('w_freeze' + dev_name + quant_type, - # np.sum(w_freeze))) - #print('{}: {}'.format('w_quant' + dev_name + quant_type, - # np.sum(w_quant))) + print('{}: {}'.format('w_freeze' + dev_name + quant_type, + np.sum(w_freeze))) + print('{}: {}'.format('w_quant' + dev_name + quant_type, + np.sum(w_quant))) # Convert parameter to 8-bit. convert_int8_pass = ConvertToInt8Pass(scope=scope, place=place) convert_int8_pass.apply(test_graph) marked_nodes = set() - for op in test_graph.all_ops(): + for op in test_graph.all_op_nodes(): if op.name().find('quantize') > -1: marked_nodes.add(op) test_graph.draw('.', 'test_int8' + dev_name + quant_type, marked_nodes) @@ -330,14 +325,14 @@ class TestQuantizationFreezePass(unittest.TestCase): w_8bit = np.array(scope.find_var('conv2d_1.w_0.int8').get_tensor()) self.assertEqual(w_8bit.dtype, np.int8) self.assertEqual(np.sum(w_8bit), np.sum(w_freeze)) - #print('{}: {}'.format('w_8bit' + dev_name + quant_type, np.sum(w_8bit))) - #print('{}: {}'.format('w_freeze' + dev_name + quant_type, - # np.sum(w_freeze))) + print('{}: {}'.format('w_8bit' + dev_name + quant_type, np.sum(w_8bit))) + print('{}: {}'.format('w_freeze' + dev_name + quant_type, + np.sum(w_freeze))) mobile_pass = TransformForMobilePass() mobile_pass.apply(test_graph) marked_nodes = set() - for op in test_graph.all_ops(): + for op in test_graph.all_op_nodes(): if op.name().find('quantize') > -1: marked_nodes.add(op) test_graph.draw('.', 'test_mobile' + dev_name + quant_type, diff --git a/python/paddle/fluid/executor.py b/python/paddle/fluid/executor.py index c0191a34deaa5ab3b53b5fe4f33cb5449a2db8b3..dfa50e721c979703165649dccfd6e42ef08e97b7 100644 --- a/python/paddle/fluid/executor.py +++ b/python/paddle/fluid/executor.py @@ -261,45 +261,42 @@ def _as_lodtensor(data, place): class Executor(object): """ - An Executor in Python, only support the single-GPU running. For multi-cards, please refer to - ParallelExecutor. - Python executor takes a program, add feed operators and fetch operators to this program according + An Executor in Python, supports single/multiple-GPU running, and single/multiple-CPU running. + Python executor takes a program, adds feed operators and fetch operators to this program according to feed map and fetch_list. Feed map provides input data for the program. fetch_list provides - the variables(or names) that user want to get after program run. Note: the executor will run all + the variables(or names) that user wants to get after program runs. Note: the executor will run all operators in the program but not only the operators dependent by the fetch_list. - It store the global variables into the global scope, and create a local scope for the temporary - variables. The local scope contents will be discarded after every minibatch forward/backward finished. - But the global scope variables will be persistent through different runs. - All of ops in program will be running in sequence. + It stores the global variables into the global scope, and creates a local scope for the temporary + variables. The contents in local scope may be discarded after every minibatch forward/backward + finished. But the global scope variables will be persistent through different runs. Example: - .. code-block:: python - # First create the Executor. - place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() - exe = fluid.Executor(place) - - # Run the startup program once and only once. - # Not need to optimize/compile the startup program. - exe.run(fluid.default_startup_program()) - - # Run the main program directly without compile. - loss, = exe.run(fluid.default_main_program(), - feed=feed_dict, - fetch_list=[loss.name]) - # Or, compiled the program and run. See `CompiledProgram` for more detail. - compiled_prog = compiler.CompiledProgram( - fluid.default_main_program()).with_data_parallel( - loss_name=loss.name) - loss, = exe.run(compiled_prog, - feed=feed_dict, - fetch_list=[loss.name]) + + .. code-block:: python + + # First create the Executor. + place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() + exe = fluid.Executor(place) + + # Run the startup program once and only once. + # Not need to optimize/compile the startup program. + exe.run(fluid.default_startup_program()) + + # Run the main program directly without compile. + loss, = exe.run(fluid.default_main_program(), + feed=feed_dict, + fetch_list=[loss.name]) + # Or, compiled the program and run. See `CompiledProgram` for more detail. + compiled_prog = compiler.CompiledProgram( + fluid.default_main_program()).with_data_parallel( + loss_name=loss.name) + loss, = exe.run(compiled_prog, + feed=feed_dict, + fetch_list=[loss.name]) Args: place(core.CPUPlace|core.CUDAPlace(n)): indicate the executor run on which device - - Note: For debugging complicated network in parallel-GPUs, you can test it on the executor. - They has the exactly same arguments, and expected the same results. """ def __init__(self, place): @@ -382,6 +379,12 @@ class Executor(object): ] return outs + ''' + TODO(typhoonzero): Define "no longer use" meaning? Can user create + a new Executor for the same program and run? + TODO(panyx0718): Why ParallelExecutor doesn't have close? + ''' + def close(self): """ Close this executor. @@ -389,9 +392,6 @@ class Executor(object): You can no longer use this executor after calling this method. For the distributed training, this method would free the resource on PServers related to the current Trainer. - TODO(typhoonzero): Define "no longer use" meaning? Can user create - a new Executor for the same program and run? - TODO(panyx0718): Why ParallelExecutor doesn't have close? Example: >>> cpu = core.CPUPlace() diff --git a/python/paddle/fluid/framework.py b/python/paddle/fluid/framework.py index 7fe3120acc756d9335e5adbc2e8cfb4270e41c02..7dc9178807c76b44c9aeb00054188ad1dbe18f0a 100644 --- a/python/paddle/fluid/framework.py +++ b/python/paddle/fluid/framework.py @@ -87,15 +87,6 @@ def _current_expected_place(): return _imperative_current_expected_place_ -def is_pserver_mode(main_program): - main = main_program if main_program \ - else default_main_program() - for op in main.global_block().ops: - if op.type in ["send", "recv"]: - return True - return False - - class NameScope(object): def __init__(self, name="", parent=None): self._children = dict() @@ -393,6 +384,9 @@ class Variable(object): if not self._ivar: self._ivar = core.VarBase(stop_gradient) self._ivar.desc = self.desc + self._ivar.block = block.desc + self._ivar.name = name + self._ivar.persistable = persistable if persistable: self.block.vars[name] = self else: @@ -721,7 +715,9 @@ class Operator(object): out_arg_names = [] for arg in out_args: out_arg_names.append(cpt.to_text(arg.name)) - arg.op = self + # TODO(minqiyang): could we remove variable's op in static mode? + if not _in_imperative_mode(): + arg.op = self self.desc.set_output(out_proto.name, out_arg_names) if op_attrs is not None: @@ -1200,15 +1196,6 @@ class Block(object): else: raise ValueError("Var {0} is not found recursively".format(name)) - def _clear_block(self): - # TODO(minqiyang): move this to backward_hooks - self.desc._clear_block() - - for name in self.vars.keys(): - assert self.vars[name].persistable - - del self.ops[:] - def all_parameters(self): return list(self.iter_parameters()) @@ -1345,26 +1332,13 @@ class Block(object): # # TODO(minqiyang): add op stop_gradient support in static mode too. # currently, we only support stop_gradient in imperative mode. - self._trace_op(op, kwargs.get("stop_gradient", False)) - self.ops.append(op) + _imperative_tracer().trace_op(op, + kwargs.get("stop_gradient", False)) + else: + self.ops.append(op) return op - def _trace_op(self, op, stop_gradient=False): - backward_refs = _imperative_tracer().trace( - op.iop, op.inputs, op.outputs, self.desc, - _imperative_current_expected_place_, stop_gradient) - - # TODO(minqiyang): support backward_hooks to eager remove backward_refs - op.backward_refs = defaultdict(list) - for k, v in six.iteritems(op.inputs): - if k in backward_refs: - op.backward_refs[k] = op.inputs[k] - - for k, v in six.iteritems(op.outputs): - if k in backward_refs: - op.backward_refs[k] = op.outputs[k] - def _insert_op(self, index, *args, **kwargs): """ Insert a Operator according to the giving arguments. @@ -1417,9 +1391,11 @@ class Block(object): inputs=kwargs.get("inputs", None), outputs=kwargs.get("outputs", None), attrs=kwargs.get("attrs", None)) - self.ops.insert(0, op) if _in_imperative_mode(): - self._trace_op(op, kwargs.get("stop_gradient", False)) + _imperative_tracer().trace_op(op, + kwargs.get("stop_gradient", False)) + else: + self.ops.insert(0, op) return op def _sync_with_cpp(self): @@ -1566,10 +1542,397 @@ class Block(object): return ret_var +class IrNode(object): + """ + Python IrNode. Beneath it is a core.Node, which is used for Ir Pass. + """ + + def __init__(self, node): + """ + Construct an IrNode using core.Node. + + Args: + node(core.Node): C++ Node. + """ + assert isinstance(node, + core.Node), 'node must be the instance of core.Node.' + self.node = node + + def name(self): + """ + Return the node name. + + Returns: + str: node name. + """ + return self.node.name() + + def node_type(self): + """ + Return the node type. + + Returns: + core.Node.Type: node type(core.Node.Type.Operation or core.Node.Type.Variable). + """ + return self.node.node_type() + + def var(self): + """ + Return the node variable description. + + Returns: + core.VarDesc: node variable description. + """ + return self.node.var() + + def op(self): + """ + Return the node operator description. + + Returns: + core.OpDesc: node operator description. + """ + return self.node.op() + + def id(self): + """ + Return the node id. + + Returns: + int: node id. + """ + return self.node.id() + + def is_op(self): + """ + If the node is an operator, then return true. + + Returns: + bool: indicate whether the node is an operator. + """ + return self.node.is_op() + + def is_var(self): + """ + If the node is a variable, then return true. + + Returns: + bool: indicate whether the node is a variable. + """ + return self.node.is_var() + + def is_ctrl_var(self): + """ + If the node is a control dependence variable, then return true. + + Returns: + bool: indicate whether the node is a control dependence variable. + """ + return self.node.is_ctrl_var() + + def clear_inputs(self): + """ + Clear the node inputs. After executing the `clear_inputs` function, + the node inputs will be empty. + """ + self.node.clear_inputs() + + def remove_input_by_id(self, node_id): + """ + Remove a node from inputs by the given node id. + + Args: + node_id(int): the given node id. + """ + self.node.remove_input(node_id) + + def remove_input(self, node): + """ + Remove a node from inputs. + + Args: + node(IrNode): the node being removed. + """ + self.node.remove_input(node.node) + + def append_input(self, node): + """ + Append a node in inputs. + + Args: + node(IrNode): the node being appended. + """ + self.node.append_input(node.node) + + def clear_outputs(self): + """ + Clear the node outputs. After executing the `clear_outputs` function, + the node outputs will be empty. + """ + self.node.clear_outputs() + + def remove_output_by_id(self, node_id): + """ + Remove a node from outputs by the given node id. + + Args: + node_id(int): the given node id. + """ + self.node.remove_output(node_id) + + def remove_output(self, node): + """ + Remove a node from outputs. + + Args: + node(IrNode): the node being removed. + """ + self.node.remove_output(node.node) + + def append_output(self, node): + """ + Append a node in outputs. + + Args: + node(IrNode): the node being appended. + """ + self.node.append_output(node.node) + + @property + def inputs(self): + """ + Return the node inputs. + + Returns: + list(IrNode): node inputs wrapped by IrNode. + """ + return [IrNode(n) for n in self.node.inputs] + + @property + def outputs(self): + """ + Return the node outputs. + + Returns: + list(IrNode): node outputs wrapped by IrNode. + """ + return [IrNode(n) for n in self.node.outputs] + + +class IrVarNode(IrNode): + """ + Python IrVarNode. Beneath it is a core.Node, it inherits from IrNode. + """ + + def __init__(self, node): + """ + Construct an IrVarNode using core.Node. + + Args: + node(core.Node): C++ Node. + """ + assert isinstance(node, core.Node) and node.is_var(), \ + 'node must be the instance of core.Node and it must be a variable node.' + super(IrVarNode, self).__init__(node) + self.node = node + + def set_shape(self, shape): + """ + Set the node variable shape. + + Args: + shape(list): shape to be set. + """ + assert self.node.var() is not None, \ + "The node variable description cannot be None." + self.node.var().set_shape(shape) + + def persistable(self): + """ + If the variable node is a persistable variable, then return true. + + Returns: + bool: indicate whether the variable is persistable. + """ + assert self.node.var() is not None, \ + "The node variable description cannot be None." + return self.node.var().persistable() + + def type(self): + """ + Return the variable type. + + Returns: + core.VarDesc.VarType: the variable type. + """ + assert self.node.var() is not None, \ + "The node variable description cannot be None." + return self.node.var().type() + + def dtype(self): + """ + Return the variable data type. + + Returns: + core.VarDesc.VarType: the variable data type. + """ + assert self.node.var() is not None, \ + "The node variable description cannot be None." + return self.node.var().dtype() + + def shape(self): + """ + Return the variable shape. + + Returns: + list: the variable shape. + """ + assert self.node.var() is not None, \ + "The node variable description cannot be None." + return self.node.var().shape() + + @property + def inputs(self): + """ + Return the node inputs. + + Returns: + list(IrOpNode): node inputs wrapped by IrOpNode. + """ + return [IrOpNode(n) for n in self.node.inputs] + + @property + def outputs(self): + """ + Return the node outputs. + + Returns: + list(IrOpNode): node outputs wrapped by IrOpNode. + """ + return [IrOpNode(n) for n in self.node.outputs] + + +class IrOpNode(IrNode): + """ + Python IrOpNode. Beneath it is a core.Node, it inherits from IrNode. + """ + + def __init__(self, node): + """ + Construct an IrOpNode using core.Node. + + Args: + node(core.Node): C++ Node. + """ + assert isinstance(node, core.Node) and node.is_op(), \ + 'node must be the instance of core.Node and it must be a operator node.' + super(IrOpNode, self).__init__(node) + self.node = node + + def rename_input(self, old_input_name, new_input_name): + """ + Rename the input of this node. + + Args: + old_input_name(str): the old input name. + new_input_name(str): the new input name. + """ + assert self.node.op() is not None, \ + "The node operator description cannot be None." + self.node.op()._rename_input(old_input_name, new_input_name) + + def input(self, name): + """ + Get the argument name list by the parameter name for input. + + Args: + name(str): the parameter name. + + Returns: + list(str): the argument name list. + """ + assert self.node.op() is not None, \ + "The node operator description cannot be None." + return self.node.op().input(name) + + def output(self, name): + """ + Get the argument name list by the parameter name for output. + + Args: + name(str): the parameter name. + + Returns: + list(str): the argument name list. + """ + assert self.node.op() is not None, \ + "The node operator description cannot be None." + return self.node.op().output(name) + + def set_type(self, new_type): + """ + Change the operator type into new type. + + Args: + new_type(str): new operator type to be set. + """ + assert self.node.op() is not None, \ + "The node operator description cannot be None." + return self.node.op().set_type(new_type) + + def set_attr(self, name, val): + """ + Set the value of attribute by attribute's name. + + Args: + name(str): the attribute name. + val(bool|int|str|float|list): the value of the attribute. + """ + self._update_desc_attr(name, val) + + def _update_desc_attr(self, name, val): + """ + Update the value of the op desc's attribute by attribute's name. + """ + assert self.node.op() is not None, \ + "The node operator description cannot be None." + desc = self.node.op() + if isinstance(val, Block): + desc.set_block_attr(name, val.desc) + elif isinstance(val, list) and val and \ + all(isinstance(v, Block) for v in val): + desc.set_blocks_attr(name, [v.desc for v in val]) + elif isinstance(val, core.BlockDesc) or \ + isinstance(val, core.ProgramDesc): + desc.set_serialized_attr(name, val.serialize_to_string()) + else: + desc._set_attr(name, val) + + @property + def inputs(self): + """ + Return the node inputs. + + Returns: + list(IrVarNode): node inputs wrapped by IrVarNode. + """ + return [IrVarNode(n) for n in self.node.inputs] + + @property + def outputs(self): + """ + Return the node outputs. + + Returns: + list(IrVarNode): node outputs wrapped by IrVarNode. + """ + return [IrVarNode(n) for n in self.node.outputs] + + class IrGraph(object): """ Python IrGraph. Beneath it is a core.Graph, which is used for - create a c++ Ir Pass Graph. An IrGraph is just a graph view of + creating a c++ Ir Pass Graph. An IrGraph is just a graph view of a Program. In an IrGraph, both Variables and Operators are graph nodes. """ @@ -1597,15 +1960,15 @@ class IrGraph(object): """ Return all nodes included in the graph as a set. """ - return {node for node in self.graph.nodes()} + return {IrNode(node) for node in self.graph.nodes()} - def all_vars(self): + def all_var_nodes(self): """ Return all variable nodes included in the graph as a set. """ - return {node for node in self.graph.nodes() if node.is_var()} + return {IrVarNode(node) for node in self.graph.nodes() if node.is_var()} - def all_persistable_vars(self): + def all_persistable_nodes(self): """ Return all persistable variable nodes included in the graph as a set. """ @@ -1614,13 +1977,13 @@ class IrGraph(object): if node.is_var() and node.var() is not None and node.var( ).persistable(): persistable_nodes.add(node) - return persistable_nodes + return {IrVarNode(p) for p in persistable_nodes} - def all_ops(self): + def all_op_nodes(self): """ Return all operator nodes included in the graph as a set. """ - return {node for node in self.graph.nodes() if node.is_op()} + return {IrOpNode(node) for node in self.graph.nodes() if node.is_op()} def var_node(self, name): """ @@ -1634,14 +1997,14 @@ class IrGraph(object): doesn't have a variable with the giving name. Returns: - core.Node: the variable node with the giving name. + IrVarNode: the variable node with the giving name. """ if not isinstance(name, six.string_types): raise TypeError( "var require string as parameter, but get %s instead." % (type(name))) target_var_node = None - var_nodes = self.all_vars() + var_nodes = self.all_var_nodes() for var_node in var_nodes: if var_node.name() == name: target_var_node = var_node @@ -1649,7 +2012,7 @@ class IrGraph(object): raise ValueError("var_node %s not in this graph" % name) return target_var_node - def create_param_node(self, name, var_type, shape, var_dtype): + def create_persistable_node(self, name, var_type, shape, var_dtype): """ Create a persistable variable node in the graph. In IrGraph, it can not distinguish between persistable variables and parameters. @@ -1661,14 +2024,14 @@ class IrGraph(object): var_dtype(core.VarDesc.VarType): the data type of the persistable variable node. Returns: - core.Node: the created persistable variable node. + IrVarNode: the created persistable variable node. """ var_desc = core.VarDesc(name) var_desc.set_type(var_type) var_desc.set_shape(shape) var_desc.set_dtype(var_dtype) var_desc.set_persistable(True) - return self.graph.create_var_node(var_desc) + return IrVarNode(self.graph.create_var_node(var_desc)) def create_var_node(self, name, var_type, shape, var_dtype): """ @@ -1682,14 +2045,14 @@ class IrGraph(object): var_dtype(core.VarDesc.VarType): the data type of the variable node. Returns: - core.Node: the created variable node. + IrVarNode: the created variable node. """ var_desc = core.VarDesc(name) var_desc.set_type(var_type) var_desc.set_shape(shape) var_desc.set_dtype(var_dtype) - return self.graph.create_var_node(var_desc) + return IrVarNode(self.graph.create_var_node(var_desc)) def create_var_node_from_desc(self, var_desc): """ @@ -1700,9 +2063,9 @@ class IrGraph(object): var_desc(core.VarDesc): the giving variable description. Returns: - core.Node: the created variable node. + IrVarNode: the created variable node. """ - return self.graph.create_var_node(var_desc) + return IrVarNode(self.graph.create_var_node(var_desc)) def create_op_node(self, op_type, attrs, inputs, outputs): """ @@ -1715,7 +2078,7 @@ class IrGraph(object): outputs(dict): the outpus of the operator node. Returns: - core.Node: the created operator node. + IrOpNode: the created operator node. """ op_desc = core.OpDesc() op_desc.set_type(op_type) @@ -1731,7 +2094,7 @@ class IrGraph(object): var_nodes = [var_nodes] op_desc.set_output(output_name, [var_node.name() for var_node in var_nodes]) - return self.graph.create_op_node(op_desc) + return IrOpNode(self.graph.create_op_node(op_desc)) def create_op_node_from_desc(self, op_desc): """ @@ -1741,40 +2104,40 @@ class IrGraph(object): op_desc(core.VarDesc): the giving operator description. Returns: - core.Node: the created operator node. + IrOpNode: the created operator node. """ - return self.graph.create_op_node(op_desc) + return IrOpNode(self.graph.create_op_node(op_desc)) def update_input_link(self, old_input_node, new_input_node, op_node): """ Update the input's link of a operator node. Args: - old_input_node(core.Node): the old input node of the giving op_node. - new_input_node(core.Node): the new input node of the giving op_node. - op_node(core.Node): the operator node that is needed to update input's link. + old_input_node(IrNode): the old input node of the giving op_node. + new_input_node(IrNode): the new input node of the giving op_node. + op_node(IrOpNode): the operator node that is needed to update input's link. """ - assert old_input_node in self.graph.nodes() and new_input_node in \ - self.graph.nodes() and op_node in self.graph.nodes(), \ + assert old_input_node.node in self.graph.nodes() and new_input_node.node in \ + self.graph.nodes() and op_node.node in self.graph.nodes(), \ 'The three arguments(old_input_node&new_input_node&op_node) must be in the graph nodes.' - old_input_node.outputs_remove(op_node) - op_node.inputs_remove(old_input_node) - new_input_node.outputs_append(op_node) - op_node.inputs_append(new_input_node) - op_node.op()._rename_input(old_input_node.name(), new_input_node.name()) + old_input_node.remove_output(op_node) + op_node.remove_input(old_input_node) + new_input_node.append_output(op_node) + op_node.append_input(new_input_node) + op_node.rename_input(old_input_node.name(), new_input_node.name()) def link_to(self, node_in, node_out): """ Connect two nodes. Args: - node_in(core.Node): the input node. - node_out(core.Node): the output node. + node_in(IrNode): the input node. + node_out(IrNode): the output node. """ - assert node_in in self.graph.nodes() and node_out in self.graph.nodes(), \ + assert node_in.node in self.graph.nodes() and node_out.node in self.graph.nodes(), \ 'The two arguments(node_in&node_out) must be in the graph nodes.' - node_in.outputs_append(node_out) - node_out.inputs_append(node_in) + node_in.append_output(node_out) + node_out.append_input(node_in) def safe_remove_nodes(self, remove_nodes): """ @@ -1789,7 +2152,8 @@ class IrGraph(object): remove_nodes = set(remove_nodes) else: remove_nodes = {remove_nodes} - core.graph_safe_remove_nodes(self.graph, remove_nodes) + original_nodes = {n.node for n in remove_nodes} + core.graph_safe_remove_nodes(self.graph, original_nodes) def has_circle(self): """ @@ -1816,18 +2180,23 @@ class IrGraph(object): Notes: the `graph` cannot contain a circle. Returns: - set(core.Node): nodes in topology order. + set(IrNode): nodes in topology order. """ - return core.topology_sort(self.graph) + ordered_nodes = core.topology_sort(self.graph) + return {IrNode(n) for n in ordered_nodes} def build_adjacency_list(self): """ Build an adjacency list of operations for the `graph`. Returns: - dict{core.Node: set(core.Node)}: the adjacency list. + dict{IrNode: set(IrNode)}: the adjacency list. """ - return core.build_adjacency_list(self.graph) + adj_list = core.build_adjacency_list(self.graph) + wrapped_adj_list = dict() + for k, v in six.iteritems(adj_list): + wrapped_adj_list[IrNode(k)] = {IrNode(n) for n in v} + return wrapped_adj_list def draw(self, save_path, name, marked_nodes=None, remove_ctr_var=True): """ @@ -1837,7 +2206,7 @@ class IrGraph(object): Args: save_path(str): the save path of drawn graph. name(str): the name of drawn graph. - marked_nodes(set(core.Node)): nodes that are needed to be marked. + marked_nodes(set(IrNode)): nodes that are needed to be marked. Default value is None. remove_ctr_var(bool): If it is set True, all control variable nodes in the graph will be removed. Default value is True. @@ -1852,20 +2221,22 @@ class IrGraph(object): print('The {} is saved as the dot filetype.'.format( dot_file_path)) + remove_ctr_vars = set() if remove_ctr_var: - remove_ctr_vars = set() - for node in self.graph.nodes(): + for node in self.all_var_nodes(): if node.is_ctrl_var(): remove_ctr_vars.add(node) self.safe_remove_nodes(remove_ctr_vars) - ops_num = 0 - for node in self.graph.nodes(): - if node.is_op(): - ops_num += 1 - print('Total ops num = {}.'.format(ops_num)) + print('Total ops num = {}.'.format(len(self.all_op_nodes()))) + if marked_nodes is not None: if not isinstance(marked_nodes, set): - marked_nodes = set(marked_nodes) + if isinstance(marked_nodes, Iterable): + marked_nodes = set(marked_nodes) + else: + marked_nodes = {marked_nodes} + marked_nodes = {n.node for n in marked_nodes} + remove_ctr_vars = {n.node for n in remove_ctr_vars} marked_nodes = marked_nodes - remove_ctr_vars if self.graph.has('__graphviz__marked_node__'): self.graph.erase('__graphviz__marked_node__') diff --git a/python/paddle/fluid/imperative/__init__.py b/python/paddle/fluid/imperative/__init__.py index 54dc794ea6392fac6f266477fe045b37001a8666..034a11e0a6049c17800c8fd5aab5bc2291320169 100644 --- a/python/paddle/fluid/imperative/__init__.py +++ b/python/paddle/fluid/imperative/__init__.py @@ -23,7 +23,11 @@ from .layers import * from . import nn from .nn import * +from . import tracer +from .tracer import * + __all__ = [] __all__ += layers.__all__ __all__ += base.__all__ __all__ += nn.__all__ +__all__ += tracer.__all__ diff --git a/python/paddle/fluid/imperative/base.py b/python/paddle/fluid/imperative/base.py index d4525233cc681720404770ef1d0c5d3006607a2e..174f138bfa2d3cfaa433c3235c2b0f9a5650e756 100644 --- a/python/paddle/fluid/imperative/base.py +++ b/python/paddle/fluid/imperative/base.py @@ -16,6 +16,7 @@ import numpy as np from paddle.fluid import core from paddle.fluid import framework +from .tracer import Tracer __all__ = ['enabled', 'guard', 'to_variable'] @@ -28,7 +29,7 @@ def enabled(): def guard(place=None): train = framework.Program() startup = framework.Program() - tracer = core.Tracer(train.current_block().desc) + tracer = Tracer(train.current_block().desc) if place is None: if core.is_compiled_with_cuda(): diff --git a/python/paddle/fluid/imperative/tracer.py b/python/paddle/fluid/imperative/tracer.py new file mode 100644 index 0000000000000000000000000000000000000000..1064ad63e7103acde9bb8106b7791441ce68849b --- /dev/null +++ b/python/paddle/fluid/imperative/tracer.py @@ -0,0 +1,67 @@ +# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import print_function + +import six + +from collections import defaultdict +from paddle.fluid import core +from paddle.fluid import framework + +__all__ = ['Tracer'] + + +def release_op(op): + del framework._imperative_tracer()._ops[op._trace_id] + + +class Tracer(core.Tracer): + """ + Python wrapper of imperative tracer + """ + + def __init__(self, block): + super(Tracer, self).__init__(block) + + self._ops = defaultdict() + self._trace_id = 0 + + def trace_op(self, op, stop_gradient=False): + # record op's trace id + op.iop._trace_id = self._trace_id + + # trace op and save it + backward_refs = self.trace(op.iop, op.inputs, op.outputs, op.block.desc, + framework._current_expected_place(), + stop_gradient) + + if not stop_gradient: + self._trace_id += 1 + self._ops[op.iop._trace_id] = op + + # register backward hooks and variables if needed + if len(backward_refs) > 0: + op.iop.register_backward_hooks(release_op) + + # TODO(minqiyang): remove all inputs and outputs after seperate + # var and grad + op.backward_refs = defaultdict(list) + for k, v in six.iteritems(op.inputs): + if k in backward_refs: + op.backward_refs[k] = op.inputs[k] + + for k, v in six.iteritems(op.outputs): + if k in backward_refs: + op.backward_refs[k] = op.outputs[k] diff --git a/python/paddle/fluid/initializer.py b/python/paddle/fluid/initializer.py index e8341be28683a25971a53a37c70533a16add1593..190e7b5608a0cdf156b449e919e108a0917a0980 100644 --- a/python/paddle/fluid/initializer.py +++ b/python/paddle/fluid/initializer.py @@ -19,6 +19,7 @@ import numpy as np from .wrapped_decorator import signature_safe_contextmanager from .core import VarDesc from . import unique_name +from .imperative import base as imperative_base __all__ = [ 'Constant', 'Uniform', 'Normal', 'TruncatedNormal', 'Xavier', 'Bilinear', @@ -165,7 +166,8 @@ class ConstantInitializer(Initializer): 'force_cpu': self._force_cpu or force_init_on_cpu() }, stop_gradient=True) - var.op = op + if not imperative_base.enabled(): + var.op = op return op @@ -244,7 +246,8 @@ class UniformInitializer(Initializer): attrs={"in_dtype": out_var.dtype, "out_dtype": var.dtype}) - var.op = op + if not imperative_base.enabled(): + var.op = op return op @@ -322,7 +325,8 @@ class NormalInitializer(Initializer): outputs={"Out": var}, attrs={"in_dtype": out_var.dtype, "out_dtype": var.dtype}) - var.op = op + if not imperative_base.enabled(): + var.op = op return op @@ -400,7 +404,8 @@ class TruncatedNormalInitializer(Initializer): outputs={"Out": var}, attrs={"in_dtype": out_var.dtype, "out_dtype": var.dtype}) - var.op = op + if not imperative_base.enabled(): + var.op = op return op @@ -505,7 +510,8 @@ class XavierInitializer(Initializer): "seed": self._seed }, stop_gradient=True) - var.op = op + if not imperative_base.enabled(): + var.op = op return op @@ -605,7 +611,8 @@ class MSRAInitializer(Initializer): "seed": self._seed }, stop_gradient=True) - var.op = op + if not imperative_base.enabled(): + var.op = op return op @@ -703,7 +710,8 @@ class BilinearInitializer(Initializer): 'shape': list(shape), value_name: values }) - var.op = op + if not imperative_base.enabled(): + var.op = op return op @@ -761,7 +769,8 @@ class NumpyArrayInitializer(Initializer): value_name: values }, stop_gradient=True) - var.op = op + if not imperative_base.enabled(): + var.op = op return op diff --git a/python/paddle/fluid/io.py b/python/paddle/fluid/io.py index 24e102b6c2612b58a9b8367ebbefcece535d58bb..1775159798414a98bede4a3db5b577fb5e47e611 100644 --- a/python/paddle/fluid/io.py +++ b/python/paddle/fluid/io.py @@ -468,9 +468,10 @@ def save_persistables(executor, dirname, main_program=None, filename=None): exe = fluid.Executor(fluid.CPUPlace()) param_path = "./my_paddle_model" + # `prog` can be a program defined by the user prog = fluid.default_main_program() fluid.io.save_persistables(executor=exe, dirname=param_path, - main_program=None) + main_program=prog) """ if main_program and main_program._is_distributed: diff --git a/python/paddle/fluid/layers/learning_rate_scheduler.py b/python/paddle/fluid/layers/learning_rate_scheduler.py index 617704a53138bd081a2ebe318de0c89e8db4aa96..378aeb37605f1971da3fe4a926e4b36b8eae2ca4 100644 --- a/python/paddle/fluid/layers/learning_rate_scheduler.py +++ b/python/paddle/fluid/layers/learning_rate_scheduler.py @@ -28,10 +28,12 @@ from . import ops from . import tensor from ..initializer import init_on_cpu from ..framework import default_main_program, Parameter, unique_name, name_scope +import math __all__ = [ 'exponential_decay', 'natural_exp_decay', 'inverse_time_decay', - 'polynomial_decay', 'piecewise_decay', 'noam_decay', 'append_LARS' + 'polynomial_decay', 'piecewise_decay', 'noam_decay', 'append_LARS', + 'cosine_decay' ] @@ -307,6 +309,41 @@ def piecewise_decay(boundaries, values): return lr +def cosine_decay(learning_rate, step_each_epoch, epochs): + """ + Applies cosine decay to the learning rate. + + when training a model, it is often recommended to lower the learning rate as the + training progresses. By using this function, the learning rate will be decayed by + following cosine decay strategy. + + decayed_lr = learning_rate * 0.5 * (math.cos(epoch * math.pi / epochs) + 1) + + Args: + learning_rate(Variable|float): The initial learning rate. + step_each_epoch(int): the number of steps in an epoch. + epochs(int): the number of epochs. + + Returns: + Variable: The decayed learning rate. + + Examples: + + ..code-block:: python + + base_lr = 0.1 + lr = fluid.layers.cosine_decay( + learning_rate = base_lr, step_each_epoch=10000, epochs=120) + """ + with default_main_program()._lr_schedule_guard(): + global_step = _decay_step_counter() + + cur_epoch = ops.floor(global_step / step_each_epoch) + decayed_lr = learning_rate * 0.5 * ( + ops.cos(cur_epoch * math.pi / epochs) + 1) + return decayed_lr + + def append_LARS(params_grads, learning_rate, weight_decay): """ Applies LARS (LAYER-WISE ADAPTIVE RATE SCALING) to learning rate for diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py index 250dc24bd8f028b22d04b3f8dde082c7e236e402..efb400ccc6d43df44325dc7ef88c14afe4b704c3 100644 --- a/python/paddle/fluid/layers/nn.py +++ b/python/paddle/fluid/layers/nn.py @@ -1767,7 +1767,7 @@ def sequence_softmax(input, use_cudnn=False, name=None): return softmax_out -def softmax(input, use_cudnn=True, name=None): +def softmax(input, use_cudnn=False, name=None): """ The input of the softmax operator is a tensor of any rank. The output tensor has the same shape as the input. @@ -1795,7 +1795,8 @@ def softmax(input, use_cudnn=True, name=None): Args: input (Variable): The input variable. use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn \ - library is installed. + library is installed. To improve numerical stablity, set use_cudnn to \ + False by default. Default: False name (str|None): A name for this layer(optional). If set None, the layer will be named automatically. Default: None. @@ -3041,7 +3042,6 @@ def data_norm(input, param_attr=None, data_layout='NCHW', in_place=False, - use_mkldnn=False, name=None, moving_mean_name=None, moving_variance_name=None, @@ -3075,7 +3075,6 @@ def data_norm(input, param_attr(ParamAttr): The parameter attribute for Parameter `scale`. data_layout(string, default NCHW): NCHW|NHWC in_place(bool, Default False): Make the input and output of batch norm reuse memory. - use_mkldnn(bool, Default false): ${use_mkldnn_comment} name(string, Default None): A name for this layer(optional). If set None, the layer will be named automatically. moving_mean_name(string, Default None): The name of moving_mean which store the global Mean. @@ -3156,8 +3155,7 @@ def data_norm(input, outputs={"Y": data_norm_out, "Means": means, "Scales": scales}, - attrs={"epsilon": epsilon, - "use_mkldnn": use_mkldnn}) + attrs={"epsilon": epsilon}) return helper.append_activation(data_norm_out) @@ -5756,7 +5754,7 @@ def softmax_with_cross_entropy(logits, label, soft_label=False, ignore_index=kIgnoreIndex, - numeric_stable_mode=False, + numeric_stable_mode=True, return_softmax=False): """ **Softmax With Cross Entropy Operator.** @@ -5820,7 +5818,7 @@ def softmax_with_cross_entropy(logits, When soft_label is True or CPU is used, the algorithm is always numerically stable. Note that the speed may be slower when use - stable algorithm. Default: False + stable algorithm. Default: True return_softmax (bool): A flag indicating whether to return the softmax along with the cross entropy loss. Default: False @@ -6846,56 +6844,58 @@ def image_resize(input, Example: - For scale: - - if align_corners = True && out_size > 1 : + .. code-block:: text - scale_factor = (in_size-1.0)/(out_size-1.0) - - else: + For scale: + + if align_corners = True && out_size > 1 : + + scale_factor = (in_size-1.0)/(out_size-1.0) + + else: + + scale_factor = float(in_size/out_size) + - scale_factor = float(in_size/out_size) - - - Nearest neighbor interpolation: - - if: - align_corners = False + Nearest neighbor interpolation: + + if: + align_corners = False - input : (N,C,H_in,W_in) - output: (N,C,H_out,W_out) where: + input : (N,C,H_in,W_in) + output: (N,C,H_out,W_out) where: - H_out = \left \lfloor {H_{in} * scale_{}factor}} \right \rfloor - W_out = \left \lfloor {W_{in} * scale_{}factor}} \right \rfloor + H_out = floor (H_{in} * scale_{factor}) + W_out = floor (W_{in} * scale_{factor}) - else: - align_corners = True + else: + align_corners = True - input : (N,C,H_in,W_in) - output: (N,C,H_out,W_out) where: + input : (N,C,H_in,W_in) + output: (N,C,H_out,W_out) where: - H_out = round(H_{in} * scale_{factor}) - W_out = round(W_{in} * scale_{factor}) + H_out = round(H_{in} * scale_{factor}) + W_out = round(W_{in} * scale_{factor}) - Bilinear interpolation: + Bilinear interpolation: - if: - align_corners = False , align_mode = 0 - - input : (N,C,H_in,W_in) - output: (N,C,H_out,W_out) where: - - H_out = (H_{in}+0.5) * scale_{factor} - 0.5 - W_out = (W_{in}+0.5) * scale_{factor} - 0.5 + if: + align_corners = False , align_mode = 0 + + input : (N,C,H_in,W_in) + output: (N,C,H_out,W_out) where: + + H_out = (H_{in}+0.5) * scale_{factor} - 0.5 + W_out = (W_{in}+0.5) * scale_{factor} - 0.5 - else: - - input : (N,C,H_in,W_in) - output: (N,C,H_out,W_out) where: + else: + + input : (N,C,H_in,W_in) + output: (N,C,H_out,W_out) where: - H_out = H_{in} * scale_{factor} - W_out = W_{in} * scale_{factor} + H_out = H_{in} * scale_{factor} + W_out = W_{in} * scale_{factor} For details of nearest neighbor interpolation, please refer to Wikipedia: https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation. @@ -7050,41 +7050,39 @@ def resize_bilinear(input, Align_corners and align_mode are optinal parameters,the calculation method of interpolation can be selected by them. - - Align_corners and align_mode are optinal parameters,the calculation method - of interpolation can be selected by them. - Example: - For scale: - - if align_corners = True && out_size > 1 : + .. code-block:: text - scale_factor = (in_size-1.0)/(out_size-1.0) - - else: + For scale: - scale_factor = float(in_size/out_size) + if align_corners = True && out_size > 1 : - Bilinear interpolation: + scale_factor = (in_size-1.0)/(out_size-1.0) + + else: + + scale_factor = float(in_size/out_size) - if: - align_corners = False , align_mode = 0 - - input : (N,C,H_in,W_in) - output: (N,C,H_out,W_out) where: - - H_out = (H_{in}+0.5) * scale_{factor} - 0.5 - W_out = (W_{in}+0.5) * scale_{factor} - 0.5 + Bilinear interpolation: + + if: + align_corners = False , align_mode = 0 + + input : (N,C,H_in,W_in) + output: (N,C,H_out,W_out) where: + + H_out = (H_{in}+0.5) * scale_{factor} - 0.5 + W_out = (W_{in}+0.5) * scale_{factor} - 0.5 - else: + else: - input : (N,C,H_in,W_in) - output: (N,C,H_out,W_out) where: + input : (N,C,H_in,W_in) + output: (N,C,H_out,W_out) where: - H_out = H_{in} * scale_{factor} - W_out = W_{in} * scale_{factor} + H_out = H_{in} * scale_{factor} + W_out = W_{in} * scale_{factor} @@ -7136,42 +7134,44 @@ def resize_nearest(input, align_corners=True): """ Resize input by performing nearest neighbor interpolation in both the - 3rd dimention(in height direction) and the 4th dimention(in width - direction) based on given output shape which specified by actual_shape, + 3rd dimension(in height direction) and the 4th dimension(in width + direction) based on given output shape which is specified by actual_shape, out_shape and scale in priority order. Example: - For scale: - - if align_corners = True && out_size > 1 : + .. code-block:: text - scale_factor = (in_size-1.0)/(out_size-1.0) - - else: + For scale: - scale_factor = float(in_size/out_size) - - - Nearest neighbor interpolation: - - if: - align_corners = False + if align_corners = True && out_size > 1 : - input : (N,C,H_in,W_in) - output: (N,C,H_out,W_out) where: + scale_factor = (in_size-1.0)/(out_size-1.0) + + else: + + scale_factor = float(in_size/out_size) + + + Nearest neighbor interpolation: + + if: + align_corners = False + + input : (N,C,H_in,W_in) + output: (N,C,H_out,W_out) where: - H_out = \left \lfloor {H_{in} * scale_{}factor}} \right \rfloor - W_out = \left \lfloor {W_{in} * scale_{}factor}} \right \rfloor + H_out = floor(H_{in} * scale_{factor}) + W_out = floor(W_{in} * scale_{factor}) - else: - align_corners = True + else: + align_corners = True - input : (N,C,H_in,W_in) - output: (N,C,H_out,W_out) where: + input : (N,C,H_in,W_in) + output: (N,C,H_out,W_out) where: - H_out = round(H_{in} * scale_{factor}) - W_out = round(W_{in} * scale_{factor}) + H_out = round(H_{in} * scale_{factor}) + W_out = round(W_{in} * scale_{factor}) For details of nearest neighbor interpolation, please refer to Wikipedia: @@ -9945,6 +9945,7 @@ def teacher_student_sigmoid_loss(input, Examples: .. code-block:: python + cost = fluid.layers.teacher_student_sigmoid_loss(input=similarity, label=label) """ helper = LayerHelper('teacher_student_sigmoid_loss', **locals()) diff --git a/python/paddle/fluid/parallel_executor.py b/python/paddle/fluid/parallel_executor.py index 9c578ef662bfdaf03141ee263fb2078064d5bfe6..84beb37c1d995161c473c3d8c11402bee0ae7319 100644 --- a/python/paddle/fluid/parallel_executor.py +++ b/python/paddle/fluid/parallel_executor.py @@ -13,15 +13,11 @@ # limitations under the License. from __future__ import print_function -import multiprocessing from . import core from . import framework from . import executor -from .. import compat as cpt -import warnings +from . import compiler import sys -import six -import os __all__ = ['ParallelExecutor'] @@ -92,104 +88,31 @@ class ParallelExecutor(object): num_trainers=1, trainer_id=0, scope=None): - # step1: get places, the places are used in run too. - self._places = [] - if use_cuda: - gpus_env = os.getenv("FLAGS_selected_gpus") - if gpus_env: - gpus = [int(s) for s in gpus_env.split(",")] - else: - gpus = [ - i for i in six.moves.range(core.get_cuda_device_count()) - ] - self._places = [core.CUDAPlace(i) for i in gpus] - else: - cpu_num = int( - os.environ.get('CPU_NUM', multiprocessing.cpu_count())) - self._places = [core.CPUPlace() for _ in six.moves.range(cpu_num)] - assert self._places, "no place for execution" + sys.stderr.write( + 'ParallelExecutor is deprecated. ' + 'Please use CompiledProgram and Executor. CompiledProgram ' + 'is a central place for optimization and Executor is the ' + 'unified executor. Example can be found in compiler.py.\n') - # step2: init exec_strategy - if exec_strategy is None: - exec_strategy = ExecutionStrategy() - exec_strategy.use_cuda = use_cuda - if exec_strategy.num_threads == 0: - if use_cuda: - # Experiments on se-resnext shows that too many threads hurt - # performance. Worth tunning for other models in the future. - exec_strategy.num_threads = len(self._places) * 4 - else: - cpu_num = int( - os.environ.get('CPU_NUM', multiprocessing.cpu_count())) - exec_strategy.num_threads = cpu_num * 2 - - # step3: init build_strategy if build_strategy is None: build_strategy = BuildStrategy() build_strategy.num_trainers = num_trainers build_strategy.trainer_id = trainer_id - # FIXME(zcd): is_distribution_ is a temporary field, because in pserver mode, - # num_trainers is 1, so the current fields of build_strategy doesn't tell if - # it's distributed model. - build_strategy.is_distribution = framework.is_pserver_mode( - main_program) or num_trainers > 1 - - # step4: get main_program, scope, local_scopes - main = main_program if main_program \ - else framework.default_main_program() - # FIXME(dzhwinter): enable_inplace should be after memory_optimize - # if turn on python memory optimize, turn off the inplace_pass. - if build_strategy.memory_optimize is None: - build_strategy.memory_optimize = False if main._is_mem_optimized else True - if build_strategy.enable_inplace is None: - build_strategy.enable_inplace = False if main._is_mem_optimized else True - scope = scope if scope is not None else executor.global_scope() - - if share_vars_from and not isinstance(share_vars_from, - ParallelExecutor): - raise TypeError("share_vars_from must be ParallelExecutor.") - - local_scopes = share_vars_from.executor.local_scopes()\ - if share_vars_from else [] - - # step5: check trainers_endpoints, it is used for distribution. - trainers_endpoints = main._trainers_endpoints - if num_trainers > 1 and trainers_endpoints: - assert num_trainers == len( - trainers_endpoints), "num_trainers == len(endpoints)" - build_strategy.trainers_endpoints = trainers_endpoints - - # step6: get persistable_vars, places. persistable_vars - # need be broadcast to other local_scope. - persistable_vars = set([ - cpt.to_text(v.name) for v in [ - var for var in main.list_vars() - if var.persistable and var.type != core.VarDesc.VarType.RAW - ] - ]) - - def place_obj(place): - p = core.Place() - p.set_place(place) - return p - - places = list(map(place_obj, self._places)) - # step7: init ParallelExecutor - # ParallelExecutor API will be deprecated, don't support parallel graph. - self._graphs = [] - if build_strategy.async_mode: - for _ in range(cpu_num): - self._graphs.append(core.Graph(main.desc)) - else: - self._graphs.append(core.Graph(main.desc)) + self._places = compiler.get_available_places(use_cuda) + self._scope = scope if scope is not None else executor.global_scope() - self.executor = core.ParallelExecutor( - places, persistable_vars, - cpt.to_text(loss_name) if loss_name else six.u(''), scope, - local_scopes, exec_strategy, build_strategy, self._graphs) - - self.scope = scope + main_program = main_program if main_program is not None \ + else framework.default_main_program() + self._compiled_program = compiler.CompiledProgram(main_program) + self._compiled_program.with_data_parallel( + loss_name=loss_name, + build_strategy=build_strategy, + exec_strategy=exec_strategy, + share_vars_from=share_vars_from) + self._place = core.CUDAPlace(0) if use_cuda else core.CPUPlace() + self._executor = executor.Executor(self._place) + self._compiled_program._compile(place=self._place, scope=self._scope) def run(self, fetch_list, feed=None, feed_dict=None, return_numpy=True): """ @@ -256,56 +179,11 @@ class ParallelExecutor(object): loss = pe.run(feed=feeder.feed(cur_batch), fetch_list=[avg_cost.name])) """ - if feed is None and feed_dict is not None: - feed = feed_dict - print( - "`feed_dict` is deprecated. Please use `feed=`", - file=sys.stderr) - - if isinstance(feed, dict): - feed_tensor_dict = dict() - for feed_name in feed: - feed_tensor = feed[feed_name] - if not isinstance(feed_tensor, core.LoDTensor): - feed_tensor = core.LoDTensor() - # always set to CPU place, since the tensor need to be splitted - # it is fast in CPU - feed_tensor.set(feed[feed_name], core.CPUPlace()) - feed_tensor_dict[feed_name] = feed_tensor - - self.executor.feed_and_split_tensor_into_local_scopes( - feed_tensor_dict) - elif isinstance(feed, list) or isinstance(feed, tuple): - if len(feed) != len(self._places): - raise ValueError( - "Feed a list of tensor, the list should be the same size as places" - ) - - res = list() - - for i, each in enumerate(feed): - if not isinstance(each, dict): - raise TypeError( - "Each element of feed list should be a dict") - res_dict = dict() - for feed_name in each: - tensor = each[feed_name] - if not isinstance(tensor, core.LoDTensor): - tmp = core.LoDTensor() - tmp.set(tensor, self._places[i]) - tensor = tmp - res_dict[feed_name] = tensor - res.append(res_dict) - self.executor.feed_tensors_into_local_scopes(res) - - fetch_var_name = 'fetch' - self.executor.run(fetch_list, fetch_var_name) - arr = self.scope.find_var(fetch_var_name).get_lod_tensor_array() - - if return_numpy: - return executor.as_numpy(arr) - - return [arr[i] for i in range(len(arr))] + return self._executor.run(program=self._compiled_program, + scope=self._scope, + feed=feed, + fetch_list=fetch_list, + return_numpy=return_numpy) @property def device_count(self): diff --git a/python/paddle/fluid/tests/unittests/ir_memory_optimize_net_base.py b/python/paddle/fluid/tests/unittests/ir_memory_optimize_net_base.py new file mode 100644 index 0000000000000000000000000000000000000000..079f0d22056c7a0ebe366a177f62fafad75eff61 --- /dev/null +++ b/python/paddle/fluid/tests/unittests/ir_memory_optimize_net_base.py @@ -0,0 +1,150 @@ +# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +import six +import unittest +import time +import math +import multiprocessing +import numpy as np + +import paddle +import paddle.fluid.core as core +import paddle.fluid as fluid +from paddle.fluid import compiler + +# open eager delete mode +os.environ['FLAGS_eager_delete_tensor_gb'] = '0.0' +os.environ['FLAGS_fast_eager_deletion_mode'] = 'true' +os.environ['CPU_NUM'] = '2' + + +class BuildIrMemOptBase(unittest.TestCase): + def check_network_convergence(self, + network, + use_cuda=True, + memory_opt=True, + use_ir_memory_optimize=True, + enable_inplace=True, + iter=5): + if use_cuda and not core.is_compiled_with_cuda(): + print('Skip use_cuda=True because Paddle is not compiled with cuda') + return + + if os.name == 'nt': + print( + 'Skip use_parallel_executor=True because Paddle comes without parallel support on windows' + ) + return + fluid.default_startup_program().random_seed = 100 + fluid.default_main_program().random_seed = 100 + batch_size = 32 + batch_size *= fluid.core.get_cuda_device_count() if use_cuda else int( + os.environ.get('CPU_NUM', multiprocessing.cpu_count())) + + # build network + word_dict = paddle.dataset.imdb.word_dict() + train_reader = paddle.batch( + paddle.dataset.imdb.train(word_dict), batch_size=batch_size) + + data = fluid.layers.data( + name="words", shape=[1], dtype="int64", lod_level=1) + + label = fluid.layers.data(name="label", shape=[1], dtype="int64") + + cost = network(data, label, len(word_dict)) + optimizer = fluid.optimizer.Adam(learning_rate=0.001) + optimizer.minimize(cost) + if memory_opt: + fluid.memory_optimize(fluid.default_main_program()) + + # execution + place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() + feeder = fluid.DataFeeder(feed_list=[data, label], place=place) + reader = feeder.decorate_reader(train_reader, multi_devices=True) + exe = fluid.Executor(place) + exe.run(fluid.default_startup_program()) + + train_cp = compiler.CompiledProgram(fluid.default_main_program()) + train_cp = train_cp.with_data_parallel(loss_name=cost.name) + fetch_list = [cost.name] + + begin = time.time() + first_loss, last_loss = None, None + step_id = 0 + custom_iter = getattr(self, "iter", None) + if not custom_iter == None: + iter = custom_iter + for data in reader(): + ret = exe.run(train_cp, feed=data, fetch_list=fetch_list) + print(ret) + step_id += 1 + if step_id == 1: + first_loss = ret[0] + if step_id == iter: + last_loss = ret[0] + break + end = time.time() + + print("%.4f Instance per second" % ( + (batch_size * iter) / (end - begin))) + + print(first_loss, last_loss) + avg_last_loss_val = np.array(last_loss).mean() + avg_first_loss_val = np.array(first_loss).mean() + if math.isnan(float(avg_last_loss_val)) or math.isnan( + float(avg_first_loss_val)): + sys.exit("got NaN loss, training failed.") + + return first_loss, last_loss + + +class TestIrMemOptBase(BuildIrMemOptBase): + def setUp(self): + self.network = None + + def test_network(self): + if self.network is None or not core.is_compiled_with_cuda(): + return + + baseline_first_loss, baseline_last_loss = None, None + for use_cuda in [True]: + for use_python_mem_opt in [True, False]: + print( + 'network: {}, use_cuda: {}, use_python_mem_opt: {}, use_ir_mem_opt : {}'. + format(self.network.__name__, use_cuda, use_python_mem_opt, + not use_python_mem_opt)) + with fluid.program_guard(fluid.Program(), fluid.Program()): + with fluid.scope_guard(core.Scope()): + if use_cuda is True and use_python_mem_opt is True: + baseline_first_loss, baseline_last_loss = self.check_network_convergence( + self.network, + use_cuda=use_cuda, + memory_opt=use_python_mem_opt) + else: + cur_first_loss, cur_last_loss = self.check_network_convergence( + self.network, + use_cuda=use_cuda, + memory_opt=use_python_mem_opt) + + self.assertAlmostEquals( + np.mean(baseline_last_loss), + np.mean(cur_last_loss), + delta=1e-2) + self.assertAlmostEquals( + np.mean(baseline_first_loss), + np.mean(cur_first_loss), + delta=1e-2) diff --git a/python/paddle/fluid/tests/unittests/mkldnn/mkldnn_op_test.py b/python/paddle/fluid/tests/unittests/mkldnn/mkldnn_op_test.py new file mode 100644 index 0000000000000000000000000000000000000000..871f8403f812c87ac493b82482fe01fdf61037d4 --- /dev/null +++ b/python/paddle/fluid/tests/unittests/mkldnn/mkldnn_op_test.py @@ -0,0 +1,72 @@ +# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import print_function + +import numpy as np +import paddle.fluid.core as core +import paddle.fluid as fluid + + +def check_if_mkldnn_primitives_exist_in_bwd(test_case, op_type, x, out, + out_grad, x_grad): + def __assert_close(tensor, np_array, msg, atol=1e-4): + test_case.assertTrue( + np.allclose( + np.array(tensor), np_array, atol=atol), msg) + + place = core.CPUPlace() + + var_dict = {'x': x, 'out': out, 'out@GRAD': out_grad, 'x@GRAD': x_grad} + var_names = list(var_dict.keys()) + ground_truth = {name: var_dict[name] for name in var_names} + + program = fluid.Program() + with fluid.program_guard(program): + block = program.global_block() + for name in ground_truth: + block.create_var( + name=name, dtype=np.float32, shape=ground_truth[name].shape) + + op = block.append_op( + type=op_type, + inputs={'X': block.var('x'), }, + outputs={'Out': block.var('out')}, + attrs={'use_mkldnn': True}) + + # Generate backward op_desc + grad_op_desc_list, op_grad_to_var = core.get_grad_op_desc(op.desc, + set(), []) + grad_op_desc = grad_op_desc_list[0] + new_op_desc = block.desc.append_op() + new_op_desc.copy_from(grad_op_desc) + for var_name in grad_op_desc.output_arg_names(): + block.desc.var(var_name.encode('ascii')) + grad_op_desc.infer_var_type(block.desc) + grad_op_desc.infer_shape(block.desc) + for arg in grad_op_desc.output_arg_names(): + grad_var = block.desc.find_var(arg.encode('ascii')) + grad_var.set_dtype(core.VarDesc.VarType.FP32) + + exe = fluid.Executor(place) + + # Do at least 2 iterations + for i in range(2): + out = exe.run( + program, + feed={name: var_dict[name] + for name in ['x', 'out@GRAD']}, + fetch_list=['x@GRAD', 'out']) + + __assert_close(x_grad, out[0], 'x@GRAD') diff --git a/python/paddle/fluid/tests/unittests/mkldnn/test_activation_mkldnn_op.py b/python/paddle/fluid/tests/unittests/mkldnn/test_activation_mkldnn_op.py index 0f301de47f53f3fcacd38d1415ebdbd7b4efc8f1..7099387b887003a205c0dfb4c8e9c83f89e29494 100644 --- a/python/paddle/fluid/tests/unittests/mkldnn/test_activation_mkldnn_op.py +++ b/python/paddle/fluid/tests/unittests/mkldnn/test_activation_mkldnn_op.py @@ -19,7 +19,7 @@ import numpy as np import paddle.fluid.core as core from paddle.fluid.tests.unittests.op_test import OpTest from paddle.fluid.tests.unittests.test_activation_op import TestRelu, TestTanh, TestSqrt, TestAbs -import paddle.fluid as fluid +from mkldnn_op_test import check_if_mkldnn_primitives_exist_in_bwd class TestMKLDNNReluDim2(TestRelu): @@ -98,62 +98,24 @@ class TestMKLDNNAbsDim4(TestAbs): # Check if primitives already exist in backward -class TestMKLDNNReluPrimitivesAlreadyExist(unittest.TestCase): - def __assert_close(self, tensor, np_array, msg, atol=1e-4): - self.assertTrue(np.allclose(np.array(tensor), np_array, atol=atol), msg) - - def test_check_forward_backward(self): - place = core.CPUPlace() +class TestMKLDNNAbsPrimitivesAlreadyExist(unittest.TestCase): + def setUp(self): + super(TestMKLDNNAbsPrimitivesAlreadyExist, self).setUp() np.random.seed(123) - x = np.random.uniform(-1, 1, [2, 2]).astype(np.float32) - out = np.abs(x) - - out_grad = np.random.random_sample(x.shape).astype(np.float32) - x_grad = out_grad * np.sign(x) # Abs grad calculation - - var_dict = {'x': x, 'out': out, 'out@GRAD': out_grad, 'x@GRAD': x_grad} - var_names = list(var_dict.keys()) - ground_truth = {name: var_dict[name] for name in var_names} - - program = fluid.Program() - with fluid.program_guard(program): - block = program.global_block() - for name in ground_truth: - block.create_var( - name=name, dtype='float32', shape=ground_truth[name].shape) - - relu_op = block.append_op( - type="abs", - inputs={"X": block.var('x'), }, - outputs={"Out": block.var('out')}, - attrs={"use_mkldnn": True}) - - # Generate backward op_desc - grad_op_desc_list, op_grad_to_var = core.get_grad_op_desc( - relu_op.desc, set(), []) - grad_op_desc = grad_op_desc_list[0] - new_op_desc = block.desc.append_op() - new_op_desc.copy_from(grad_op_desc) - for var_name in grad_op_desc.output_arg_names(): - block.desc.var(var_name.encode("ascii")) - grad_op_desc.infer_var_type(block.desc) - grad_op_desc.infer_shape(block.desc) - for arg in grad_op_desc.output_arg_names(): - grad_var = block.desc.find_var(arg.encode("ascii")) - grad_var.set_dtype(core.VarDesc.VarType.FP32) - - exe = fluid.Executor(place) - - # Do at least 2 iterations - for i in range(2): - out = exe.run( - program, - feed={name: var_dict[name] - for name in ['x', 'out@GRAD']}, - fetch_list=['x@GRAD']) - - self.__assert_close(x_grad, out[0], "x@GRAD") + self.op_type = 'abs' + self.x = np.random.uniform(-1, 1, [2, 2]).astype(np.float32) + self.out = np.abs(self.x) + self.out_grad = np.random.random_sample(self.x.shape).astype(np.float32) + self.x_grad = self.__abs_bwd(self.x, self.out_grad) + + # Abs grad calculation + def __abs_bwd(self, x, out_grad): + return out_grad * np.sign(x) + + def test_check(self): + check_if_mkldnn_primitives_exist_in_bwd( + self, self.op_type, self.x, self.out, self.out_grad, self.x_grad) if __name__ == '__main__': diff --git a/python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_mkldnn_op.py b/python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_mkldnn_op.py index 0542eef80070cbf281ee013c28b7092a2dd17eaa..28b670d7ab3267a03157b7e617504eb9a35656aa 100644 --- a/python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_mkldnn_op.py +++ b/python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_mkldnn_op.py @@ -15,44 +15,139 @@ from __future__ import print_function import unittest +import numpy as np -from paddle.fluid.tests.unittests.test_conv2d_op import TestConv2dOp, TestWithPad, TestWithStride, TestWithGroup, TestWith1x1, TestWithInput1x1Filter1x1 +import paddle.fluid.core as core +from paddle.fluid.tests.unittests.op_test import OpTest +from paddle.fluid.tests.unittests.test_conv2d_op import TestConv2dOp -class TestMKLDNN(TestConv2dOp): - def init_kernel_type(self): - self.use_mkldnn = True - self.data_format = "NCHW" +def conv2d_bias_naive(out, bias): + _, out_c, _, _ = out.shape + for l in range(out_c): + out[:, l, :, :] = out[:, l, :, :] + bias[l] + return out -class TestMKLDNNWithPad(TestWithPad): - def init_kernel_type(self): - self.use_mkldnn = True - self.data_format = "NCHW" +def conv2d_residual_naive(out, residual): + assert out.shape == residual.shape + out = np.add(out, residual) + return out -class TestMKLDNNWithStride(TestWithStride): - def init_kernel_type(self): - self.use_mkldnn = True - self.data_format = "NCHW" +class TestConv2dMKLDNNOp(TestConv2dOp): + def init_group(self): + self.groups = 1 -class TestMKLDNNWithGroup(TestWithGroup): def init_kernel_type(self): - self.use_mkldnn = True self.data_format = "NCHW" + self.use_mkldnn = True + self._cpu_only = True + def init_test_case(self): + self.pad = [0, 0] + self.stride = [1, 1] + self.input_size = [2, 3, 5, 5] # NCHW + assert np.mod(self.input_size[1], self.groups) == 0 + f_c = self.input_size[1] // self.groups + self.filter_size = [6, f_c, 3, 3] -class TestMKLDNNWith1x1(TestWith1x1): - def init_kernel_type(self): - self.use_mkldnn = True - self.data_format = "NCHW" + def setUp(self): + self.fuse_bias = False + self.bias_size = None + self.fuse_relu = False + self.fuse_residual_connection = False + self.input_residual_size = None + TestConv2dOp.setUp(self) + output = self.outputs['Output'] -class TestMKLDNNWithInput1x1Filter1x1(TestWithInput1x1Filter1x1): - def init_kernel_type(self): - self.use_mkldnn = True - self.data_format = "NCHW" + #mkldnn only support either conv-sum-relu, or conv-relu. + if self.fuse_bias and self.bias_size is not None: + bias = np.random.random(self.bias_size).astype(self.dtype) + output = conv2d_bias_naive(output, bias) + output = output.astype(self.dtype) + self.attrs['fuse_bias'] = self.fuse_bias + self.inputs['Bias'] = OpTest.np_dtype_to_fluid_dtype(bias) + + if self.fuse_residual_connection and self.input_residual_size is not None: + input_residual = np.random.random(self.input_residual_size).astype( + self.dtype) + output = conv2d_residual_naive(output, input_residual) + + self.attrs[ + 'fuse_residual_connection'] = self.fuse_residual_connection + self.inputs['ResidualData'] = OpTest.np_dtype_to_fluid_dtype( + input_residual) + + if self.fuse_relu: + output = np.maximum(output, 0).astype(self.dsttype) + + output = output.astype(self.dtype) + + self.attrs['fuse_bias'] = self.fuse_bias + self.attrs['fuse_relu'] = self.fuse_relu + self.attrs['fuse_residual_connection'] = self.fuse_residual_connection + + self.outputs['Output'] = output + + +class TestWithFuse(TestConv2dMKLDNNOp): + def init_test_case(self): + TestConv2dMKLDNNOp.init_test_case(self) + self.pad = [1, 1] + self.fuse_bias = True + self.bias_size = [6] + self.fuse_residual_connection = True + self.input_residual_size = [2, 6, 5, 5] + + def test_check_grad(self): + pass + + def test_check_grad_no_filter(self): + pass + + def test_check_grad_no_input(self): + pass + + +class TestWithPadWithBias(TestConv2dMKLDNNOp): + def init_test_case(self): + TestConv2dMKLDNNOp.init_test_case(self) + self.pad = [1, 1] + self.input_size = [2, 3, 6, 6] + + +class TestWithStride(TestConv2dMKLDNNOp): + def init_test_case(self): + TestConv2dMKLDNNOp.init_test_case(self) + self.pad = [1, 1] + self.stride = [2, 2] + self.input_size = [2, 3, 6, 6] + + +class TestWithGroup(TestConv2dMKLDNNOp): + def init_group(self): + self.groups = 3 + + +class TestWith1x1(TestConv2dMKLDNNOp): + def init_test_case(self): + TestConv2dMKLDNNOp.init_test_case(self) + self.filter_size = [6, 3, 1, 1] + + +class TestWithInput1x1Filter1x1(TestConv2dMKLDNNOp): + def init_test_case(self): + TestConv2dMKLDNNOp.init_test_case(self) + self.input_size = [2, 3, 1, 1] # NCHW + assert np.mod(self.input_size[1], self.groups) == 0 + f_c = self.input_size[1] // self.groups + self.filter_size = [6, f_c, 1, 1] + + def init_group(self): + self.groups = 3 if __name__ == '__main__': diff --git a/python/paddle/fluid/tests/unittests/mkldnn/test_pool2d_mkldnn_op.py b/python/paddle/fluid/tests/unittests/mkldnn/test_pool2d_mkldnn_op.py index 6de43dd46e5d184ec934f2d85e0c87137e9702e0..feb2a563eeaed7a83a82ec56ec08a0ed8664d126 100644 --- a/python/paddle/fluid/tests/unittests/mkldnn/test_pool2d_mkldnn_op.py +++ b/python/paddle/fluid/tests/unittests/mkldnn/test_pool2d_mkldnn_op.py @@ -18,6 +18,24 @@ import unittest from paddle.fluid.tests.unittests.test_pool2d_op import TestPool2D_Op, TestCase1, TestCase2, TestCase3, TestCase4, TestCase5 +def create_test_mkldnn_use_ceil_class(parent): + class TestMKLDNNPool2DUseCeilCase(parent): + def init_kernel_type(self): + self.use_mkldnn = True + + def init_ceil_mode(self): + self.ceil_mode = True + + cls_name = "{0}_{1}".format(parent.__name__, "MKLDNNCeilModeCast") + TestMKLDNNPool2DUseCeilCase.__name__ = cls_name + globals()[cls_name] = TestMKLDNNPool2DUseCeilCase + + +create_test_mkldnn_use_ceil_class(TestPool2D_Op) +create_test_mkldnn_use_ceil_class(TestCase1) +create_test_mkldnn_use_ceil_class(TestCase2) + + def create_test_mkldnn_class(parent): class TestMKLDNNCase(parent): def init_kernel_type(self): diff --git a/python/paddle/fluid/tests/unittests/mkldnn/test_softmax_mkldnn_op.py b/python/paddle/fluid/tests/unittests/mkldnn/test_softmax_mkldnn_op.py new file mode 100644 index 0000000000000000000000000000000000000000..748b77f2bf48f450426d3ea918138a7db8df78f0 --- /dev/null +++ b/python/paddle/fluid/tests/unittests/mkldnn/test_softmax_mkldnn_op.py @@ -0,0 +1,57 @@ +# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import print_function + +import unittest +import numpy as np +from paddle.fluid.tests.unittests.op_test import OpTest +import paddle.fluid.core as core +from paddle.fluid.tests.unittests.test_softmax_op import TestSoftmaxOp, stable_softmax +from mkldnn_op_test import check_if_mkldnn_primitives_exist_in_bwd + + +class TestSoftmaxMKLDNNOp(TestSoftmaxOp): + def init_kernel_type(self): + self.use_mkldnn = True + + +class TestSoftmaxMKLDNNOp2(TestSoftmaxMKLDNNOp): + def get_x_shape(self): + return [2, 3, 4, 5] + + +# Check if primitives already exist in backward +class TestSoftmaxMKLDNNPrimitivesAlreadyExist(unittest.TestCase): + def setUp(self): + super(TestSoftmaxMKLDNNPrimitivesAlreadyExist, self).setUp() + + np.random.seed(123) + self.op_type = 'softmax' + self.x = np.random.uniform(-1, 1, 2).astype(np.float32) + self.out = stable_softmax(self.x) + self.out_grad = np.random.random_sample(self.x.shape).astype(np.float32) + self.x_grad = self.__softmax_bwd(self.out, self.out_grad) + + # Softmax grad calculation + def __softmax_bwd(self, out, out_grad): + return out * (out_grad - np.dot(out, out_grad)) + + def test_check(self): + check_if_mkldnn_primitives_exist_in_bwd( + self, self.op_type, self.x, self.out, self.out_grad, self.x_grad) + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/fluid/tests/unittests/ngraph/test_adam_ngraph_op.py b/python/paddle/fluid/tests/unittests/ngraph/test_adam_ngraph_op.py new file mode 100644 index 0000000000000000000000000000000000000000..ef2aedf65f4c0cc182738c7a7a538095f8f628d5 --- /dev/null +++ b/python/paddle/fluid/tests/unittests/ngraph/test_adam_ngraph_op.py @@ -0,0 +1,21 @@ +# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import print_function + +import unittest +from paddle.fluid.tests.unittests.test_adam_op import TestAdamOp1, TestAdamOp2, TestAdamOpMultipleSteps, TestSparseAdamOp + +if __name__ == "__main__": + unittest.main() diff --git a/python/paddle/fluid/tests/unittests/ngraph/test_concat_ngraph_op.py b/python/paddle/fluid/tests/unittests/ngraph/test_concat_ngraph_op.py new file mode 100644 index 0000000000000000000000000000000000000000..a223d73a7416c3564d5d4ef5ca4f3e1b42595a0d --- /dev/null +++ b/python/paddle/fluid/tests/unittests/ngraph/test_concat_ngraph_op.py @@ -0,0 +1,21 @@ +# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import print_function + +import unittest +from paddle.fluid.tests.unittests.test_concat_op import TestConcatOp, TestConcatOp2, TestConcatOp3 + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/fluid/tests/unittests/test_activation_op.py b/python/paddle/fluid/tests/unittests/test_activation_op.py index 55c43ef115a316cc0fe5bb336b7a766a956c1496..d5a838540994abcd1407fd258e723218670bfb58 100644 --- a/python/paddle/fluid/tests/unittests/test_activation_op.py +++ b/python/paddle/fluid/tests/unittests/test_activation_op.py @@ -26,6 +26,7 @@ class TestActivation(OpTest): self.op_type = "exp" self.dtype = np.float32 self.init_dtype() + self.init_kernel_type() x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype) out = np.exp(x) @@ -44,6 +45,9 @@ class TestActivation(OpTest): def init_dtype(self): self.dtype = np.float32 + def init_kernel_type(self): + pass + class TestSigmoid(TestActivation): def setUp(self): @@ -601,6 +605,25 @@ class TestSwish(TestActivation): self.check_grad(['X'], 'Out', max_relative_error=0.008) +#------------------ Test Cudnn Activation---------------------- +def create_test_act_cudnn_class(parent, atol=1e-3, grad_atol=1e-3): + @unittest.skipIf(not core.is_compiled_with_cuda(), + "core is not compiled with CUDA") + class TestActCudnn(parent): + def init_kernel_type(self): + self.attrs = {"use_cudnn": True} + + cls_name = "{0}_{1}".format(parent.__name__, "cudnn") + TestActCudnn.__name__ = cls_name + globals()[cls_name] = TestActCudnn + + +create_test_act_cudnn_class(TestRelu) +create_test_act_cudnn_class(TestRelu6) +create_test_act_cudnn_class(TestSigmoid) +create_test_act_cudnn_class(TestTanh) + + #------------------ Test Fp16 ---------------------- def create_test_act_fp16_class(parent, atol=1e-3, diff --git a/python/paddle/fluid/tests/unittests/test_alloc_continuous_space_op.py b/python/paddle/fluid/tests/unittests/test_alloc_continuous_space_op.py new file mode 100644 index 0000000000000000000000000000000000000000..9d5fe114bad2b2bae73cf18e17ebd7af288a91da --- /dev/null +++ b/python/paddle/fluid/tests/unittests/test_alloc_continuous_space_op.py @@ -0,0 +1,74 @@ +# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import print_function + +import unittest +import numpy as np + +from op_test import OpTest + + +class TestAllocContinuousSpace(OpTest): + def setUp(self): + self.op_type = "alloc_continuous_space" + self.dtype = np.float32 + attrs = self.init_attr() + self.copy_data = attrs["copy_data"] + self.constant = attrs["constant"] + self.set_constant = attrs["set_constant"] + self.Inputs = self.init_input() + self.FusedOutput = self.init_output(self.Inputs, self.set_constant, + self.constant) + self.inputs = {'Input': self.Inputs} + self.attrs = attrs + self.outputs = {'Output': self.Inputs, 'FusedOutput': self.FusedOutput} + + def init_dtype(self): + self.dtype = np.float32 + + def init_input(self): + inputs = [] + inputs.append(("x1", np.random.random([20, 3]).astype(self.dtype))) + inputs.append(("x2", np.random.random([20]).astype(self.dtype))) + inputs.append(("x3", np.random.random([1]).astype(self.dtype))) + inputs.append(("x4", np.random.random([200, 30]).astype(self.dtype))) + inputs.append(("x5", np.random.random([30]).astype(self.dtype))) + inputs.append(("x6", np.random.random([1]).astype(self.dtype))) + return inputs + + def init_attr(self): + return {"copy_data": True, "set_constant": False, "constant": 0.0} + + def init_output(self, input_list, set_constant, constant): + inputs = [input[1].flatten() for input in input_list] + output = np.concatenate(inputs) + if set_constant: + output = np.ones((len(output))) * constant + return output + + def test_check_output(self): + self.check_output() + + +class TestAllocContinuousSpace2(TestAllocContinuousSpace): + def init_attr(self): + return {"copy_data": False, "set_constant": True, "constant": 0.5} + + def test_check_output(self): + self.check_output(no_check_set=["Output"]) + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/fluid/tests/unittests/test_eager_deletion_dynamic_rnn_base.py b/python/paddle/fluid/tests/unittests/test_eager_deletion_dynamic_rnn_base.py index bc3c422f2f68b79b2d938e25625093b2ce8977bb..910f53a91a7b5ca1413adf9505ed2c3ad3d56dad 100644 --- a/python/paddle/fluid/tests/unittests/test_eager_deletion_dynamic_rnn_base.py +++ b/python/paddle/fluid/tests/unittests/test_eager_deletion_dynamic_rnn_base.py @@ -56,6 +56,8 @@ def train(network, use_cuda, use_parallel_executor, batch_size=32, pass_num=2): train_reader, multi_devices=use_parallel_executor) exe = fluid.Executor(place) + fluid.default_startup_program().random_seed = 1 + fluid.default_main_program().random_seed = 1 exe.run(fluid.default_startup_program()) train_cp = compiler.CompiledProgram(fluid.default_main_program()) diff --git a/python/paddle/fluid/tests/unittests/test_fake_quantize_op.py b/python/paddle/fluid/tests/unittests/test_fake_quantize_op.py index 820ad4af88e9dc49cbe57ac182e1ba0402725f3d..4582b2a0eed401235835374d4cd58782d8d3a68f 100644 --- a/python/paddle/fluid/tests/unittests/test_fake_quantize_op.py +++ b/python/paddle/fluid/tests/unittests/test_fake_quantize_op.py @@ -35,7 +35,7 @@ class TestFakeQuantizeOp(OpTest): self.check_output() -class TestFakeQuantizeOp(OpTest): +class TestFakeQuantizeRangeAbsMaxOp(OpTest): def setUp(self): self.op_type = "fake_quantize_range_abs_max" self.attrs = { @@ -43,8 +43,10 @@ class TestFakeQuantizeOp(OpTest): 'window_size': int(1), 'is_test': False } + x = (np.random.random((8, 16, 7, 7)) - 0.5) * 10 + x = x.astype("float32") self.inputs = { - 'X': np.random.random((8, 16, 7, 7)).astype("float32"), + 'X': x, 'Iter': np.zeros(1).astype("int64"), 'InScale': np.zeros(1).astype("float32") } @@ -62,5 +64,36 @@ class TestFakeQuantizeOp(OpTest): self.check_output() +class TestFakeQuantizeRangeAbsMaxOp2(OpTest): + def setUp(self): + self.op_type = "fake_quantize_range_abs_max" + self.attrs = { + 'bit_length': int(8), + 'window_size': int(1), + 'is_test': True + } + x = (np.random.random((8, 16, 7, 7)) - 0.5) * 10 + x = x.astype("float32") + scale = np.max(np.abs(x)).astype("float32") - 1.0 + out_scales = np.zeros(self.attrs['window_size']).astype("float32") + out_scales[0] = scale + + self.inputs = { + 'X': x, + 'Iter': np.zeros(1).astype("int64"), + 'InScale': scale.astype("float32") + } + xs = np.clip(x, -scale, scale) + qs = np.round(xs / scale * ((1 << (self.attrs['bit_length'] - 1)) - 1)) + self.outputs = { + 'Out': qs, + 'OutScale': scale.astype("float32"), + 'OutScales': out_scales, + } + + def test_check_output(self): + self.check_output(no_check_set=set(['OutScale', 'OutScales'])) + + if __name__ == "__main__": unittest.main() diff --git a/python/paddle/fluid/tests/unittests/test_imperative.py b/python/paddle/fluid/tests/unittests/test_imperative_basic.py similarity index 100% rename from python/paddle/fluid/tests/unittests/test_imperative.py rename to python/paddle/fluid/tests/unittests/test_imperative_basic.py diff --git a/python/paddle/fluid/tests/unittests/test_imperative_optimizer.py b/python/paddle/fluid/tests/unittests/test_imperative_optimizer.py index 0d0a3bbe0bd47fe0e01761f8b42c92b884a5680a..7afbf61472a3d09ba5e34731d3a3ebbb8076e310 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_optimizer.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_optimizer.py @@ -12,6 +12,8 @@ # See the License for the specific language governing permissions and # limitations under the License. +from __future__ import print_function + import contextlib import unittest import numpy as np @@ -142,8 +144,6 @@ class TestImperativeMnist(unittest.TestCase): sgd.minimize(avg_loss) mnist.clear_gradients() - fluid.default_main_program().global_block()._clear_block() - dy_param_value = {} for param in mnist.parameters(): dy_param_value[param.name] = param._numpy() diff --git a/python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py b/python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py index c8e42d5ede57896b0d5c09a2334709ced2d16a3f..878c27d9344111d18e1ff27a1d4f41f8ae0df4b0 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py @@ -243,7 +243,9 @@ class TestImperativePtbRnn(unittest.TestCase): dy_loss = None last_hidden = None last_cell = None - for i in range(2): + batch_num = 50 + + for i in range(batch_num): x_data = np.arange(12).reshape(4, 3).astype('int64') y_data = np.arange(1, 13).reshape(4, 3).astype('int64') x_data = x_data.reshape((-1, num_steps, 1)) @@ -302,7 +304,7 @@ class TestImperativePtbRnn(unittest.TestCase): static_loss_value = None static_last_cell_value = None static_last_hidden_value = None - for i in range(2): + for i in range(batch_num): x_data = np.arange(12).reshape(4, 3).astype('int64') y_data = np.arange(1, 13).reshape(4, 3).astype('int64') x_data = x_data.reshape((-1, num_steps, 1)) diff --git a/python/paddle/fluid/tests/unittests/test_imperative_resnet.py b/python/paddle/fluid/tests/unittests/test_imperative_resnet.py index 4892495e1108e6d2a7e96cab88dc7668e360d79f..94ac3933151ac612ea9d308f0e28c73f0c067abf 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_resnet.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_resnet.py @@ -231,7 +231,7 @@ class TestImperativeResnet(unittest.TestCase): seed = 90 batch_size = train_parameters["batch_size"] - batch_num = 2 + batch_num = 20 with fluid.imperative.guard(): fluid.default_startup_program().random_seed = seed fluid.default_main_program().random_seed = seed @@ -286,8 +286,6 @@ class TestImperativeResnet(unittest.TestCase): optimizer.minimize(avg_loss) resnet.clear_gradients() - fluid.default_main_program().global_block()._clear_block() - dy_param_value = {} for param in resnet.parameters(): dy_param_value[param.name] = param._numpy() diff --git a/python/paddle/fluid/tests/unittests/test_ir_memory_optimize_nlp.py b/python/paddle/fluid/tests/unittests/test_ir_memory_optimize_nlp.py new file mode 100644 index 0000000000000000000000000000000000000000..30b6d6106cdc46cfed201e5bb44a0c80d7e8ca3d --- /dev/null +++ b/python/paddle/fluid/tests/unittests/test_ir_memory_optimize_nlp.py @@ -0,0 +1,55 @@ +# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# nlp model stack of op operate on lod. It's a classical test case in optimize pass. + +from __future__ import print_function + +import paddle.fluid as fluid +import unittest +from ir_memory_optimize_net_base import TestIrMemOptBase + + +def lstm_net(data, + label, + dict_dim, + emb_dim=128, + hid_dim=128, + hid_dim2=96, + class_dim=2, + emb_lr=30.0): + emb = fluid.layers.embedding( + input=data, + size=[dict_dim, emb_dim], + param_attr=fluid.ParamAttr(learning_rate=emb_lr)) + fc0 = fluid.layers.fc(input=emb, size=hid_dim * 4) + + lstm_h, c = fluid.layers.dynamic_lstm( + input=fc0, size=hid_dim * 4, is_reverse=False) + lstm_max = fluid.layers.sequence_pool(input=lstm_h, pool_type='max') + lstm_max_tanh = fluid.layers.tanh(lstm_max) + fc1 = fluid.layers.fc(input=lstm_max_tanh, size=hid_dim2, act='tanh') + prediction = fluid.layers.fc(input=fc1, size=class_dim, act='softmax') + cost = fluid.layers.cross_entropy(input=prediction, label=label) + avg_cost = fluid.layers.mean(x=cost) + return avg_cost + + +class TestIrMemOptRNN(TestIrMemOptBase): + def setUp(self): + self.network = lstm_net + + +if __name__ == "__main__": + unittest.main() diff --git a/python/paddle/fluid/tests/unittests/test_ir_memory_optimize_transformer.py b/python/paddle/fluid/tests/unittests/test_ir_memory_optimize_transformer.py index c0f480e34dcac3351ba3008ad632a29943afdb81..50d998990f9bbba0d35241f5e53d05675ca08c28 100644 --- a/python/paddle/fluid/tests/unittests/test_ir_memory_optimize_transformer.py +++ b/python/paddle/fluid/tests/unittests/test_ir_memory_optimize_transformer.py @@ -13,21 +13,44 @@ # limitations under the License. import os +import sys import unittest +from timeit import default_timer as timer +import paddle import paddle.fluid as fluid import paddle.fluid.core as core +import paddle.dataset.wmt16 as wmt16 os.environ['FLAGS_eager_delete_tensor_gb'] = "0.0" os.environ[ 'RECORDIO_FILENAME'] = '/tmp/ir_memory_optimize_transformer.wmt16.recordio' -from test_parallel_executor_transformer import TestTransformer -from test_parallel_executor_transformer import transformer +from test_parallel_executor_transformer import transformer, ModelHyperParams, transformer_model, transformer, prepare_batch_input +from parallel_executor_test_base import TestParallelExecutorBase # NOTE(dzhwinter): test diferent strategy colisions. # open the eager delete tensor strategy by default. -class TestTransformerWithIR(TestTransformer): +class TestTransformerWithIR(TestParallelExecutorBase): + @classmethod + def setUpClass(cls): + os.environ['CPU_NUM'] = str(4) + reader = paddle.batch( + wmt16.train(ModelHyperParams.src_vocab_size, + ModelHyperParams.trg_vocab_size), + batch_size=transformer_model.batch_size) + + with fluid.recordio_writer.create_recordio_writer( + os.environ.get("RECORDIO_FILENAME")) as writer: + for batch in reader(): + for tensor in prepare_batch_input( + batch, ModelHyperParams.src_pad_idx, + ModelHyperParams.trg_pad_idx, ModelHyperParams.n_head): + t = fluid.LoDTensor() + t.set(tensor, fluid.CPUPlace()) + writer.append_tensor(t) + writer.complete_append_tensor() + def test_main(self): if core.is_compiled_with_cuda(): # check python transpiler @@ -35,13 +58,15 @@ class TestTransformerWithIR(TestTransformer): transformer, use_cuda=True, memory_opt=True, - use_ir_memory_optimize=False) + use_ir_memory_optimize=False, + iter=2) # check IR memory optimize self.check_network_convergence( transformer, use_cuda=True, memory_opt=False, - use_ir_memory_optimize=True) + use_ir_memory_optimize=True, + iter=2) if __name__ == '__main__': 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 0d3e6d73e0149fe633b8f1de9041068c2e3bb293..5212d97dfbc16e463e5f68456a3d735ac6679ae1 100644 --- a/python/paddle/fluid/tests/unittests/test_learning_rate_scheduler.py +++ b/python/paddle/fluid/tests/unittests/test_learning_rate_scheduler.py @@ -82,6 +82,13 @@ def piecewise_decay(global_step, boundaries, values): return values[len(values) - 1] +def cosine_decay(global_step, learning_rate, step_each_epoch, epochs): + cur_epoch = math.floor(global_step / step_each_epoch) + decayed_lr = learning_rate * 0.5 * ( + math.cos(cur_epoch * math.pi / epochs) + 1) + return decayed_lr + + class TestLearningRateDecay(unittest.TestCase): def check_decay(self, python_decay_fn, fluid_decay_fn, kwargs): places = [fluid.CPUPlace()] @@ -149,6 +156,11 @@ class TestLearningRateDecay(unittest.TestCase): "boundaries": [3, 6, 9], "values": [0.1, 0.2, 0.3, 0.4] }), + (cosine_decay, layers.cosine_decay, { + "learning_rate": 0.1, + "step_each_epoch": 100, + "epochs": 120 + }), ] for py_decay_fn, fluid_decay_fn, kwargs in decay_fns: diff --git a/python/paddle/fluid/tests/unittests/test_sgd_op.py b/python/paddle/fluid/tests/unittests/test_sgd_op.py index b46e4bfb86bd5dc9c74375693328f2506281be3e..162e6d1938c8174d342d8e4af1e4b6c424afc521 100644 --- a/python/paddle/fluid/tests/unittests/test_sgd_op.py +++ b/python/paddle/fluid/tests/unittests/test_sgd_op.py @@ -24,17 +24,28 @@ from op_test import OpTest class TestSGDOp(OpTest): def setUp(self): self.op_type = "sgd" - w = np.random.random((102, 105)).astype("float32") - g = np.random.random((102, 105)).astype("float32") + self.conf() + w = np.random.random((self.h, self.w)).astype("float32") + g = np.random.random((self.h, self.w)).astype("float32") lr = np.array([0.1]).astype("float32") self.inputs = {'Param': w, 'Grad': g, 'LearningRate': lr} self.outputs = {'ParamOut': w - lr * g} + def conf(self): + self.h = 102 + self.w = 105 + def test_check_output(self): self.check_output() +class TestSGDOpCase8X(TestSGDOp): + def conf(self): + self.h = 10 + self.w = 64 + + class TestSparseSGDOp(unittest.TestCase): def check_with_place(self, place): scope = core.Scope() @@ -42,12 +53,12 @@ class TestSparseSGDOp(unittest.TestCase): # create and initialize Grad Variable height = 10 rows = [0, 4, 7] - row_numel = 12 + self.conf() grad_selected_rows = scope.var('Grad').get_selected_rows() grad_selected_rows.set_height(height) grad_selected_rows.set_rows(rows) - np_array = np.ones((len(rows), row_numel)).astype("float32") + np_array = np.ones((len(rows), self.row_numel)).astype("float32") np_array[0, 0] = 2.0 np_array[2, 8] = 4.0 @@ -56,7 +67,7 @@ class TestSparseSGDOp(unittest.TestCase): # create and initialize Param Variable param = scope.var('Param').get_tensor() - param_array = np.full((height, row_numel), 5.0).astype("float32") + param_array = np.full((height, self.row_numel), 5.0).astype("float32") param.set(param_array, place) # create and initialize LeraningRate Variable @@ -98,6 +109,14 @@ class TestSparseSGDOp(unittest.TestCase): for place in places: self.check_with_place(place) + def conf(self): + self.row_numel = 12 + + +class TestSparseSGDOpCase8X(TestSparseSGDOp): + def conf(self): + self.row_numel = 16 + class TestSGDOpOptimizeSelectedRows(unittest.TestCase): def check_with_place(self, place): diff --git a/python/paddle/fluid/tests/unittests/test_softmax_op.py b/python/paddle/fluid/tests/unittests/test_softmax_op.py index 40c3135183a128cd9b7324ce27da798fa2d93afd..5c56de6779d238064f03a65b54f3c73a77119f60 100644 --- a/python/paddle/fluid/tests/unittests/test_softmax_op.py +++ b/python/paddle/fluid/tests/unittests/test_softmax_op.py @@ -144,15 +144,5 @@ class TestSoftmaxFP16CUDNNOp2(TestSoftmaxFP16CUDNNOp): return [2, 3, 4, 5] -class TestSoftmaxMKLDNNOp(TestSoftmaxOp): - def init_kernel_type(self): - self.use_mkldnn = True - - -class TestSoftmaxMKLDNNOp2(TestSoftmaxMKLDNNOp): - def get_x_shape(self): - return [2, 3, 4, 5] - - if __name__ == "__main__": unittest.main() diff --git a/python/paddle/fluid/transpiler/inference_transpiler.py b/python/paddle/fluid/transpiler/inference_transpiler.py index cc7f5ec90c26c87b7c5514c900e853be9e16d6eb..8a527e72fb9ac806254d2c055fc283c938cc55b4 100644 --- a/python/paddle/fluid/transpiler/inference_transpiler.py +++ b/python/paddle/fluid/transpiler/inference_transpiler.py @@ -15,6 +15,7 @@ from __future__ import print_function import os +import sys import numpy as np from .. import core from ..framework import Program @@ -50,6 +51,9 @@ class InferenceTranspiler(object): place (Place): inference place scope (Scope|None): inference Scope ''' + sys.stderr.write("InferenceTranspiler is deprecated since it's not " + "safe. Users should be " + "responsible for constructing the inference program\n") if not isinstance(program, Program): raise TypeError("program should be as Program type") if not isinstance(place, core.CPUPlace) and not isinstance( diff --git a/python/paddle/fluid/transpiler/memory_optimization_transpiler.py b/python/paddle/fluid/transpiler/memory_optimization_transpiler.py index ee8cde441ffc63ebd923bd579a7f44d1e2218cf0..c434423bae76c2ebdd7bdeb164350d6ec66621c8 100755 --- a/python/paddle/fluid/transpiler/memory_optimization_transpiler.py +++ b/python/paddle/fluid/transpiler/memory_optimization_transpiler.py @@ -15,6 +15,7 @@ from __future__ import print_function import six +import sys from collections import defaultdict, MutableSet from .. import core from ... import compat as cpt @@ -509,6 +510,8 @@ def memory_optimize(input_program, Returns: None """ + sys.stderr.write('memory_optimize is deprecated. ' + 'Use CompiledProgram and Executor\n') def to_name_str(var): if isinstance(var, Variable): diff --git a/python/paddle/utils/plot.py b/python/paddle/utils/plot.py index 08889c0313fc24151cde6ca7b662d81eb53c9d7b..ee651f2f0cd6f2e594a4e74c896baa924f70bbf5 100644 --- a/python/paddle/utils/plot.py +++ b/python/paddle/utils/plot.py @@ -13,6 +13,7 @@ # limitations under the License. import os +import six class PlotData(object): @@ -60,9 +61,9 @@ class Ploter(object): def append(self, title, step, value): """ - Feed data - - Args: + Feed data + + Args: title: assign the group data to this subtitle. step: the x_axis of data. value: the y_axis of data. @@ -71,9 +72,9 @@ class Ploter(object): .. code-block:: python plot_curve = Ploter("Curve 1","Curve 2") plot_curve.append(title="Curve 1",step=1,value=1) - """ - assert isinstance(title, basestring) - assert self.__plot_data__.has_key(title) + """ + assert isinstance(title, six.string_types) + assert title in self.__plot_data__ data = self.__plot_data__[title] assert isinstance(data, PlotData) data.append(step, value) @@ -89,7 +90,7 @@ class Ploter(object): .. code-block:: python plot_curve = Ploter() plot_cure.plot() - """ + """ if self.__plot_is_disabled__(): return diff --git a/python/paddle/utils/preprocess_img.py b/python/paddle/utils/preprocess_img.py index a322f7b769a2a32df516a4b8ea04289a7f882ff2..fc67949dfe0ef21487de29678781aa2bfd93f354 100644 --- a/python/paddle/utils/preprocess_img.py +++ b/python/paddle/utils/preprocess_img.py @@ -122,7 +122,7 @@ class ImageClassificationDatasetCreater(preprocess_util.DatasetCreater): def create_dataset_from_list(self, path): data = [] label_set = [] - for line in open(file_list): + for line in open(path): items = line.rstrip.split() image_path = items[0] label_name = items[1] @@ -141,7 +141,7 @@ class ImageClassificationDatasetCreater(preprocess_util.DatasetCreater): path: the path of the image dataset. """ if self.from_list: - return create_dataset_from_list(path) + return self.create_dataset_from_list(path) label_set = preprocess_util.get_label_set_from_dir(path) data = [] for l_name in list(label_set.keys()): diff --git a/tools/check_doc_approval.py b/tools/check_doc_approval.py deleted file mode 100644 index 44fdf58b49a1715696e8c28746282c38fb3c7763..0000000000000000000000000000000000000000 --- a/tools/check_doc_approval.py +++ /dev/null @@ -1,85 +0,0 @@ -# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import os -import sys -import ast -import hashlib -import importlib -import paddle.fluid - -files = [ - "paddle.fluid", "paddle.fluid.average", "paddle.fluid.backward", - "paddle.fluid.clip", "paddle.fluid.data_feeder", "paddle.fluid.executor", - "paddle.fluid.initializer", "paddle.fluid.io", "paddle.fluid.layers", - "paddle.fluid.metrics", "paddle.fluid.nets", "paddle.fluid.optimizer", - "paddle.fluid.profiler", "paddle.fluid.recordio_writer", - "paddle.fluid.regularizer", "paddle.fluid.transpiler" -] - - -def md5(doc): - hash = hashlib.md5() - hash.update(str(doc)) - return hash.hexdigest() - - -def get_module(): - for fi in files: - fi_lib = importlib.import_module(fi) - doc_function = getattr(fi_lib, "__all__") - for api in doc_function: - api_name = fi + "." + api - try: - doc_module = getattr(eval(api_name), "__doc__") - except: - pass - doc_md5_code = md5(doc_module) - doc_dict[api_name] = doc_md5_code - - -def doc_md5_dict(doc_md5_path): - with open(doc_md5_path, "rb") as f: - doc_md5 = f.read() - doc_md5_dict = ast.literal_eval(doc_md5) - return doc_md5_dict - - -def check_doc_md5(): - for k, v in doc_dict.items(): - try: - if doc_ci_dict[k] != v: - return doc_dict - except: - return doc_dict - return True - - -if __name__ == "__main__": - doc_dict = {} - doc_ci_dict = {} - doc_md5_file = "/root/.cache/doc_md5.txt" - if not os.path.exists(doc_md5_file): - os.mknod(doc_md5_file) - else: - doc_ci_dict = doc_md5_dict(doc_md5_file) - get_module() - if not os.path.getsize(doc_md5_file): - with open(doc_md5_file, 'w') as f: - f.write(str(doc_dict)) - check_dic = True - print(check_dic) - else: - check_dic = check_doc_md5() - print(check_dic) diff --git a/tools/codestyle/cpplint_pre_commit.hook b/tools/codestyle/cpplint_pre_commit.hook index aa14d3a2a12208eda11e82d88bc582eb3d2f5893..658008d852123b6eab06d1f13d61ba896e7e9c98 100755 --- a/tools/codestyle/cpplint_pre_commit.hook +++ b/tools/codestyle/cpplint_pre_commit.hook @@ -1,10 +1,22 @@ #!/bin/bash TOTAL_ERRORS=0 - +if [[ ! $TRAVIS_BRANCH ]]; then + # install cpplint on local machine. + if [[ ! $(which cpplint) ]]; then + pip install cpplint + fi + # diff files on local machine. + files=$(git diff --cached --name-status | awk '$1 != "D" {print $2}') +else + # diff files between PR and latest commit on Travis CI. + branch_ref=$(git rev-parse "$TRAVIS_BRANCH") + head_ref=$(git rev-parse HEAD) + files=$(git diff --name-status $branch_ref $head_ref | awk '$1 != "D" {print $2}') +fi # The trick to remove deleted files: https://stackoverflow.com/a/2413151 -for file in $(git diff --cached --name-status | awk '$1 != "D" {print $2}'); do - if [[ $file =~ ^(paddle/legacy/api/.*|paddle/legacy/capi/.*|paddle/contrib/.*|paddle/legacy/cuda/.*|paddle/legacy/function/.*|paddle/legacy/gserver/.*|paddle/legacy/math/.*|paddle/legacy/optimizer/.*|paddle/legacy/parameter/.*|paddle/legacy/pserver/.*|paddle/legacy/trainer/.*|paddle/legacy/utils/.*|paddle/testing/TestUtil.*|patches/grpc/.*) ]]; then +for file in $files; do + if [[ $file =~ ^(patches/grpc/.*) ]]; then continue; else cpplint --filter=-readability/fn_size $file; @@ -13,4 +25,3 @@ for file in $(git diff --cached --name-status | awk '$1 != "D" {print $2}'); do done exit $TOTAL_ERRORS - diff --git a/tools/diff_api.py b/tools/diff_api.py index 97c739ed2a5627ad9fd326f206976a4579dc26a3..ec51711d68a155dabdf3125d43fc35bab0b0c944 100644 --- a/tools/diff_api.py +++ b/tools/diff_api.py @@ -26,4 +26,10 @@ for each_diff in result: print(each_diff) if error: + print( + '''If you modify/add/delete the API files, including code and comment, please follow these steps in order to pass the CI: + 1. cd ${paddle_path}, compile paddle; + 2. pip install build/python/dist/(build whl package); + 3. run "python tools/print_signatures.py paddle.fluid, paddle.reader > paddle/fluid/API.spec"''' + ) sys.exit(1) diff --git a/tools/print_signatures.py b/tools/print_signatures.py index 7e61dde0a446cf5bfe656105ffd2472f03576f05..c56f30f724ca9f183d6c5cac427411b7711739a4 100644 --- a/tools/print_signatures.py +++ b/tools/print_signatures.py @@ -24,12 +24,19 @@ import inspect import collections import sys import pydoc +import hashlib member_dict = collections.OrderedDict() experimental_namespace = {"paddle.fluid.imperative"} +def md5(doc): + hash = hashlib.md5() + hash.update(str(doc).encode('utf-8')) + return hash.hexdigest() + + def visit_member(parent_name, member): cur_name = ".".join([parent_name, member.__name__]) if inspect.isclass(member): @@ -39,7 +46,10 @@ def visit_member(parent_name, member): visit_member(cur_name, value) elif callable(member): try: - member_dict[cur_name] = inspect.getargspec(member) + doc = ('document', md5(member.__doc__)) + args = inspect.getargspec(member) + all = (args, doc) + member_dict[cur_name] = all except TypeError: # special for PyBind method member_dict[cur_name] = " ".join([ line.strip() for line in pydoc.render_doc(member).split('\n')