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Paddle
“50da611b9f9703b237eb7cb613cf38cafdbb0078”上不存在“examples/Cpp/fit_a_line/local_train.py”
未验证 提交 431eab64 编写于 作者: X xiaoting 提交者： GitHub
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Merge branch 'develop' into yolov3_loss

上级 9b9e5e60 5d45eb06
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Showing with 3015 addition and 935 deletion
CMakeLists.txt
浏览文件 @ 431eab64
......@@ -47,33 +47,34 @@ find_package(Threads REQUIRED)
include(simd)
################################ Configurations #######################################
################################ Exposed Configurations #######################################
option(WITH_GPU "Compile PaddlePaddle with NVIDIA GPU" ${CUDA_FOUND})
option(WITH_AMD_GPU "Compile PaddlePaddle with AMD GPU" OFF)
option(WITH_DSO "Compile PaddlePaddle with dynamic linked CUDA" ON)
option(WITH_AVX "Compile PaddlePaddle with AVX intrinsics" ${AVX_FOUND})
option(WITH_PYTHON "Compile PaddlePaddle with python interpreter" ON)
option(WITH_TESTING "Compile PaddlePaddle with unit testing" OFF)
option(WITH_MKL "Compile PaddlePaddle with MKL support." ${AVX_FOUND})
option(WITH_SYSTEM_BLAS "Use system blas library" OFF)
option(WITH_DISTRIBUTE "Compile with distributed support" OFF)
option(WITH_BRPC_RDMA "Use brpc rdma as the rpc protocal" OFF)
option(ON_INFER "Turn on inference optimization." OFF)
option(WITH_ANAKIN "Compile with Anakin library" OFF)
################################ Internal Configurations #######################################
option(WITH_AMD_GPU "Compile PaddlePaddle with AMD GPU" OFF)
option(WITH_NGRAPH "Compile PaddlePaddle with nGraph support." OFF)
option(WITH_DSO "Compile PaddlePaddle with dynamic linked CUDA" ON)
option(WITH_TESTING "Compile PaddlePaddle with unit testing" OFF)
option(WITH_PYTHON "Compile PaddlePaddle with python interpreter" ON)
option(WITH_PROFILER "Compile PaddlePaddle with GPU profiler and gperftools" OFF)
option(WITH_JEMALLOC "Compile PaddlePaddle with jemalloc" OFF)
option(WITH_COVERAGE "Compile PaddlePaddle with code coverage" OFF)
option(COVERALLS_UPLOAD "Package code coverage data to coveralls" OFF)
option(WITH_DISTRIBUTE "Compile with distributed support" OFF)
option(WITH_PSLIB "Compile with pslib support" OFF)
option(WITH_CONTRIB "Compile the third-party contributation" OFF)
option(REPLACE_ENFORCE_GLOG "Replace PADDLE_ENFORCE with glog/CHECK for better debug." OFF)
# TODO(Superjomn) Remove WITH_ANAKIN option if not needed latter.
option(WITH_ANAKIN "Compile with Anakin library" OFF)
option(ANAKIN_BUILD_FAT_BIN "Build anakin cuda fat-bin lib for all device plantform, ignored when WITH_ANAKIN=OFF" OFF)
option(ANAKIN_BUILD_CROSS_PLANTFORM "Build anakin lib for any nvidia device plantform. ignored when WITH_ANAKIN=OFF" ON)
option(WITH_GRPC "Use grpc as the default rpc framework" ${WITH_DISTRIBUTE})
option(WITH_BRPC_RDMA "Use brpc rdma as the rpc protocal" OFF)
option(ON_INFER "Turn on inference optimization." OFF)
option(WITH_INFERENCE_API_TEST "Test fluid inference C++ high-level api interface" OFF)
option(WITH_HIGH_LEVEL_API_TEST "Test fluid python high-level api interface" OFF)
option(WITH_SYSTEM_BLAS "Use system blas library" OFF)
option(PY_VERSION "Compile PaddlePaddle with python3 support" ${PY_VERSION})
option(WITH_FAST_MATH "Make use of fast math library, might affect the precision to some extent" ON)
......
paddle/fluid/API.spec
浏览文件 @ 431eab64
......@@ -241,6 +241,7 @@ paddle.fluid.layers.tree_conv (ArgSpec(args=['nodes_vector', 'edge_set', 'output
paddle.fluid.layers.npair_loss (ArgSpec(args=['anchor', 'positive', 'labels', 'l2_reg'], varargs=None, keywords=None, defaults=(0.002,)), ('document', '46994d10276dd4cb803b4062b5d14329'))
paddle.fluid.layers.pixel_shuffle (ArgSpec(args=['x', 'upscale_factor'], varargs=None, keywords=None, defaults=None), ('document', '731b21c62a4add60a33bd76d802ffc5c'))
paddle.fluid.layers.fsp_matrix (ArgSpec(args=['x', 'y'], varargs=None, keywords=None, defaults=None), ('document', 'b76ccca3735bea4a58a0dbf0d77c5393'))
paddle.fluid.layers.continuous_value_model (ArgSpec(args=['input', 'cvm', 'use_cvm'], varargs=None, keywords=None, defaults=(True,)), ('document', 'a07a44c2bacdcd09c1f5f35a96a0514e'))
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'))
......@@ -276,6 +277,7 @@ paddle.fluid.layers.has_nan (ArgSpec(args=['x'], varargs=None, keywords=None, de
paddle.fluid.layers.isfinite (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '0a437011c3906079fd8947ed3e52d292'))
paddle.fluid.layers.range (ArgSpec(args=['start', 'end', 'step', 'dtype'], varargs=None, keywords=None, defaults=None), ('document', '2ec937ede953ded2fdff2675883900bb'))
paddle.fluid.layers.linspace (ArgSpec(args=['start', 'stop', 'num', 'dtype'], varargs=None, keywords=None, defaults=None), ('document', '495e21e9a848c2d075a102802fc67756'))
paddle.fluid.layers.zeros_like (ArgSpec(args=['x', 'out'], varargs=None, keywords=None, defaults=(None,)), ('document', 'c7e4cfffc93ae89c8f6f53b6d650f923'))
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'))
......@@ -285,7 +287,11 @@ paddle.fluid.layers.increment (ArgSpec(args=['x', 'value', 'in_place'], varargs=
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=None, defaults=(None, None)), ('document', '067bbc799c66289ca8b8924c26b6673f'))
paddle.fluid.layers.less_equal (ArgSpec(args=['x', 'y', 'cond'], varargs=None, keywords=None, defaults=(None,)), ('document', 'd6b173ae1a149e0bdfe7b8bf69285957'))
paddle.fluid.layers.greater_than (ArgSpec(args=['x', 'y', 'cond'], varargs=None, keywords=None, defaults=(None,)), ('document', '2c9bd414caa6c615539018d27001b44c'))
paddle.fluid.layers.greater_equal (ArgSpec(args=['x', 'y', 'cond'], varargs=None, keywords=None, defaults=(None,)), ('document', '62c667d24e7b07e166b47a53b61b2ff4'))
paddle.fluid.layers.equal (ArgSpec(args=['x', 'y', 'cond'], varargs=None, keywords=None, defaults=(None,)), ('document', '80c29b1dc64718f0116de90d1ac88a77'))
paddle.fluid.layers.not_equal (ArgSpec(args=['x', 'y', 'cond'], varargs=None, keywords=None, defaults=(None,)), ('document', '56148fb1024687a08e96af79bdc5c929'))
paddle.fluid.layers.array_read (ArgSpec(args=['array', 'i'], varargs=None, keywords=None, defaults=None), ('document', 'dd68bead34dfbaf6b0a163fc1cc3c385'))
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'))
......@@ -301,12 +307,12 @@ paddle.fluid.layers.DynamicRNN.static_input (ArgSpec(args=['self', 'x'], varargs
paddle.fluid.layers.DynamicRNN.step_input (ArgSpec(args=['self', 'x', 'level'], varargs=None, keywords=None, defaults=(0,)), ('document', '7568c5ac7622a10288d3307a94134655'))
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.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', '72530f299d6451a567cf4a12dc3fb1ff'))
paddle.fluid.layers.StaticRNN.output (ArgSpec(args=['self'], varargs='outputs', keywords=None, defaults=None), ('document', 'df6ceab6e6c9bd31e97914d7e7538137'))
paddle.fluid.layers.StaticRNN.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6d3e0a5d9aa519a9773a36e1620ea9b7'))
paddle.fluid.layers.StaticRNN.step_input (ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None), ('document', '903387ec11f3d0bf46821d31a68cffa5'))
paddle.fluid.layers.StaticRNN.step_output (ArgSpec(args=['self', 'o'], varargs=None, keywords=None, defaults=None), ('document', '252890d4c3199a7623ab8667e13fd837'))
paddle.fluid.layers.StaticRNN.update_memory (ArgSpec(args=['self', 'mem', 'var'], varargs=None, keywords=None, defaults=None), ('document', '7a0000520f179f35239956a5ba55119f'))
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=None, defaults=(None,)), ('document', 'bbe578dbb49ad13e15b014e98c22b519'))
......@@ -318,6 +324,7 @@ paddle.fluid.layers.atan (ArgSpec(args=['x', 'name'], varargs=None, keywords=Non
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', 'e9e27491c39ac74d0b1ffe506aec0ebb'))
paddle.fluid.layers.rsqrt (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'c445467ebe58b3c0d7f0bba7795b6f56'))
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'))
......@@ -330,13 +337,13 @@ paddle.fluid.layers.reciprocal (ArgSpec(args=['x', 'name'], varargs=None, keywor
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.uniform_random (ArgSpec(args=['shape', 'dtype', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', -1.0, 1.0, 0)), ('document', 'a8c4e972b7d6742c838a37abf407ed9a'))
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.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', 'fe9afaee481dd09f28866df22756466f'))
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'))
......@@ -351,7 +358,7 @@ paddle.fluid.layers.generate_mask_labels (ArgSpec(args=['im_info', 'gt_classes',
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', 'gtscore', 'use_label_smooth', 'name'], varargs=None, keywords=None, defaults=(None, True, None)), ('document', 'bbed7a8e63324cb76873ddd32b2f84ef'))
paddle.fluid.layers.yolov3_loss (ArgSpec(args=['x', 'gt_box', 'gt_label', 'anchors', 'anchor_mask', 'class_num', 'ignore_thresh', 'downsample_ratio', 'gt_score', 'use_label_smooth', 'name'], varargs=None, keywords=None, defaults=(None, True, None)), ('document', '4d170807a13d33925d1049d2892832bf'))
paddle.fluid.layers.yolo_box (ArgSpec(args=['x', 'img_size', 'anchors', 'class_num', 'conf_thresh', 'downsample_ratio', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '5566169a5ab993d177792c023c7fb340'))
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/framework/details/async_ssa_graph_executor.cc
浏览文件 @ 431eab64
......@@ -64,9 +64,12 @@ void ProcessGraph(std::vector<ir::Graph *> graphs, Scope *scope) {
node->Op()->GetNullableAttr("epmap"));
auto height_section = boost::get<std::vector<int64_t>>(
node->Op()->GetNullableAttr("sections"));
auto trainer_id =
boost::get<int>(node->Op()->GetNullableAttr("trainer_id"));
send_varname_to_ctx[send_var_name] =
operators::distributed::RpcContext(send_var_name, send_varnames,
epmap, height_section);
epmap, height_section,
trainer_id);
VLOG(3) << "find and init an send op: "
<< send_varname_to_ctx[send_var_name];
} else if (node->Name() == "recv") {
......@@ -75,9 +78,11 @@ void ProcessGraph(std::vector<ir::Graph *> graphs, Scope *scope) {
node->Op()->GetNullableAttr("recv_varnames"));
auto epmap = boost::get<std::vector<std::string>>(
node->Op()->GetNullableAttr("epmap"));
auto trainer_id =
boost::get<int>(node->Op()->GetNullableAttr("trainer_id"));
recv_varname_to_ctx[recv_var_name] =
operators::distributed::RpcContext(recv_var_name, recv_varnames,
epmap, {});
epmap, {}, trainer_id);
nodes_to_delete.push_back(node);
VLOG(3) << "find and remove an recv op: "
<< recv_varname_to_ctx[recv_var_name];
......
paddle/fluid/framework/details/build_strategy.cc
浏览文件 @ 431eab64
......@@ -101,8 +101,6 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
"mode.";
strategy_.fuse_all_optimizer_ops_ = false;
} else {
VLOG(10) << "Add alloc_continuous_space_for_grad_pass";
AppendPass("alloc_continuous_space_for_grad_pass");
// NOTE: fuse_all_xx_ops will count the number of xx operator first,
// if the number is zero, fuse_all_reduce_ops will do nothing.
// Currently, only one type of optimization algorithm can be fused.
......
paddle/fluid/framework/details/fuse_adam_op_pass.cc
浏览文件 @ 431eab64
......@@ -24,7 +24,7 @@ namespace details {
const std::string FuseAdamOpPass::GetOpType() const { return "adam"; }
const std::vector<std::string> FuseAdamOpPass::GetAuxiliaryVarNames() const {
return {"Param", "Moment1", "Moment2", "Beta1Pow", "Beta2Pow"};
return {"Moment1", "Moment2", "Beta1Pow", "Beta2Pow"};
}
void FuseAdamOpPass::FuseOptimizerOps(
......@@ -77,16 +77,16 @@ void FuseAdamOpPass::FuseAdamOps(
VLOG(10) << "Insert adam to graph ";
OpDesc adam_desc(adam_ops[0]->Op()->Block());
adam_desc.SetType("adam");
adam_desc.SetInput("Param", {fused_vars_name.at("Param")});
adam_desc.SetInput("Grad", {fused_vars_name.at("Grad")});
adam_desc.SetInput(kParam, {fused_vars_name.at(kParam)});
adam_desc.SetInput(kGrad, {fused_vars_name.at(kGrad)});
adam_desc.SetInput("Moment1", {fused_vars_name.at("Moment1")});
adam_desc.SetInput("Moment2", {fused_vars_name.at("Moment2")});
// TODO(zcd): The LearningRate, Beta1Pow, Beta2Pow should be equal.
adam_desc.SetInput("LearningRate", adam_ops[0]->Op()->Input("LearningRate"));
adam_desc.SetInput(kLearningRate, adam_ops[0]->Op()->Input(kLearningRate));
adam_desc.SetInput("Beta1Pow", adam_ops[0]->Op()->Input("Beta1Pow"));
adam_desc.SetInput("Beta2Pow", adam_ops[0]->Op()->Input("Beta2Pow"));
adam_desc.SetOutput("ParamOut", {fused_vars_name.at("Param")});
adam_desc.SetOutput("ParamOut", {fused_vars_name.at(kParam)});
adam_desc.SetOutput("Moment1Out", {fused_vars_name.at("Moment1")});
adam_desc.SetOutput("Moment2Out", {fused_vars_name.at("Moment2")});
adam_desc.SetAttr("beta1", beta1);
......
paddle/fluid/framework/details/fuse_optimizer_op_pass.cc
浏览文件 @ 431eab64
......@@ -29,7 +29,9 @@ void FuseOptimizerOpPass::ApplyImpl(ir::Graph *graph) const {
auto &local_scopes = Get<const std::vector<Scope *>>(kLocalScopes);
const std::string fuse_op_type = GetOpType();
const std::vector<std::string> aux_var_names = GetAuxiliaryVarNames();
std::vector<std::string> aux_var_names = GetAuxiliaryVarNames();
aux_var_names.emplace_back(kParam);
aux_var_names.emplace_back(kGrad);
// Step 1: Get the specified op and auxiliary variables.
std::vector<ir::Node *> topo_nodes = ir::TopologySortOperations(result);
......@@ -61,7 +63,7 @@ void FuseOptimizerOpPass::ApplyImpl(ir::Graph *graph) const {
result.Set(kFusedVars, new FusedVars);
}
std::unordered_map<std::string, std::string> fused_vars_name;
fused_vars_name.reserve(aux_var_names.size() + 1);
fused_vars_name.reserve(aux_var_names.size());
auto &fused_var_set = result.Get<FusedVars>(kFusedVars);
const std::string prefix(kFusedVarNamePrefix);
// NOTE: the fused_var_name should be unique.
......@@ -75,39 +77,103 @@ void FuseOptimizerOpPass::ApplyImpl(ir::Graph *graph) const {
}
// Step 3: Get the fused Gradient's name
auto &params_grads = result.Get<ParamsAndGrads>(kParamsAndGrads);
if (!result.Has(kFusedGrads)) {
PADDLE_THROW(
"The alloc_continuous_space_for_grad_pass should be called before this "
"pass.");
}
auto &fused_grad = result.Get<FusedGrads>(kFusedGrads);
auto &fused_vars = result.Get<FusedVars>(kFusedVars);
auto iter = std::find(fused_vars.begin(), fused_vars.end(), fused_grad);
PADDLE_ENFORCE(iter != fused_vars.end(), "Not find the fused_grad.");
fused_vars_name.emplace("Grad", fused_grad);
// Step 4: Sort the parameters and auxiliary variables according
// to parameters' name to make variables' name correspond correctly.
PADDLE_ENFORCE(result.Has(kParamsAndGrads), "Does't find kParamsAndGrads.");
PADDLE_ENFORCE_EQ(params_grads.size(), aux_var_set.begin()->second.size(),
"The size of params_grads and aux_var_set are not equal.");
SortParametersAndAuxVars(params_grads, &aux_var_set, &opt_ops);
// Step 5: Alloc continuous space for Parameters and AuxiliaryVar(e.g.
bool grad_fused = false;
if (result.Has(kParamsAndGrads)) {
auto &params_grads = result.Get<ParamsAndGrads>(kParamsAndGrads);
PADDLE_ENFORCE_EQ(
params_grads.size(), aux_var_set.at(kGrad).size(),
"The number of gradients and optimizer ops is not equal.");
std::unordered_set<std::string> opt_grad_set(aux_var_set.at(kGrad).begin(),
aux_var_set.at(kGrad).end());
size_t same_grad_num = 0;
for (auto &p_g : params_grads) {
if (opt_grad_set.count(p_g.second)) {
++same_grad_num;
}
}
// NOTE(zcd): the gradient of kParamsAndGrads may be different with the
// kGrad.
if (same_grad_num == aux_var_set.at(kGrad).size()) {
if (!result.Has(kFusedGrads)) {
PADDLE_THROW(
"The alloc_continuous_space_for_grad_pass should be called before "
"this pass.");
}
auto &fused_grad = result.Get<FusedGrads>(kFusedGrads);
auto &fused_vars = result.Get<FusedVars>(kFusedVars);
auto iter = std::find(fused_vars.begin(), fused_vars.end(), fused_grad);
PADDLE_ENFORCE(iter != fused_vars.end(), "Not find the fused_grad.");
fused_vars_name[kGrad] = fused_grad;
// Sort the parameters and auxiliary variables according
// to parameters' name to make variables' name correspond correctly.
SortParametersAndAuxVars(params_grads, &aux_var_set, &opt_ops);
grad_fused = true;
}
}
// Step 4: Alloc continuous space for Parameters and AuxiliaryVar(e.g.
// Moment1, Moment2, Beta1Pow, Beta2Pow) of all the optimizer ops separately.
aux_var_names.pop_back();
if (!grad_fused) {
InitFusedGradsAndAllocSpaceForGrads(
places, local_scopes, aux_var_set.at(kParam), aux_var_set.at(kGrad),
fused_vars_name.at(kGrad), &result);
}
InitFusedVarsAndAllocSpaceForVars(places, local_scopes, aux_var_names,
aux_var_set, fused_vars_name);
// Step 6: Fuse optimizer Ops and Scale Ops
// Step 5: Fuse optimizer Ops and Scale Ops
FuseOptimizerOps(aux_var_set, fused_vars_name, opt_ops, &result);
// Step 7: Remove optimizer Ops
// Step 6: Remove optimizer Ops
for (auto &opt_op : opt_ops) {
graph->RemoveNode(opt_op);
}
}
void FuseOptimizerOpPass::InitFusedGradsAndAllocSpaceForGrads(
const std::vector<platform::Place> &places,
const std::vector<Scope *> &local_scopes,
const std::vector<std::string> &params,
const std::vector<std::string> &grads, const std::string &fused_grad_name,
ir::Graph *result) const {
// Get Var Nodes
std::unordered_map<std::string, ir::Node *> vars;
for (ir::Node *node : result->Nodes()) {
if (node->IsVar() && node->Var()) {
// Note: The graph may have the same name node. For example, parameter
// is the input of operator and it also is the output of optimizer;
vars.emplace(node->Var()->Name(), node);
}
}
// Init Grads
for (auto it = local_scopes.rbegin(); it != local_scopes.rend(); ++it) {
auto &scope = *it;
VLOG(10) << "Init " << fused_grad_name;
PADDLE_ENFORCE(scope->FindVar(fused_grad_name) == nullptr,
"%s has existed in scope.", fused_grad_name);
scope->Var(fused_grad_name)->GetMutable<LoDTensor>();
for (auto &grad_var_name : grads) {
auto iter = vars.find(grad_var_name);
PADDLE_ENFORCE(iter != vars.end());
PADDLE_ENFORCE_NOT_NULL(iter->second->Var());
PADDLE_ENFORCE_EQ(iter->second->Var()->GetType(),
proto::VarType::LOD_TENSOR);
scope->Var(grad_var_name)->GetMutable<LoDTensor>();
}
}
// Define Ops
ProgramDesc program_desc;
auto *global_block = program_desc.MutableBlock(0);
AppendAllocContinuousSpace(params, grads, fused_grad_name, global_block,
false, false);
// Run Ops
RunInitOps(places, local_scopes, *global_block);
}
void FuseOptimizerOpPass::InitFusedVarsAndAllocSpaceForVars(
const std::vector<platform::Place> &places,
const std::vector<Scope *> &local_scopes,
......@@ -115,37 +181,49 @@ void FuseOptimizerOpPass::InitFusedVarsAndAllocSpaceForVars(
const std::unordered_map<std::string, std::vector<std::string>>
&aux_var_set,
const std::unordered_map<std::string, std::string> &fused_vars_name) const {
VLOG(10) << "Init FusedVars.";
// Alloc parameters and auxiliary vars in the respective scope.
size_t idx = local_scopes.size();
for (auto iter = local_scopes.rbegin(); iter != local_scopes.rend();
++iter, --idx) {
auto &scope = *iter;
for (auto &var_name : aux_var_names) {
auto fused_var_name = fused_vars_name.at(var_name);
VLOG(10) << "Init " << fused_var_name;
PADDLE_ENFORCE(scope->FindVar(fused_var_name) == nullptr,
"%s has exist in scope[%d]", fused_var_name, idx);
scope->Var(fused_var_name)->GetMutable<LoDTensor>();
}
// Init Vars
for (auto &var_name : aux_var_names) {
auto &fused_var_name = fused_vars_name.at(var_name);
InitVars(local_scopes, fused_var_name);
}
// Define Ops
ProgramDesc program_desc;
auto *global_block = program_desc.MutableBlock(0);
for (auto &var_name : aux_var_names) {
AppendAllocContinuousSpace(aux_var_set.at(var_name),
fused_vars_name.at(var_name), true,
global_block);
AppendAllocContinuousSpace(
aux_var_set.at(var_name), aux_var_set.at(var_name),
fused_vars_name.at(var_name), global_block, true);
}
// Run Ops
RunInitOps(places, local_scopes, *global_block);
}
void FuseOptimizerOpPass::RunInitOps(const std::vector<platform::Place> &places,
const std::vector<Scope *> &local_scopes,
const BlockDesc &global_block) const {
for (size_t i = 0; i < local_scopes.size(); ++i) {
for (auto &op_desc : global_block->AllOps()) {
for (auto &op_desc : global_block.AllOps()) {
auto op = OpRegistry::CreateOp(*op_desc);
op->Run(*local_scopes[i], places[i]);
}
}
}
void FuseOptimizerOpPass::InitVars(const std::vector<Scope *> &local_scopes,
const std::string &fused_var_name) const {
VLOG(10) << "Init FusedVars.";
// Alloc parameters and auxiliary vars in the respective scope.
size_t idx = local_scopes.size();
for (auto iter = local_scopes.rbegin(); iter != local_scopes.rend();
++iter, --idx) {
auto &scope = *iter;
VLOG(10) << "Init " << fused_var_name;
PADDLE_ENFORCE(scope->FindVar(fused_var_name) == nullptr,
"%s has exist in scope[%d]", fused_var_name, idx);
scope->Var(fused_var_name)->GetMutable<LoDTensor>();
}
}
void FuseOptimizerOpPass::SortParametersAndAuxVars(
const std::vector<std::pair<std::string, std::string>> &params_grads,
std::unordered_map<std::string, std::vector<std::string>> *aux_vars_set,
......@@ -203,15 +281,16 @@ void FuseOptimizerOpPass::GetSpecifiedOpsAndVars(
}
void FuseOptimizerOpPass::AppendAllocContinuousSpace(
const std::vector<std::string> &args, const std::string &out_arg,
bool copy_data, BlockDesc *global_block) const {
const std::vector<std::string> &in_args,
const std::vector<std::string> &out_args, const std::string &fused_out_arg,
BlockDesc *global_block, bool copy_data, bool check_name) const {
auto op_desc = global_block->AppendOp();
op_desc->SetType("alloc_continuous_space");
op_desc->SetInput("Input", args);
op_desc->SetOutput("Output", args);
op_desc->SetOutput("FusedOutput", {out_arg});
op_desc->SetInput("Input", in_args);
op_desc->SetOutput("Output", out_args);
op_desc->SetOutput("FusedOutput", {fused_out_arg});
op_desc->SetAttr("copy_data", copy_data);
op_desc->SetAttr("check_name", true);
op_desc->SetAttr("check_name", check_name);
}
void FuseOptimizerOpPass::InserInputAndOutputForOptOps(
......
paddle/fluid/framework/details/fuse_optimizer_op_pass.h
浏览文件 @ 431eab64
......@@ -27,6 +27,10 @@ namespace paddle {
namespace framework {
namespace details {
constexpr char kGrad[] = "Grad";
constexpr char kParam[] = "Param";
constexpr char kLearningRate[] = "LearningRate";
class FuseOptimizerOpPass : public ir::Pass {
protected:
void ApplyImpl(ir::Graph *graph) const override;
......@@ -56,9 +60,18 @@ class FuseOptimizerOpPass : public ir::Pass {
std::unordered_map<std::string, std::vector<std::string>> *aux_args_name)
const;
void AppendAllocContinuousSpace(const std::vector<std::string> &args,
const std::string &out_arg, bool copy_data,
BlockDesc *global_block) const;
void AppendAllocContinuousSpace(const std::vector<std::string> &in_args,
const std::vector<std::string> &out_args,
const std::string &fused_out_arg,
BlockDesc *global_block, bool copy_data,
bool check_name = true) const;
void InitFusedGradsAndAllocSpaceForGrads(
const std::vector<platform::Place> &places,
const std::vector<Scope *> &local_scopes,
const std::vector<std::string> &params,
const std::vector<std::string> &grads, const std::string &fused_grad_name,
ir::Graph *result) const;
void InitFusedVarsAndAllocSpaceForVars(
const std::vector<platform::Place> &places,
......@@ -68,6 +81,13 @@ class FuseOptimizerOpPass : public ir::Pass {
&aux_var_set,
const std::unordered_map<std::string, std::string> &fused_vars_name)
const;
void RunInitOps(const std::vector<platform::Place> &places,
const std::vector<Scope *> &local_scopes,
const BlockDesc &global_block) const;
void InitVars(const std::vector<Scope *> &local_scopes,
const std::string &fused_var_name) const;
};
} // namespace details
......
paddle/fluid/framework/details/fuse_sgd_op_pass.cc
浏览文件 @ 431eab64
......@@ -24,7 +24,7 @@ namespace details {
const std::string FuseSgdOpPass::GetOpType() const { return "sgd"; }
const std::vector<std::string> FuseSgdOpPass::GetAuxiliaryVarNames() const {
return {"Param"};
return {};
}
void FuseSgdOpPass::FuseOptimizerOps(
......@@ -50,12 +50,12 @@ void FuseSgdOpPass::FuseSgdOps(
// Add fused scale
OpDesc Sgd_desc(sgd_ops[0]->Op()->Block());
Sgd_desc.SetType("sgd");
Sgd_desc.SetInput("Param", {fused_vars_name.at("Param")});
Sgd_desc.SetInput("Grad", {fused_vars_name.at("Grad")});
Sgd_desc.SetOutput("ParamOut", {fused_vars_name.at("Param")});
Sgd_desc.SetInput(kParam, {fused_vars_name.at(kParam)});
Sgd_desc.SetInput(kGrad, {fused_vars_name.at(kGrad)});
Sgd_desc.SetOutput("ParamOut", {fused_vars_name.at(kParam)});
// TODO(zcd): The LearningRate, Beta1Pow, Beta2Pow should be equal.
Sgd_desc.SetInput("LearningRate", sgd_ops[0]->Op()->Input("LearningRate"));
Sgd_desc.SetInput(kLearningRate, sgd_ops[0]->Op()->Input(kLearningRate));
// NOTE: multi_devices_pass requires that every op should have a role.
Sgd_desc.SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(), op_role);
......
paddle/fluid/framework/details/parallel_ssa_graph_executor.cc
浏览文件 @ 431eab64
......@@ -106,7 +106,7 @@ ParallelSSAGraphExecutor::ParallelSSAGraphExecutor(
VLOG(1) << "set num_threads: " << strategy_.num_threads_
<< " to run the operators of the graph on each device.";
for (size_t i = 0; i < places.size(); ++i) {
executors_.emplace_back(new details::ThreadedSSAGraphExecutor(
executors_.emplace_back(new details::FastThreadedSSAGraphExecutor(
strategy_, local_scopes_, {places_[i]}, graphs_.at(i).get()));
}
}
......
paddle/fluid/framework/details/parallel_ssa_graph_executor.h
浏览文件 @ 431eab64
......@@ -14,12 +14,12 @@
#pragma once
#include <memory>
#include <string>
#include <vector>
#include "ThreadPool.h"
#include "paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.h"
#include "paddle/fluid/framework/details/multi_devices_helper.h"
#include "paddle/fluid/framework/details/threaded_ssa_graph_executor.h"
#include "paddle/fluid/framework/ir/graph.h"
namespace paddle {
......@@ -48,7 +48,8 @@ class ParallelSSAGraphExecutor : public SSAGraphExecutor {
std::vector<platform::Place> places_;
std::vector<std::unique_ptr<ir::Graph>> graphs_;
std::vector<std::unique_ptr<details::ThreadedSSAGraphExecutor>> executors_;
std::vector<std::unique_ptr<details::FastThreadedSSAGraphExecutor>>
executors_;
ExceptionHolder exception_holder_;
};
......
paddle/fluid/framework/var_type_inference.h
浏览文件 @ 431eab64
......@@ -45,12 +45,16 @@ class InferVarTypeContext {
virtual bool HasInput(const std::string& name) const {
PADDLE_ENFORCE_NOT_NULL(op_);
return op_->Inputs().count(name) > 0;
auto& inputs = op_->Inputs();
auto input = inputs.find(name);
return input != inputs.end() && !input->second.empty();
}
virtual bool HasOutput(const std::string& name) const {
PADDLE_ENFORCE_NOT_NULL(op_);
return op_->Outputs().count(name) > 0;
auto& outputs = op_->Outputs();
auto output = outputs.find(name);
return output != outputs.end() && !output->second.empty();
}
virtual const std::vector<std::string>& Input(const std::string& name) const {
......
paddle/fluid/inference/api/analysis_predictor.cc
浏览文件 @ 431eab64
......@@ -832,6 +832,45 @@ std::string AnalysisPredictor::GetSerializedProgram() const {
return inference_program_->Proto()->SerializeAsString();
}
// Add SaveOptimModel
void AnalysisPredictor::SaveOptimModel(const std::string &dir) {
// save model
std::string model_name = dir + "/model";
std::ofstream outfile;
outfile.open(model_name, std::ios::out | std::ios::binary);
std::string inference_prog_desc = GetSerializedProgram();
outfile << inference_prog_desc;
// save params
framework::ProgramDesc save_program;
auto *save_block = save_program.MutableBlock(0);
const framework::ProgramDesc &main_program = program();
const framework::BlockDesc &global_block = main_program.Block(0);
std::vector<std::string> save_var_list;
for (framework::VarDesc *var : global_block.AllVars()) {
if (IsPersistable(var)) {
framework::VarDesc *new_var = save_block->Var(var->Name());
new_var->SetShape(var->GetShape());
new_var->SetDataType(var->GetDataType());
new_var->SetType(var->GetType());
new_var->SetLoDLevel(var->GetLoDLevel());
new_var->SetPersistable(true);
save_var_list.push_back(new_var->Name());
}
}
std::sort(save_var_list.begin(), save_var_list.end());
auto *op = save_block->AppendOp();
op->SetType("save_combine");
op->SetInput("X", save_var_list);
op->SetAttr("file_path", dir + "/params");
op->CheckAttrs();
platform::CPUPlace place;
framework::Executor exe(place);
exe.Run(save_program, scope(), 0, true, true);
}
template <>
std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<AnalysisConfig>(
const AnalysisConfig &config) {
......
paddle/fluid/inference/api/analysis_predictor.h
浏览文件 @ 431eab64
......@@ -86,6 +86,10 @@ class AnalysisPredictor : public PaddlePredictor {
bool MkldnnQuantize();
// save program to model
// save parameters to params
void SaveOptimModel(const std::string &dir);
protected:
// For memory optimization.
bool need_collect_var_shapes_for_memory_optim();
......
paddle/fluid/inference/api/analysis_predictor_tester.cc
浏览文件 @ 431eab64
......@@ -196,6 +196,9 @@ TEST(AnalysisPredictor, Clone) {
}
}
// This function is not released yet, will fail on some machine.
// TODO(Superjomn) Turn on it latter.
/*
TEST(AnalysisPredictor, memory_optim) {
AnalysisConfig config(FLAGS_dirname);
config.DisableGpu();
......@@ -246,6 +249,7 @@ TEST(AnalysisPredictor, memory_optim) {
inference::CompareResult(output, output1);
}
*/
#ifdef PADDLE_WITH_MKLDNN
class MkldnnQuantizerTest : public testing::Test {
......
paddle/fluid/inference/tests/api/analyzer_dam_tester.cc
浏览文件 @ 431eab64
......@@ -170,6 +170,15 @@ void SetConfig(AnalysisConfig *cfg) {
cfg->SwitchIrOptim(true);
}
void SetOptimConfig(AnalysisConfig *cfg) {
std::string optimModelPath =
FLAGS_infer_model.substr(0, FLAGS_infer_model.find_last_of("/")) +
"/saved_optim_model";
cfg->SetModel(optimModelPath + "/model", optimModelPath + "/params");
cfg->SwitchIrOptim(true);
cfg->SwitchSpecifyInputNames();
}
void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
DataRecord data(FLAGS_infer_data, FLAGS_batch_size);
std::vector<PaddleTensor> input_slots;
......@@ -315,5 +324,44 @@ TEST(Analyzer_dam, compare_determine) {
input_slots_all);
}
// Save optim model
TEST(Analyzer_dam, save_optim_model) {
AnalysisConfig cfg;
SetConfig(&cfg);
std::string optimModelPath =
FLAGS_infer_model.substr(0, FLAGS_infer_model.find_last_of("/")) +
"/saved_optim_model";
mkdir(optimModelPath.c_str(), 0777);
auto predictor = CreateTestPredictor(
reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
FLAGS_use_analysis);
(static_cast<AnalysisPredictor *>(predictor.get()))
->SaveOptimModel(optimModelPath);
}
void CompareOptimAndOrig(const PaddlePredictor::Config *orig_config,
const PaddlePredictor::Config *optim_config,
const std::vector<std::vector<PaddleTensor>> &inputs) {
PrintConfig(orig_config, true);
PrintConfig(optim_config, true);
std::vector<std::vector<PaddleTensor>> orig_outputs, optim_outputs;
TestOneThreadPrediction(orig_config, inputs, &orig_outputs, false);
TestOneThreadPrediction(optim_config, inputs, &optim_outputs, false);
CompareResult(orig_outputs.back(), optim_outputs.back());
}
TEST(Analyzer_dam, compare_optim_orig) {
AnalysisConfig orig_cfg;
AnalysisConfig optim_cfg;
SetConfig(&orig_cfg);
SetOptimConfig(&optim_cfg);
std::vector<std::vector<PaddleTensor>> input_slots_all;
SetInput(&input_slots_all);
CompareOptimAndOrig(
reinterpret_cast<const PaddlePredictor::Config *>(&orig_cfg),
reinterpret_cast<const PaddlePredictor::Config *>(&optim_cfg),
input_slots_all);
}
} // namespace inference
} // namespace paddle
paddle/fluid/inference/tests/api/analyzer_resnet50_tester.cc
浏览文件 @ 431eab64
......@@ -32,6 +32,17 @@ void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
SetFakeImageInput(inputs, FLAGS_infer_model);
}
void SetOptimConfig(AnalysisConfig *cfg) {
std::string optimModelPath =
FLAGS_infer_model.substr(0, FLAGS_infer_model.find_last_of("/")) +
"/saved_optim_model";
cfg->SetModel(optimModelPath + "/model", optimModelPath + "/params");
cfg->DisableGpu();
cfg->SwitchIrOptim();
cfg->SwitchSpecifyInputNames();
cfg->SetCpuMathLibraryNumThreads(FLAGS_paddle_num_threads);
}
// Easy for profiling independently.
void profile(bool use_mkldnn = false) {
AnalysisConfig cfg;
......@@ -87,13 +98,51 @@ TEST(Analyzer_resnet50, compare_mkldnn) { compare(true /* use_mkldnn */); }
TEST(Analyzer_resnet50, compare_determine) {
AnalysisConfig cfg;
SetConfig(&cfg);
std::vector<std::vector<PaddleTensor>> input_slots_all;
SetInput(&input_slots_all);
CompareDeterministic(reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
input_slots_all);
}
// Save optim model
TEST(Analyzer_resnet50, save_optim_model) {
AnalysisConfig cfg;
SetConfig(&cfg);
std::string optimModelPath =
FLAGS_infer_model.substr(0, FLAGS_infer_model.find_last_of("/")) +
"/saved_optim_model";
mkdir(optimModelPath.c_str(), 0777);
auto predictor = CreateTestPredictor(
reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
FLAGS_use_analysis);
(static_cast<AnalysisPredictor *>(predictor.get()))
->SaveOptimModel(optimModelPath);
}
void CompareOptimAndOrig(const PaddlePredictor::Config *orig_config,
const PaddlePredictor::Config *optim_config,
const std::vector<std::vector<PaddleTensor>> &inputs) {
PrintConfig(orig_config, true);
PrintConfig(optim_config, true);
std::vector<std::vector<PaddleTensor>> orig_outputs, optim_outputs;
TestOneThreadPrediction(orig_config, inputs, &orig_outputs, false);
TestOneThreadPrediction(optim_config, inputs, &optim_outputs, false);
CompareResult(orig_outputs.back(), optim_outputs.back());
}
TEST(Analyzer_resnet50, compare_optim_orig) {
AnalysisConfig orig_cfg;
AnalysisConfig optim_cfg;
SetConfig(&orig_cfg);
SetOptimConfig(&optim_cfg);
std::vector<std::vector<PaddleTensor>> input_slots_all;
SetInput(&input_slots_all);
CompareOptimAndOrig(
reinterpret_cast<const PaddlePredictor::Config *>(&orig_cfg),
reinterpret_cast<const PaddlePredictor::Config *>(&optim_cfg),
input_slots_all);
}
} // namespace analysis
} // namespace inference
} // namespace paddle
paddle/fluid/inference/tests/api/trt_models_tester.cc
浏览文件 @ 431eab64
......@@ -116,7 +116,7 @@ void compare_continuous_input(std::string model_dir, bool use_tensorrt) {
reinterpret_cast<const PaddlePredictor::Config*>(&analysis_config);
auto native_pred = CreateTestPredictor(config, false);
auto analysis_pred = CreateTestPredictor(config, true);
for (int i = 0; i < 100; i++) {
for (int i = 0; i < 20; i++) {
std::vector<std::vector<PaddleTensor>> inputs_all;
if (!FLAGS_prog_filename.empty() && !FLAGS_param_filename.empty()) {
SetFakeImageInput(&inputs_all, model_dir, true, FLAGS_prog_filename,
......@@ -133,11 +133,13 @@ void compare_continuous_input(std::string model_dir, bool use_tensorrt) {
TEST(TensorRT_mobilenet, compare) {
std::string model_dir = FLAGS_infer_model + "/mobilenet";
compare(model_dir, /* use_tensorrt */ true);
// Open it when need.
// profile(model_dir, /* use_analysis */ true, FLAGS_use_tensorrt);
}
TEST(TensorRT_resnet50, compare) {
TEST(resnet50, compare_continuous_input) {
std::string model_dir = FLAGS_infer_model + "/resnet50";
compare(model_dir, /* use_tensorrt */ true);
compare_continuous_input(model_dir, true);
}
TEST(TensorRT_resnext50, compare) {
......@@ -145,24 +147,6 @@ TEST(TensorRT_resnext50, compare) {
compare(model_dir, /* use_tensorrt */ true);
}
TEST(TensorRT_resnext50, profile) {
std::string model_dir = FLAGS_infer_model + "/resnext50";
// Set FLAGS_record_benchmark to true to record benchmark to file.
// FLAGS_record_benchmark=true;
FLAGS_model_name = "resnext50";
profile(model_dir, /* use_analysis */ true, FLAGS_use_tensorrt);
}
TEST(resnext50, compare_analysis_native) {
std::string model_dir = FLAGS_infer_model + "/resnext50";
compare(model_dir, false /*use tensorrt*/);
}
TEST(TensorRT_mobilenet, analysis) {
std::string model_dir = FLAGS_infer_model + "/" + "mobilenet";
compare(model_dir, false /* use_tensorrt */);
}
TEST(AnalysisPredictor, use_gpu) {
std::string model_dir = FLAGS_infer_model + "/" + "mobilenet";
AnalysisConfig config;
......@@ -180,20 +164,5 @@ TEST(AnalysisPredictor, use_gpu) {
}
}
TEST(TensorRT_mobilenet, profile) {
std::string model_dir = FLAGS_infer_model + "/" + "mobilenet";
profile(model_dir, true, false);
}
TEST(resnet50, compare_continuous_input) {
std::string model_dir = FLAGS_infer_model + "/resnet50";
compare_continuous_input(model_dir, true);
}
TEST(resnet50, compare_continuous_input_native) {
std::string model_dir = FLAGS_infer_model + "/resnet50";
compare_continuous_input(model_dir, false);
}
} // namespace inference
} // namespace paddle
paddle/fluid/op_use_default_grad_op_maker.spec
浏览文件 @ 431eab64
......@@ -18,7 +18,6 @@ gru
hierarchical_sigmoid
lrn
lstm_unit
lstmp
max_pool2d_with_index
max_pool3d_with_index
maxout
......@@ -29,8 +28,6 @@ pool3d
prelu
quantize
rank_loss
reduce_all
reduce_any
reduce_max
reduce_mean
reduce_min
......
paddle/fluid/operators/activation_op.cc
浏览文件 @ 431eab64
......@@ -227,6 +227,15 @@ $out = \sqrt{x}$
)DOC";
UNUSED constexpr char RsqrtDoc[] = R"DOC(
Rsqrt Activation Operator.
Please make sure input is legal in case of numeric errors.
$out = \frac{1}{\sqrt{x}}$
)DOC";
UNUSED constexpr char AbsDoc[] = R"DOC(
Abs Activation Operator.
......@@ -575,6 +584,7 @@ REGISTER_ACTIVATION_OP_MAKER(Gelu, GeluDoc);
REGISTER_ACTIVATION_OP_MAKER(Tanh, TanhDoc);
REGISTER_ACTIVATION_OP_MAKER(TanhShrink, TanhShrinkDoc);
REGISTER_ACTIVATION_OP_MAKER(Sqrt, SqrtDoc);
REGISTER_ACTIVATION_OP_MAKER(Rsqrt, RsqrtDoc);
REGISTER_ACTIVATION_OP_MAKER(Abs, AbsDoc);
REGISTER_ACTIVATION_OP_MAKER(Ceil, CeilDoc);
REGISTER_ACTIVATION_OP_MAKER(Floor, FloorDoc);
......@@ -586,6 +596,7 @@ REGISTER_ACTIVATION_OP_MAKER(Log, LogDoc);
REGISTER_ACTIVATION_OP_MAKER(Square, SquareDoc);
REGISTER_ACTIVATION_OP_MAKER(Softplus, SoftplusDoc);
REGISTER_ACTIVATION_OP_MAKER(Softsign, SoftsignDoc);
} // namespace operators
} // namespace paddle
......
paddle/fluid/operators/activation_op.h
浏览文件 @ 431eab64
......@@ -511,6 +511,26 @@ struct SqrtGradFunctor : public BaseActivationFunctor<T> {
static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepOut; }
};
// rsqrt(x) = x^(-1/2)
template <typename T>
struct RsqrtFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) = x.rsqrt();
}
};
template <typename T>
struct RsqrtGradFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
dx.device(d) = static_cast<T>(-0.5) * dout * out * out * out;
}
static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepOut; }
};
// ceil(x) = ceiling(x)
template <typename T>
struct CeilFunctor : public BaseActivationFunctor<T> {
......@@ -1191,6 +1211,7 @@ struct SwishGradFunctor : public BaseActivationFunctor<T> {
__macro(atan, Atan, AtanFunctor, AtanGradFunctor); \
__macro(softshrink, SoftShrink, SoftShrinkFunctor, SoftShrinkGradFunctor); \
__macro(sqrt, Sqrt, SqrtFunctor, SqrtGradFunctor); \
__macro(rsqrt, Rsqrt, RsqrtFunctor, RsqrtGradFunctor); \
__macro(abs, Abs, AbsFunctor, AbsGradFunctor); \
__macro(ceil, Ceil, CeilFunctor, ZeroGradFunctor); \
__macro(floor, Floor, FloorFunctor, ZeroGradFunctor); \
......
paddle/fluid/operators/affine_channel_op.cc
浏览文件 @ 431eab64
......@@ -79,9 +79,13 @@ class AffineChannelOp : public framework::OperatorWithKernel {
: x_dims[x_dims.size() - 1]);
PADDLE_ENFORCE_EQ(scale_dims.size(), 1UL);
PADDLE_ENFORCE_EQ(scale_dims[0], C);
PADDLE_ENFORCE_EQ(b_dims.size(), 1UL);
PADDLE_ENFORCE_EQ(b_dims[0], C);
if (ctx->IsRuntime() || scale_dims[0] > 0) {
PADDLE_ENFORCE_EQ(scale_dims[0], C);
}
if (ctx->IsRuntime() || b_dims[0] > 0) {
PADDLE_ENFORCE_EQ(b_dims[0], C);
}
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
ctx->ShareLoD("X", "Out");
......
paddle/fluid/operators/affine_grid_op.h
浏览文件 @ 431eab64
......@@ -121,9 +121,11 @@ class AffineGridOpKernel : public framework::OpKernel<T> {
// TODO(wanghaoshuang): Refine batched matrix multiply
auto blas = math::GetBlas<DeviceContext, T>(ctx);
for (int i = 0; i < n; ++i) {
Tensor sliced_grid = grid.Slice(i, i + 1).Resize({h * w, 3});
Tensor sliced_grid = grid.Slice(i, i + 1).Resize(
{static_cast<int64_t>(h) * static_cast<int64_t>(w), 3});
Tensor sliced_theta = theta->Slice(i, i + 1).Resize({2, 3});
Tensor sliced_out = output->Slice(i, i + 1).Resize({h * w, 2});
Tensor sliced_out = output->Slice(i, i + 1).Resize(
{static_cast<int64_t>(h) * static_cast<int64_t>(w), 2});
blas.MatMul(sliced_grid, false, sliced_theta, true, T(1), &sliced_out,
T(0));
}
......@@ -161,8 +163,10 @@ class AffineGridGradOpKernel : public framework::OpKernel<T> {
// TODO(wanghaoshuang): Refine batched matrix multiply
auto blas = math::GetBlas<DeviceContext, T>(ctx);
for (int i = 0; i < n; ++i) {
Tensor sliced_grid = grid.Slice(i, i + 1).Resize({h * w, 3});
Tensor sliced_out_grad = output_grad->Slice(i, i + 1).Resize({h * w, 2});
Tensor sliced_grid = grid.Slice(i, i + 1).Resize(
{static_cast<int64_t>(h) * static_cast<int64_t>(w), 3});
Tensor sliced_out_grad = output_grad->Slice(i, i + 1).Resize(
{static_cast<int64_t>(h) * static_cast<int64_t>(w), 2});
Tensor sliced_theta_grad = theta_grad->Slice(i, i + 1).Resize({2, 3});
blas.MatMul(sliced_out_grad, true, sliced_grid, false, T(1),
&sliced_theta_grad, T(0));
......
paddle/fluid/operators/batch_norm_op.cc
浏览文件 @ 431eab64
......@@ -65,11 +65,22 @@ void BatchNormOp::InferShape(framework::InferShapeContext *ctx) const {
(data_layout == DataLayout::kNCHW ? x_dims[1]
: x_dims[x_dims.size() - 1]);
PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale").size(), 1UL);
PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale")[0], C);
PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias").size(), 1UL);
PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias")[0], C);
auto scale_dim = ctx->GetInputDim("Scale");
auto bias_dim = ctx->GetInputDim("Bias");
PADDLE_ENFORCE_EQ(scale_dim.size(), 1UL);
PADDLE_ENFORCE_EQ(scale_dim.size(), 1UL);
bool check = true;
if ((!ctx->IsRuntime()) && (framework::product(scale_dim) <= 0 ||
framework::product(bias_dim) <= 0)) {
check = false;
}
if (check) {
PADDLE_ENFORCE_EQ(scale_dim[0], C);
PADDLE_ENFORCE_EQ(scale_dim[0], C);
}
ctx->SetOutputDim("Y", x_dims);
ctx->SetOutputDim("MeanOut", {C});
ctx->SetOutputDim("VarianceOut", {C});
......
paddle/fluid/operators/concat_op.cc
浏览文件 @ 431eab64
......@@ -49,7 +49,15 @@ class ConcatOp : public framework::OperatorWithKernel {
for (size_t i = 1; i < n; i++) {
for (size_t j = 0; j < in_zero_dims_size; j++) {
if (j == axis) {
out_dims[axis] += ins[i][j];
if (ctx->IsRuntime()) {
out_dims[axis] += ins[i][j];
} else {
if (ins[i][j] == -1) {
out_dims[axis] = -1;
} else {
out_dims[axis] += ins[i][j];
}
}
} else {
if (ctx->IsRuntime()) {
// check all shape in run time
......
paddle/fluid/operators/conv_op.cc
浏览文件 @ 431eab64
......@@ -68,9 +68,14 @@ void ConvOp::InferShape(framework::InferShapeContext* ctx) const {
std::vector<int64_t> output_shape({in_dims[0], filter_dims[0]});
for (size_t i = 0; i < strides.size(); ++i) {
output_shape.push_back(ConvOutputSize(in_dims[i + 2], filter_dims[i + 2],
dilations[i], paddings[i],
strides[i]));
if ((!ctx->IsRuntime()) &&
(in_dims[i + 2] <= 0 || filter_dims[i + 2] <= 0)) {
output_shape.push_back(-1);
} else {
output_shape.push_back(ConvOutputSize(in_dims[i + 2], filter_dims[i + 2],
dilations[i], paddings[i],
strides[i]));
}
}
ctx->SetOutputDim("Output", framework::make_ddim(output_shape));
ctx->ShareLoD("Input", "Output");
......
paddle/fluid/operators/conv_shift_op.cc
浏览文件 @ 431eab64
......@@ -36,14 +36,17 @@ class ConvShiftOp : public framework::OperatorWithKernel {
auto y_dims = ctx->GetInputDim("Y");
PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank should be 2.");
PADDLE_ENFORCE_EQ(y_dims.size(), 2, "Input(Y)'s rank should be 2.");
PADDLE_ENFORCE_EQ(x_dims[0], y_dims[0],
"The 1st dimension of Input(X) and Input(Y) should "
"be equal.");
PADDLE_ENFORCE_EQ(y_dims[1] % 2, 1,
"The 2nd dimension of Input(Y) should be odd.");
PADDLE_ENFORCE_LE(y_dims[1], x_dims[1],
"The 2nd dimension of Input(Y) should be less than or "
"equal to the 2nd dimension of Input(X).");
if (ctx->IsRuntime() || (x_dims[0] > 0 && y_dims[0] > 0))
PADDLE_ENFORCE_EQ(x_dims[0], y_dims[0],
"The 1st dimension of Input(X) and Input(Y) should "
"be equal.");
if (ctx->IsRuntime() || y_dims[1] > 0)
PADDLE_ENFORCE_EQ(y_dims[1] % 2, 1,
"The 2nd dimension of Input(Y) should be odd.");
if (ctx->IsRuntime() || (x_dims[1] > 0 && y_dims[1] > 0))
PADDLE_ENFORCE_LE(y_dims[1], x_dims[1],
"The 2nd dimension of Input(Y) should be less than or "
"equal to the 2nd dimension of Input(X).");
ctx->ShareDim("X", /*->*/ "Out");
ctx->ShareLoD("X", /*->*/ "Out");
}
......
paddle/fluid/operators/cos_sim_op.cc
浏览文件 @ 431eab64
......@@ -40,17 +40,27 @@ class CosSimOp : public framework::OperatorWithKernel {
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
PADDLE_ENFORCE_EQ(x_dims.size(), y_dims.size(),
"Ranks of Input(X) and Input(Y) must be equal.");
PADDLE_ENFORCE_GE(x_dims.size(), 2,
"Rank of Input(X) must not be less than 2.");
PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 1, x_dims.size()),
framework::slice_ddim(y_dims, 1, y_dims.size()),
"All dimensions except the 1st of Input(X) and Input(Y) "
"must be equal.");
PADDLE_ENFORCE(x_dims[0] == y_dims[0] || y_dims[0] == 1,
"The 1st dimension of Input(Y) must be equal to Input(X) or"
" just 1 (which will be broadcasted to match Input(X)).");
bool check = true;
if ((!ctx->IsRuntime()) &&
(framework::product(x_dims) <= 0 || framework::product(y_dims) <= 0)) {
check = false;
}
if (check) {
PADDLE_ENFORCE_EQ(x_dims.size(), y_dims.size(),
"Ranks of Input(X) and Input(Y) must be equal.");
PADDLE_ENFORCE_GE(x_dims.size(), 2,
"Rank of Input(X) must not be less than 2.");
PADDLE_ENFORCE_EQ(
framework::slice_ddim(x_dims, 1, x_dims.size()),
framework::slice_ddim(y_dims, 1, y_dims.size()),
"All dimensions except the 1st of Input(X) and Input(Y) "
"must be equal.");
PADDLE_ENFORCE(
x_dims[0] == y_dims[0] || y_dims[0] == 1,
"The 1st dimension of Input(Y) must be equal to Input(X) or"
" just 1 (which will be broadcasted to match Input(X)).");
}
// resize tensor
ctx->SetOutputDim("Out", {x_dims[0], 1});
......
paddle/fluid/operators/cvm_op.cc 0 → 100644
浏览文件 @ 431eab64
/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/cvm_op.h"
#include <memory>
#include "paddle/fluid/operators/math/math_function.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
class CVMOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
PADDLE_ENFORCE(ctx->HasInput("CVM"), "Input(CVM) should be not null.");
PADDLE_ENFORCE(ctx->HasOutput("Y"), "Output(Y) should be not null.");
auto x_dims = ctx->GetInputDim("X");
auto cvm_dims = ctx->GetInputDim("CVM");
PADDLE_ENFORCE_EQ(x_dims.size(), 2UL, "Input(X)'s rank should be 2.");
PADDLE_ENFORCE_EQ(cvm_dims.size(), 2UL, "Input(CVM)'s rank should be 2.");
PADDLE_ENFORCE_EQ(cvm_dims[1], 2UL,
"The 2nd dimension of "
"Input(CVM) should be 2.");
if (ctx->Attrs().Get<bool>("use_cvm")) {
ctx->SetOutputDim("Y", {x_dims[0], x_dims[1]});
} else {
ctx->SetOutputDim("Y", {x_dims[0], x_dims[1] - 2});
}
ctx->ShareLoD("X", /*->*/ "Y");
}
protected:
// Explicitly set that the data type of computation kernel of
// cvm
// is determined by its input "X".
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
platform::CPUPlace());
}
};
class CVMGradientOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
PADDLE_ENFORCE(ctx->HasInput("CVM"), "Input(CVM) should be not null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Y")),
"Input(Y@GRAD) should be not null.");
PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
"Output(X@GRAD) should be not null.");
auto x_dims = ctx->GetInputDim("X");
auto cvm_dims = ctx->GetInputDim("CVM");
auto dy_dims = ctx->GetInputDim(framework::GradVarName("Y"));
PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank should be 2.");
PADDLE_ENFORCE_EQ(dy_dims.size(), 2, "Input(Y@Grad)'s rank should be 2.");
PADDLE_ENFORCE_EQ(cvm_dims.size(), 2, "Input(CVM)'s rank should be 2.");
PADDLE_ENFORCE_EQ(x_dims[0], dy_dims[0],
"The 1st dimension of Input(X) and Input(Y@Grad) should "
"be equal.");
PADDLE_ENFORCE_EQ(cvm_dims[1], 2,
"When Attr(soft_label) == false, the 2nd dimension of "
"Input(CVM) should be 2.");
ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
ctx->ShareLoD("X", framework::GradVarName("X"));
}
protected:
// Explicitly set that the data type of computation kernel of
// cvm
// is determined by its input "X".
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
platform::CPUPlace());
}
};
class CVMOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"(LodTensor, default LodTensor<float>), a 2-D tensor with shape "
"[N x D],"
" where N is the batch size and D is the emebdding dim. ");
AddInput("CVM",
"(Tensor), a 2-D Tensor with shape [N x 2], where N is the batch "
"size, 2 is show and click.");
AddOutput("Y",
"(LodTensor, default LodTensor<float>), a 2-D tensor with shape "
"[N x K].");
AddAttr<bool>("use_cvm", "bool, use cvm or not").SetDefault(true);
AddComment(R"DOC(
CVM Operator.
We assume that input X is a embedding vector with cvm_feature(show and click), which shape is [N * D] (D is 2(cvm_feature) + embedding dim, N is batch_size)
if use_cvm is True, we will log(cvm_feature), and output shape is [N * D].
if use_cvm is False, we will remove cvm_feature from input, and output shape is [N * (D - 2)].
)DOC");
}
};
class CVMGradOpDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("cvm_grad");
op->SetInput("X", Input("X"));
op->SetInput("CVM", Input("CVM"));
op->SetInput(framework::GradVarName("Y"), OutputGrad("Y"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetAttrMap(Attrs());
return op;
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(cvm, ops::CVMOp, ops::CVMOpMaker, ops::CVMGradOpDescMaker);
REGISTER_OPERATOR(cvm_grad, ops::CVMGradientOp);
REGISTER_OP_CPU_KERNEL(cvm, ops::CVMOpKernel<float>, ops::CVMOpKernel<double>);
REGISTER_OP_CPU_KERNEL(cvm_grad, ops::CVMGradOpKernel<float>,
ops::CVMGradOpKernel<double>);
paddle/fluid/operators/cvm_op.h 0 → 100644
浏览文件 @ 431eab64
/* 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 "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
template <typename T>
class CVMOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
const LoDTensor* x = context.Input<LoDTensor>("X");
const T* x_data = x->data<T>();
auto lod = x->lod()[0];
int64_t item_size = x->numel() / x->dims()[0];
int offset = 2;
if (!context.Attr<bool>("use_cvm")) {
item_size -= offset;
}
LoDTensor* y = context.Output<LoDTensor>("Y");
T* y_data = y->mutable_data<T>(context.GetPlace());
int seq_num = static_cast<int>(lod.size()) - 1;
for (int i = 0; i < seq_num; ++i) {
int64_t seq_len = static_cast<int64_t>(lod[i + 1] - lod[i]);
for (int j = 0; j < seq_len; ++j) {
if (context.Attr<bool>("use_cvm")) {
std::memcpy(y_data, x_data, item_size * sizeof(T));
y_data[0] = log(y_data[0] + 1);
y_data[1] = log(y_data[1] + 1) - y_data[0];
x_data += item_size;
y_data += item_size;
} else {
std::memcpy(y_data, x_data + offset, item_size * sizeof(T));
x_data += item_size + offset;
y_data += item_size;
}
}
}
}
};
template <typename T>
class CVMGradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
LoDTensor* dx = context.Output<LoDTensor>(framework::GradVarName("X"));
T* dx_data = dx->mutable_data<T>(context.GetPlace());
const Tensor* cvm = context.Input<Tensor>("CVM");
const T* cvm_data = cvm->data<T>();
int offset = 2;
const framework::LoDTensor* dOut =
context.Input<framework::LoDTensor>(framework::GradVarName("Y"));
const T* dout_data = dOut->data<T>();
auto lod = dx->lod()[0];
int64_t item_size = dx->numel() / dx->dims()[0];
if (!context.Attr<bool>("use_cvm")) {
item_size -= offset;
}
int seq_num = static_cast<int>(lod.size()) - 1;
for (int i = 0; i < seq_num; ++i) {
int64_t seq_len = static_cast<int64_t>(lod[i + 1] - lod[i]);
for (int j = 0; j < seq_len; ++j) {
if (context.Attr<bool>("use_cvm")) {
std::memcpy(dx_data, dout_data, item_size * sizeof(T));
dx_data[0] = cvm_data[0];
dx_data[1] = cvm_data[1];
dx_data += item_size;
dout_data += item_size;
} else {
std::memcpy(dx_data + offset, dout_data, item_size * sizeof(T));
dx_data[0] = cvm_data[0];
dx_data[1] = cvm_data[1];
dx_data += item_size + offset;
dout_data += item_size;
}
}
cvm_data += offset;
}
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/detection_map_op.cc
浏览文件 @ 431eab64
......@@ -51,8 +51,10 @@ class DetectionMAPOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ(label_dims.size(), 2,
"The rank of Input(Label) must be 2, "
"the shape is [N, 6].");
PADDLE_ENFORCE(label_dims[1] == 6 || label_dims[1] == 5,
"The shape of Input(Label) is [N, 6] or [N, 5].");
if (ctx->IsRuntime() || label_dims[1] > 0) {
PADDLE_ENFORCE(label_dims[1] == 6 || label_dims[1] == 5,
"The shape of Input(Label) is [N, 6] or [N, 5].");
}
if (ctx->HasInput("PosCount")) {
PADDLE_ENFORCE(ctx->HasInput("TruePos"),
......
paddle/fluid/operators/distributed/CMakeLists.txt
浏览文件 @ 431eab64
......@@ -9,6 +9,9 @@ else()
endif()
configure_file(send_recv.proto.in ${CMAKE_CURRENT_SOURCE_DIR}/send_recv.proto @ONLY)
cc_library(async_sparse_param_update_recorder SRCS async_sparse_param_update_recorder.cc DEPS enforce simple_threadpool)
cc_test(async_sparse_param_update_recorder_test SRCS async_sparse_param_update_recorder_test.cc DEPS async_sparse_param_update_recorder)
# FIXME(typhoonzero): use add_subdirectory once we clean the dependency of these files
set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
if(WITH_GRPC)
......@@ -20,7 +23,7 @@ if(WITH_GRPC)
collective_client.cc collective_server.cc
${GRPC_SRCS}
PROTO send_recv.proto
DEPS lod_tensor selected_rows_functor memory scope ${GRPC_DEPS})
DEPS lod_tensor selected_rows_functor memory scope ${GRPC_DEPS} async_sparse_param_update_recorder)
set_source_files_properties(grpc_serde_test.cc rpc_server_test.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
set(RPC_DEPS sendrecvop_rpc ${GRPC_DEPS})
......
paddle/fluid/operators/distributed/async_sparse_param_update_recorder.cc 0 → 100644
浏览文件 @ 431eab64
// 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/operators/distributed/async_sparse_param_update_recorder.h"
namespace paddle {
namespace operators {
namespace distributed {
std::once_flag AsyncSparseParamUpdateRecorder::init_flag_;
std::unique_ptr<AsyncSparseParamUpdateRecorder>
AsyncSparseParamUpdateRecorder::recorder_(nullptr);
} // namespace distributed
} // namespace operators
} // namespace paddle
paddle/fluid/operators/distributed/async_sparse_param_update_recorder.h 0 → 100644
浏览文件 @ 431eab64
// 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 <functional>
#include <future> // NOLINT
#include <memory>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <vector>
#include <ThreadPool.h>
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
namespace operators {
namespace distributed {
class ConcurrentSet {
public:
ConcurrentSet() : pool_(new ::ThreadPool(1)) {}
~ConcurrentSet() {}
std::future<void> Update(const std::vector<int64_t>& rows) {
auto task = [this, rows] {
if (VLOG_IS_ON(3)) {
std::ostringstream sstream;
sstream << "[";
for (auto& id : rows) {
sstream << id << ", ";
}
sstream << "]";
VLOG(3) << "update ids -> " << sstream.str();
}
for (auto row : rows) {
set_.insert(row);
}
};
return pool_->enqueue(std::move(task));
}
std::future<void> GetAndClear(std::vector<int64_t>* result) {
auto task = [this, &result] {
result->clear();
for (auto& id : set_) {
result->push_back(id);
}
if (VLOG_IS_ON(3)) {
std::ostringstream sstream;
sstream << "[";
for (auto& id : *result) {
sstream << id << ", ";
}
sstream << "]";
VLOG(3) << "result ids size: " << result->size() << " "
<< sstream.str();
}
set_.clear();
};
return pool_->enqueue(std::move(task));
}
private:
std::unordered_set<int64_t> set_;
std::unique_ptr<::ThreadPool> pool_{nullptr};
};
class AsyncSparseParamUpdateRecorder {
using TrainerToRows = std::vector<std::unique_ptr<ConcurrentSet>>;
public:
AsyncSparseParamUpdateRecorder(
int trainer_num,
const std::unordered_map<std::string, std::string>& grad_to_param)
: trainer_num_(trainer_num), grad_to_param_(grad_to_param) {
if (VLOG_IS_ON(3)) {
std::ostringstream sstream;
sstream << "[";
for (auto& item : grad_to_param) {
sstream << item.first << ":" << item.second << ", ";
}
sstream << "]";
VLOG(3) << "trainer_num: " << trainer_num
<< " grad_to_param_: " << sstream.str();
}
for (auto& iter : grad_to_param) {
param_to_grad_[iter.second] = iter.first;
auto& param_name = iter.second;
param_to_updated_rows_[param_name] = TrainerToRows();
auto& trainer_to_rows = param_to_updated_rows_[param_name];
for (auto i = 0; i < trainer_num; ++i) {
trainer_to_rows.emplace_back(new ConcurrentSet());
}
}
}
~AsyncSparseParamUpdateRecorder() = default;
void Update(const std::string& grad_name,
const std::vector<int64_t>& update_rows) {
VLOG(3) << "update grad: " << grad_name
<< " row size: " << update_rows.size();
auto& param_name = grad_to_param_.at(grad_name);
auto& trainer_to_rows = param_to_updated_rows_.at(param_name);
std::vector<std::future<void>> fs;
for (auto& set : trainer_to_rows) {
fs.push_back(set->Update(update_rows));
}
for (auto& f : fs) {
f.wait();
}
}
void GetAndClear(const std::string& param_name, int trainer_id,
std::vector<int64_t>* result) {
VLOG(3) << "GetAndClear param: " << param_name
<< " for trainer: " << trainer_id;
PADDLE_ENFORCE_LT(trainer_id, trainer_num_);
param_to_updated_rows_.at(param_name)[trainer_id]
->GetAndClear(result)
.wait();
}
bool HasParam(const std::string& param_name) {
return param_to_grad_.find(param_name) != param_to_grad_.end();
}
bool HasGrad(const std::string& grad_name) {
return grad_to_param_.find(grad_name) != grad_to_param_.end();
}
private:
const int trainer_num_;
std::unordered_map<std::string, std::string> grad_to_param_;
std::unordered_map<std::string, std::string> param_to_grad_;
std::unordered_map<std::string, TrainerToRows> param_to_updated_rows_;
// init recorder
public:
static void Init(
int trainer_num,
const std::unordered_map<std::string, std::string>& grad_to_param) {
InitImpl(trainer_num, grad_to_param);
}
static AsyncSparseParamUpdateRecorder* GetInstance() {
return recorder_.get();
}
private:
// Init is called by GetInstance.
static void InitImpl(
int trainer_num,
const std::unordered_map<std::string, std::string>& grad_to_param) {
if (recorder_ == nullptr) {
recorder_.reset(
new AsyncSparseParamUpdateRecorder(trainer_num, grad_to_param));
}
}
static std::once_flag init_flag_;
static std::unique_ptr<AsyncSparseParamUpdateRecorder> recorder_;
};
} // namespace distributed
} // namespace operators
} // namespace paddle
paddle/fluid/operators/distributed/async_sparse_param_update_recorder_test.cc 0 → 100644
浏览文件 @ 431eab64
// 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/operators/distributed/async_sparse_param_update_recorder.h"
#include <algorithm>
#include "gtest/gtest.h"
namespace paddle {
namespace operators {
namespace distributed {
TEST(ConcurrentSet, All) {
ConcurrentSet concurrent_set;
std::vector<int64_t> in1 = {1, 2, 3, 4};
std::vector<int64_t> in2 = {2, 3, 5, 6};
std::vector<std::future<void>> futures;
futures.push_back(concurrent_set.Update(in1));
futures.push_back(concurrent_set.Update(in2));
for (auto &f : futures) {
f.wait();
}
std::unordered_set<int64_t> in;
std::copy(in1.begin(), in1.end(), std::inserter(in, in.begin()));
std::copy(in2.begin(), in2.end(), std::inserter(in, in.begin()));
std::vector<int64_t> ret;
concurrent_set.GetAndClear(&ret).wait();
std::unordered_set<int64_t> out;
std::copy(ret.begin(), ret.end(), std::inserter(out, out.begin()));
EXPECT_EQ(in, out);
concurrent_set.GetAndClear(&ret).wait();
EXPECT_EQ(ret.size(), 0);
}
TEST(AsyncSparseParamUpdateRecorder, All) {
std::unordered_map<std::string, std::string> grad_to_param;
grad_to_param["grad1"] = "param1";
grad_to_param["grad2"] = "param2";
int trainer_num = 10;
AsyncSparseParamUpdateRecorder recorder(trainer_num, grad_to_param);
std::vector<int64_t> in1 = {1, 2, 3, 4};
std::vector<int64_t> in2 = {2, 3, 5, 6};
std::unordered_set<int64_t> in;
std::copy(in1.begin(), in1.end(), std::inserter(in, in.begin()));
std::copy(in2.begin(), in2.end(), std::inserter(in, in.begin()));
recorder.Update("grad1", in1);
recorder.Update("grad1", in2);
EXPECT_TRUE(recorder.HasParam("param1"));
EXPECT_TRUE(recorder.HasParam("param2"));
EXPECT_FALSE(recorder.HasParam("param3"));
EXPECT_TRUE(recorder.HasGrad("grad1"));
EXPECT_TRUE(recorder.HasGrad("grad2"));
EXPECT_FALSE(recorder.HasGrad("grad3"));
std::vector<int64_t> ret;
EXPECT_ANY_THROW(recorder.GetAndClear("param1", trainer_num, &ret));
for (int i = 0; i < trainer_num; ++i) {
std::vector<int64_t> ret;
std::unordered_set<int64_t> out;
recorder.GetAndClear("param1", i, &ret);
std::copy(ret.begin(), ret.end(), std::inserter(out, out.begin()));
EXPECT_EQ(in, out);
recorder.GetAndClear("param1", i, &ret);
EXPECT_EQ(ret.size(), 0);
}
}
} // namespace distributed
} // namespace operators
} // namespace paddle
paddle/fluid/operators/distributed/brpc/brpc_client.cc
浏览文件 @ 431eab64
......@@ -234,6 +234,7 @@ VarHandlePtr BRPCClient::AsyncGetVar(const std::string& ep,
const framework::Scope& scope,
const std::string& var_name,
const std::string& out_var_name,
const std::string& table_name,
int64_t time_out) {
return _AsyncGetVar(ep, ctx, scope, var_name, out_var_name, kGetRPC,
time_out);
......
paddle/fluid/operators/distributed/brpc/brpc_client.h
浏览文件 @ 431eab64
......@@ -21,8 +21,10 @@ limitations under the License. */
#include <functional>
#include <iostream>
#include <map>
#include <memory>
#include <mutex> // NOLINT
#include <string>
#include <unordered_map>
#include <vector>
#include "brpc/channel.h"
......@@ -66,6 +68,7 @@ class BRPCClient : public RPCClient {
const framework::Scope& scope,
const std::string& var_name,
const std::string& out_var_name,
const std::string& table_name = "",
int64_t time_out = FLAGS_rpc_deadline) override;
VarHandlePtr AsyncGetMonomerBarrier(
......@@ -107,13 +110,11 @@ class BRPCClient : public RPCClient {
void SendComplete() override;
private:
VarHandlePtr _AsyncGetVar(const std::string& ep,
const platform::DeviceContext& ctx,
const framework::Scope& scope,
const std::string& var_name,
const std::string& out_var_name,
const std::string& method_name,
int64_t time_out = FLAGS_rpc_deadline);
VarHandlePtr _AsyncGetVar(
const std::string& ep, const platform::DeviceContext& ctx,
const framework::Scope& scope, const std::string& var_name,
const std::string& out_var_name, const std::string& method_name,
const std::string& table_name, int64_t time_out = FLAGS_rpc_deadline);
void Proceed();
ChannelQueuePtr GetChannel(const std::string& ep);
......
paddle/fluid/operators/distributed/communicator.cc
浏览文件 @ 431eab64
......@@ -32,6 +32,9 @@ DEFINE_int32(communicator_send_queue_size, 20,
DEFINE_int32(communicator_max_send_grad_num_before_recv, 20,
"max grad num to send before recv parameters");
DEFINE_int32(communicator_thread_pool_size, 5, "thread num to do send or recv");
DEFINE_int32(communicator_send_wait_times, 5,
"times that send thread will wait if merge num does not reach "
"max_merge_var_num");
DEFINE_int32(communicator_max_merge_var_num, 20,
"max var num to merge and send");
DEFINE_bool(communicator_fake_rpc, false,
......@@ -65,6 +68,8 @@ Communicator::Communicator(const RpcCtxMap &send_varname_to_ctx,
<< FLAGS_communicator_max_send_grad_num_before_recv;
VLOG(0) << "communicator_thread_pool_size: "
<< FLAGS_communicator_thread_pool_size;
VLOG(0) << "communicator_send_wait_times: "
<< FLAGS_communicator_send_wait_times;
VLOG(0) << "communicator_max_merge_var_num: "
<< FLAGS_communicator_max_merge_var_num;
VLOG(0) << "communicator_fake_rpc: " << FLAGS_communicator_fake_rpc;
......@@ -101,20 +106,32 @@ void Communicator::SendThread() {
VLOG(3) << var_name << " merge and send";
std::vector<std::shared_ptr<Variable>> vars;
size_t merged_var_num = 0;
while (var_queue->Size() > 0 &&
merged_var_num < FLAGS_communicator_max_merge_var_num) {
vars.push_back(var_queue->Pop());
// only count the send number of the first var
if (var_name == send_varname_to_queue_.begin()->first) {
grad_num_.fetch_add(1, std::memory_order_relaxed);
size_t wait_times = 0;
while (merged_var_num < FLAGS_communicator_max_merge_var_num) {
if (var_queue->Size() == 0) {
VLOG(3) << "wait_times -> " << wait_times;
if (wait_times >= FLAGS_communicator_send_wait_times) {
break;
}
std::this_thread::sleep_for(std::chrono::milliseconds(10));
wait_times++;
continue;
} else {
wait_times = 0;
vars.push_back(var_queue->Pop());
// only count the send number of the first var
if (var_name == send_varname_to_queue_.begin()->first) {
grad_num_.fetch_add(1, std::memory_order_relaxed);
}
merged_var_num++;
}
merged_var_num++;
}
auto before_merge = GetCurrentUS();
MergeVars(var_name, vars, send_scope_.get());
auto after_merge = GetCurrentUS();
VLOG(3) << "merge " << var_name << " use time "
<< after_merge - before_merge;
VLOG(3) << "merge " << merged_var_num << " " << var_name
<< " use time " << after_merge - before_merge;
auto send_functor = distributed::ParameterSend<float>();
auto &ctx = send_varname_to_ctx_.at(var_name);
if (!FLAGS_communicator_fake_rpc) {
......
paddle/fluid/operators/distributed/communicator.h
浏览文件 @ 431eab64
......@@ -109,7 +109,7 @@ inline void MergeVars(const std::string& var_name,
auto* out_var = scope->Var(var_name);
if (var0->IsType<framework::LoDTensor>()) {
auto dims = var0->Get<framework::LoDTensor>().dims();
VLOG(3) << "merge " << var_name << " LoDTensor " << dims;
VLOG(3) << "merge " << var_name << " LoDTensor dims " << dims;
// init output tensor
auto* out_t = out_var->GetMutable<framework::LoDTensor>();
......
paddle/fluid/operators/distributed/grpc/grpc_client.cc
浏览文件 @ 431eab64
......@@ -128,9 +128,11 @@ VarHandlePtr GRPCClient::AsyncGetVar(const std::string& ep,
const framework::Scope& scope,
const std::string& var_name,
const std::string& out_varname,
const std::string& table_name,
int64_t time_out) {
return _AsyncGetVar(ep, ctx, scope, kGetRPC, var_name, out_varname,
"/sendrecv.SendRecvService/GetVariable", time_out);
"/sendrecv.SendRecvService/GetVariable", table_name,
time_out);
}
VarHandlePtr GRPCClient::AsyncGetVarNoBarrier(
......@@ -142,7 +144,7 @@ VarHandlePtr GRPCClient::AsyncGetVarNoBarrier(
return _AsyncGetVar(
ep, ctx, scope, kGetNoBarrierRPC, var_name_no_barrier, out_varname,
"/sendrecv.SendRecvService/GetVariableNoBarrier", time_out);
"/sendrecv.SendRecvService/GetVariableNoBarrier", "", time_out);
}
VarHandlePtr GRPCClient::AsyncGetMonomerVariable(
......@@ -150,18 +152,21 @@ VarHandlePtr GRPCClient::AsyncGetMonomerVariable(
const framework::Scope& scope, const std::string& var_name,
int64_t time_out) {
return _AsyncGetVar(ep, ctx, scope, kGetMonomerRPC, var_name, var_name,
"/sendrecv.SendRecvService/GetMonomerVariable", time_out);
"/sendrecv.SendRecvService/GetMonomerVariable", "",
time_out);
}
VarHandlePtr GRPCClient::_AsyncGetVar(
const std::string& ep, const platform::DeviceContext& ctx,
const framework::Scope& scope, const std::string& method,
const std::string& var_name, const std::string& out_varname,
const std::string& rpc_path, int64_t time_out) {
const std::string& rpc_path, const std::string& table_name,
int64_t time_out) {
const platform::DeviceContext* p_ctx = &ctx;
const std::string ep_val = ep;
const std::string var_name_val = var_name;
const std::string out_varname_val = out_varname;
const std::string table_name_val = table_name;
const framework::Scope* p_scope = &scope;
const auto ch = GetChannel(ep_val);
GetProcessor* s = new GetProcessor(ch);
......@@ -169,32 +174,33 @@ VarHandlePtr GRPCClient::_AsyncGetVar(
VarHandlePtr h(new VarHandle(ep, method, out_varname_val, p_ctx, p_scope));
s->Prepare(h, time_out);
framework::AsyncIO(
[var_name_val, out_varname_val, s, method, p_ctx, h, rpc_path, this] {
// prepare input
sendrecv::VariableMessage req;
req.set_varname(var_name_val);
req.set_out_varname(out_varname_val);
req.set_trainer_id(trainer_id_);
::grpc::ByteBuffer buf;
RequestToByteBuffer<sendrecv::VariableMessage>(req, &buf);
framework::AsyncIO([var_name_val, out_varname_val, table_name_val, s, method,
p_ctx, h, rpc_path, this] {
// prepare input
sendrecv::VariableMessage req;
req.set_varname(var_name_val);
req.set_out_varname(out_varname_val);
req.set_trainer_id(trainer_id_);
req.set_table_name(table_name_val);
::grpc::ByteBuffer buf;
RequestToByteBuffer<sendrecv::VariableMessage>(req, &buf);
VLOG(3) << s->GetVarHandlePtr()->String() << " begin";
VLOG(3) << s->GetVarHandlePtr()->String() << " begin";
// stub context
s->response_call_back_ = ProcGetResponse;
// stub context
s->response_call_back_ = ProcGetResponse;
platform::RecordRPCEvent record_event(method);
platform::RecordRPCEvent record_event(method);
auto call =
s->stub_g_.PrepareUnaryCall(s->context_.get(), rpc_path, buf, &cq_);
call->StartCall();
call->Finish(&s->reply_, &s->status_, reinterpret_cast<void*>(s));
auto call =
s->stub_g_.PrepareUnaryCall(s->context_.get(), rpc_path, buf, &cq_);
call->StartCall();
call->Finish(&s->reply_, &s->status_, reinterpret_cast<void*>(s));
if (UNLIKELY(platform::IsProfileEnabled())) {
h->Wait();
}
});
if (UNLIKELY(platform::IsProfileEnabled())) {
h->Wait();
}
});
req_count_++;
......
paddle/fluid/operators/distributed/grpc/grpc_client.h
浏览文件 @ 431eab64
......@@ -23,9 +23,11 @@ limitations under the License. */
#include <functional>
#include <iostream>
#include <map>
#include <memory>
#include <mutex> // NOLINT
#include <string>
#include <thread> // NOLINT
#include <unordered_map>
#include <vector>
#include "grpc++/channel.h"
......@@ -187,6 +189,7 @@ class GRPCClient : public RPCClient {
const framework::Scope& scope,
const std::string& var_name,
const std::string& out_varname,
const std::string& table_name = "",
int64_t time_out = FLAGS_rpc_deadline) override;
VarHandlePtr AsyncGetVarNoBarrier(
......@@ -239,7 +242,8 @@ class GRPCClient : public RPCClient {
const std::string& ep, const platform::DeviceContext& ctx,
const framework::Scope& scope, const std::string& method,
const std::string& var_name, const std::string& out_varname,
const std::string& rpc_path, int64_t time_out = FLAGS_rpc_deadline);
const std::string& rpc_path, const std::string& table_name = "",
int64_t time_out = FLAGS_rpc_deadline);
private:
grpc::CompletionQueue cq_;
......
paddle/fluid/operators/distributed/grpc/grpc_server.cc
浏览文件 @ 431eab64
......@@ -137,6 +137,7 @@ class RequestGet final : public RequestBase {
// proc request.
std::string varname = request_.varname();
std::string out_varname = request_.out_varname();
std::string table_name = request_.table_name();
int trainer_id = request_.trainer_id();
VLOG(4) << "RequestGet " << out_varname << " from " << varname;
......@@ -145,19 +146,23 @@ class RequestGet final : public RequestBase {
framework::Variable* invar = nullptr;
framework::Variable* outvar = nullptr;
request_handler_->Handle(varname, scope, invar, &outvar, trainer_id,
out_varname);
tmp_scope_ = std::move(scope->NewTmpScope());
request_handler_->Handle(varname, tmp_scope_.get(), invar, &outvar,
trainer_id, out_varname, table_name);
VLOG(1) << "before SerializeToByteBuffer";
if (outvar) {
SerializeToByteBuffer(out_varname, outvar, *request_handler_->dev_ctx(),
&reply_);
}
VLOG(1) << "after SerializeToByteBuffer";
Finish(reply_, &responder_);
}
protected:
sendrecv::VariableMessage request_;
::grpc::ByteBuffer reply_;
std::unique_ptr<framework::Scope> tmp_scope_;
ServerAsyncResponseWriter<::grpc::ByteBuffer> responder_;
};
......
paddle/fluid/operators/distributed/parameter_recv.cc
浏览文件 @ 431eab64
......@@ -42,27 +42,23 @@ using DDim = framework::DDim;
template <typename T>
void ParameterRecv<T>::operator()(const RpcContext &rpc_ctx,
const framework::Scope &scope) {
VLOG(3) << "ParameterRecv in";
VLOG(3) << "ParameterRecv in " << rpc_ctx.var_name;
std::unique_ptr<framework::Scope> local_scope = scope.NewTmpScope();
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &cpu_ctx = *pool.Get(platform::CPUPlace());
distributed::RPCClient *rpc_client =
distributed::RPCClient::GetInstance<RPCCLIENT_T>(0);
distributed::RPCClient::GetInstance<RPCCLIENT_T>(rpc_ctx.trainer_id);
auto *recv_var = scope.FindVar(rpc_ctx.var_name);
std::vector<framework::Tensor *> recved_tensors;
// recv all vars to local scope
if (recv_var->IsType<framework::LoDTensor>()) {
std::vector<distributed::VarHandlePtr> rets;
for (size_t i = 0; i < rpc_ctx.splited_var_names.size(); i++) {
auto &recv_var_name = rpc_ctx.splited_var_names[i];
framework::Tensor *t =
local_scope->Var(recv_var_name)->GetMutable<framework::LoDTensor>();
recved_tensors.push_back(t);
local_scope->Var(recv_var_name);
VLOG(3) << "recv " << recv_var_name << " from " << rpc_ctx.epmap[i];
rets.push_back(rpc_client->AsyncGetVar(rpc_ctx.epmap[i], cpu_ctx,
*local_scope.get(), recv_var_name,
......@@ -78,23 +74,61 @@ void ParameterRecv<T>::operator()(const RpcContext &rpc_ctx,
// concat recved tensor into one var
{
size_t output_offset = 0;
size_t row_offset = 0;
framework::Tensor *recv_tensor =
recv_var->GetMutable<framework::LoDTensor>();
auto dev_ctx = paddle::platform::CPUDeviceContext();
int64_t recv_numel = 0;
for (auto *in : recved_tensors) {
recv_numel += in->numel();
auto in_stride = framework::stride_numel(in->dims());
auto out_stride = framework::stride_numel(recv_tensor->dims());
StridedNumelCopyWithAxis<T>(
dev_ctx, 0, recv_tensor->data<T>() + output_offset, out_stride,
in->data<T>(), in_stride, in_stride[0]);
output_offset += in_stride[0];
for (auto &recv_var_name : rpc_ctx.splited_var_names) {
auto *recv_var = local_scope->FindVar(recv_var_name);
if (recv_var->IsType<framework::LoDTensor>()) {
auto &in = recv_var->Get<framework::LoDTensor>();
recv_numel += in.numel();
auto in_stride = framework::stride_numel(in.dims());
auto out_stride = framework::stride_numel(recv_tensor->dims());
StridedNumelCopyWithAxis<T>(
dev_ctx, 0, recv_tensor->data<T>() + output_offset, out_stride,
in.data<T>(), in_stride, in_stride[0]);
output_offset += in_stride[0];
} else if (recv_var->IsType<framework::SelectedRows>()) {
auto &recv_slr = recv_var->Get<framework::SelectedRows>();
auto &recv_dims = recv_tensor->dims();
int64_t width = recv_dims[1];
recv_numel += recv_slr.height() * width;
PADDLE_ENFORCE_EQ(recv_slr.value().dims()[1], width);
PADDLE_ENFORCE_EQ(recv_slr.value().dims()[0], recv_slr.rows().size());
VLOG(3) << "recv slr " << recv_var_name << " dims "
<< recv_slr.value().dims();
if (VLOG_IS_ON(3)) {
std::ostringstream sstream;
sstream << "[";
for (auto &row_id : recv_slr.rows()) {
sstream << row_id << ", ";
}
sstream << "]";
VLOG(3) << "recv_slr size: " << recv_slr.rows().size() << " "
<< sstream.str();
}
for (auto i = 0; i < recv_slr.rows().size(); ++i) {
auto row_id = recv_slr.rows()[i] + row_offset;
PADDLE_ENFORCE_LT(row_id, recv_dims[0]);
memcpy(recv_tensor->data<T>() + row_id * width,
recv_slr.value().data<T>() + i * width, sizeof(T) * width);
}
row_offset += recv_slr.height();
} else {
PADDLE_THROW("unsupported recieved var type");
}
}
auto numel = recv_tensor->numel();
if (recv_numel != numel) {
LOG(FATAL) << "recv_numel: " << recv_numel << " acture numel: " << numel;
}
PADDLE_ENFORCE_EQ(recv_numel, recv_tensor->numel());
PADDLE_ENFORCE_EQ(recv_numel, numel);
}
VLOG(3) << "ParameterRecv out";
VLOG(3) << "ParameterRecv out " << rpc_ctx.var_name;
}
template struct ParameterRecv<float>;
......
paddle/fluid/operators/distributed/parameter_send.cc
浏览文件 @ 431eab64
......@@ -47,7 +47,7 @@ void ParameterSend<T>::operator()(const RpcContext &rpc_ctx,
auto &cpu_ctx = *pool.Get(platform::CPUPlace());
distributed::RPCClient *rpc_client =
distributed::RPCClient::GetInstance<RPCCLIENT_T>(0);
distributed::RPCClient::GetInstance<RPCCLIENT_T>(rpc_ctx.trainer_id);
auto *send_var = scope.FindVar(rpc_ctx.var_name);
size_t out_num = rpc_ctx.splited_var_names.size();
......
paddle/fluid/operators/distributed/request_handler.h
浏览文件 @ 431eab64
......@@ -18,7 +18,9 @@
#include <condition_variable> // NOLINT
#include <functional>
#include <memory>
#include <string>
#include <unordered_map>
#include <utility>
#include <vector>
......@@ -180,6 +182,10 @@ class RequestHandler {
grad_to_prepared_ctx_ = g;
}
void SetSparseGradToParam(std::unordered_map<std::string, std::string>* g) {
sparse_grad_to_param_ = g;
}
void SetRPCServer(RPCServer* rpc_server) { rpc_server_ = rpc_server; }
// Get attributes.
......@@ -228,6 +234,7 @@ class RequestHandler {
std::unordered_map<std::string,
std::shared_ptr<framework::ExecutorPrepareContext>>*
grad_to_prepared_ctx_;
std::unordered_map<std::string, std::string>* sparse_grad_to_param_;
RPCServer* rpc_server_;
};
......
paddle/fluid/operators/distributed/request_handler_impl.cc
浏览文件 @ 431eab64
......@@ -22,6 +22,7 @@
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/variable_helper.h"
#include "paddle/fluid/operators/distributed/async_sparse_param_update_recorder.h"
#include "paddle/fluid/operators/distributed/rpc_server.h"
#include "paddle/fluid/string/piece.h"
#include "paddle/fluid/string/printf.h"
......@@ -59,6 +60,12 @@ bool RequestSendHandler::Handle(const std::string& varname,
"async mode should not recv BATCH_BARRIER_MESSAGE or "
"COMPLETE_MESSAGE");
}
if (AsyncSparseParamUpdateRecorder::GetInstance()->HasGrad(varname)) {
auto& grad_slr =
scope->FindVar(varname)->Get<framework::SelectedRows>();
AsyncSparseParamUpdateRecorder::GetInstance()->Update(varname,
grad_slr.rows());
}
executor_->RunPreparedContext((*grad_to_prepared_ctx_)[varname].get(),
scope);
return true;
......@@ -82,8 +89,9 @@ bool RequestGetHandler::Handle(const std::string& varname,
const int trainer_id,
const std::string& out_var_name,
const std::string& table_name) {
VLOG(4) << "RequestGetHandler:" << varname
<< " out_var_name: " << out_var_name;
VLOG(3) << "RequestGetHandler:" << varname
<< " out_var_name: " << out_var_name << " trainer_id: " << trainer_id
<< " table_name: " << table_name;
if (sync_mode_) {
if (varname == FETCH_BARRIER_MESSAGE) {
......@@ -108,7 +116,42 @@ bool RequestGetHandler::Handle(const std::string& varname,
VLOG(3) << "copying " << varname << " to " << param_bak_name;
framework::TensorCopy(t_orig, dev_ctx_->GetPlace(), t);
}
*outvar = scope_->FindVar(varname);
if (AsyncSparseParamUpdateRecorder::GetInstance()->HasParam(varname) &&
!table_name.empty()) {
std::vector<int64_t> updated_rows;
AsyncSparseParamUpdateRecorder::GetInstance()->GetAndClear(
varname, trainer_id, &updated_rows);
if (VLOG_IS_ON(3)) {
std::ostringstream sstream;
sstream << "[";
for (auto& row_id : updated_rows) {
sstream << row_id << ", ";
}
sstream << "]";
VLOG(3) << "updated_rows size: " << updated_rows.size() << " "
<< sstream.str();
}
auto& origin_tensor =
scope_->FindVar(varname)->Get<framework::LoDTensor>();
auto* origin_tensor_data = origin_tensor.data<float>();
auto& dims = origin_tensor.dims();
*outvar = scope->Var();
auto* out_slr = (*outvar)->GetMutable<framework::SelectedRows>();
out_slr->set_rows(updated_rows);
out_slr->set_height(dims[0]);
auto out_dims = framework::make_ddim(
{static_cast<int64_t>(updated_rows.size()), dims[1]});
auto* data = out_slr->mutable_value()->mutable_data<float>(
out_dims, origin_tensor.place());
auto width = dims[1];
for (auto i = 0; i < updated_rows.size(); ++i) {
PADDLE_ENFORCE_LT(updated_rows[i], dims[0]);
memcpy(data + i * width, origin_tensor_data + updated_rows[i] * width,
sizeof(float) * width);
}
} else {
*outvar = scope_->FindVar(varname);
}
}
}
return true;
......
paddle/fluid/operators/distributed/rpc_client.h
浏览文件 @ 431eab64
......@@ -15,6 +15,7 @@
#pragma once
#include <condition_variable> // NOLINT
#include <memory>
#include <string>
#include "gflags/gflags.h"
......@@ -44,6 +45,7 @@ class RPCClient {
const framework::Scope& scope,
const std::string& var_name,
const std::string& out_varname,
const std::string& table_name = "",
int64_t time_out = FLAGS_rpc_deadline) = 0;
virtual VarHandlePtr AsyncGetVarNoBarrier(
......@@ -96,6 +98,7 @@ class RPCClient {
// Init is called by GetInstance.
template <typename T>
static void Init(int trainer_id) {
VLOG(0) << "init rpc client with trainer_id " << trainer_id;
trainer_id_ = trainer_id;
if (rpc_client_.get() == nullptr) {
rpc_client_.reset(new T());
......
paddle/fluid/operators/distributed/rpc_common.h
浏览文件 @ 431eab64
......@@ -27,23 +27,26 @@ struct RpcContext {
RpcContext(const std::string &name, const std::vector<std::string> &names,
const std::vector<std::string> &emap,
const std::vector<int64_t> &sections)
const std::vector<int64_t> &sections, int id)
: var_name(name),
splited_var_names(names),
epmap(emap),
height_sections(sections) {}
height_sections(sections),
trainer_id(id) {}
RpcContext(const RpcContext &ctx) {
var_name = ctx.var_name;
splited_var_names = ctx.splited_var_names;
epmap = ctx.epmap;
height_sections = ctx.height_sections;
trainer_id = ctx.trainer_id;
}
std::string var_name;
std::vector<std::string> splited_var_names;
std::vector<std::string> epmap;
std::vector<int64_t> height_sections;
int trainer_id;
};
inline std::ostream &operator<<(std::ostream &os, const RpcContext &rpc_ctx) {
......
paddle/fluid/operators/distributed_ops/CMakeLists.txt
浏览文件 @ 431eab64
......@@ -2,9 +2,9 @@ include(operators)
set(DISTRIBUTE_DEPS "")
if(WITH_GRPC)
set(DISTRIBUTE_DEPS sendrecvop_rpc parameter_send parameter_recv communicator grpc++_unsecure grpc_unsecure gpr cares zlib protobuf node)
set(DISTRIBUTE_DEPS sendrecvop_rpc parameter_send parameter_recv communicator async_sparse_param_update_recorder grpc++_unsecure grpc_unsecure gpr cares zlib protobuf node)
else()
set(DISTRIBUTE_DEPS sendrecvop_rpc parameter_send parameter_recv communicator brpc leveldb snappystream snappy protobuf ssl crypto zlib node)
set(DISTRIBUTE_DEPS sendrecvop_rpc parameter_send parameter_recv communicator async_sparse_param_update_recorder brpc leveldb snappystream snappy protobuf ssl crypto zlib node)
if(WITH_BRPC_RDMA)
find_library(IBVERBS_LIBRARY NAMES ibverbs)
ADD_LIBRARY(ibverbs SHARED IMPORTED GLOBAL)
......
paddle/fluid/operators/distributed_ops/listen_and_serv_op.cc
浏览文件 @ 431eab64
......@@ -24,8 +24,10 @@ limitations under the License. */
#include "paddle/fluid/operators/distributed/distributed.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/distributed/async_sparse_param_update_recorder.h"
#include "paddle/fluid/operators/distributed/request_handler_impl.h"
#include "paddle/fluid/operators/distributed_ops/listen_and_serv_op.h"
#include "paddle/fluid/platform/profiler.h"
DEFINE_int32(rpc_send_thread_num, 12, "number of threads for rpc send");
......@@ -292,6 +294,8 @@ static void FillRequestCtx(
std::unordered_map<std::string,
std::shared_ptr<framework::ExecutorPrepareContext>>
*prefetch_ctx,
std::unordered_map<std::string, std::string>
*sparse_grad_name_to_param_name,
std::shared_ptr<framework::ExecutorPrepareContext> checkpoint_ctx,
distributed::RPCServer *rpc_server) {
h->SetScope(scope);
......@@ -299,6 +303,7 @@ static void FillRequestCtx(
h->SetExecutor(executor);
h->SetProgram(program);
h->SetPrefetchPreparedCtx(prefetch_ctx);
h->SetSparseGradToParam(sparse_grad_name_to_param_name);
h->SetRPCServer(rpc_server);
h->SetCheckpointNotifyPreparedCtx(checkpoint_ctx);
}
......@@ -414,10 +419,24 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
prefetch_var_name_to_prepared_ctx[prefetch_var_name] = prefetch_prepared[i];
}
auto f =
std::bind(FillRequestCtx, std::placeholders::_1, &recv_scope, &dev_ctx,
&executor, program, &prefetch_var_name_to_prepared_ctx,
ckpt_pre_context, rpc_service_.get());
// parse attr of kSparseGradToParam sparse_grad_name -> param_name
std::unordered_map<std::string, std::string> sparse_grad_name_to_param_name;
auto sparse_grad_name_to_param_name_str =
Attr<std::vector<std::string>>(kSparseGradToParam);
for (const auto &sparse_grad_name_and_param_name :
sparse_grad_name_to_param_name_str) {
std::vector<std::string> pieces;
split(sparse_grad_name_and_param_name, ':', &pieces);
PADDLE_ENFORCE_EQ(pieces.size(), 2);
VLOG(3) << "after split, sparse_grad_name = " << pieces[0]
<< ", param_name = " << pieces[1];
sparse_grad_name_to_param_name[pieces[0]] = pieces[1];
}
auto f = std::bind(
FillRequestCtx, std::placeholders::_1, &recv_scope, &dev_ctx, &executor,
program, &prefetch_var_name_to_prepared_ctx,
&sparse_grad_name_to_param_name, ckpt_pre_context, rpc_service_.get());
f(request_send_handler_.get());
f(request_get_handler_.get());
......@@ -445,6 +464,8 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
RunSyncLoop(&executor, program, &recv_scope, &dev_ctx,
prefetch_block_id_list, checkpoint_block_id);
} else {
distributed::AsyncSparseParamUpdateRecorder::Init(
fan_in, sparse_grad_name_to_param_name);
RunAsyncLoop(&executor, program, &recv_scope);
}
}
......@@ -475,6 +496,10 @@ class ListenAndServOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<std::vector<std::string>>(kPrefetchVarNameToBlockId,
"prefetch blocks to run on server side.")
.SetDefault({});
AddAttr<std::vector<std::string>>(
kSparseGradToParam,
"sparse grad name to param name. like: 'emb@Grad:emb'")
.SetDefault({});
AddAttr<int>("Fanin", "How many clients send to this server.")
.SetDefault(1);
AddAttr<int>(kCheckpointBlockId,
......
paddle/fluid/operators/distributed_ops/listen_and_serv_op.h
浏览文件 @ 431eab64
......@@ -16,8 +16,10 @@ limitations under the License. */
#include <stdint.h>
#include <atomic>
#include <memory>
#include <set>
#include <string>
#include <unordered_map>
#include <utility>
#include <vector>
......@@ -35,6 +37,7 @@ namespace operators {
constexpr char kOptimizeBlocks[] = "optimize_blocks";
constexpr char kPrefetchVarNameToBlockId[] = "prefetch_var_name_to_block_id";
constexpr char kCheckpointBlockId[] = "checkpint_block_id";
constexpr char kSparseGradToParam[] = "sparse_grad_to_param";
void RunServer(std::shared_ptr<distributed::RPCServer> service);
......
paddle/fluid/operators/distributed_ops/recv_op.cc
浏览文件 @ 431eab64
......@@ -50,17 +50,18 @@ class RecvOp : public framework::OperatorBase {
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &ctx = *pool.Get(place);
auto trainer_id = Attr<int>("trainer_id");
distributed::RPCClient *rpc_client =
distributed::RPCClient::GetInstance<RPCCLIENT_T>(
Attr<int>("trainer_id"));
distributed::RPCClient::GetInstance<RPCCLIENT_T>(trainer_id);
std::vector<std::string> recv_varnames =
Attr<std::vector<std::string>>("recv_varnames");
if (recv_varnames.size() > 0) {
auto recv_functor = distributed::ParameterRecv<float>();
auto rpc_ctx = distributed::RpcContext(outs[0], recv_varnames, epmap, {});
auto rpc_ctx = distributed::RpcContext(outs[0], recv_varnames, epmap, {},
trainer_id);
recv_functor(rpc_ctx, scope);
} else {
if (with_barrier) {
......
paddle/fluid/operators/distributed_ops/send_op.cc
浏览文件 @ 431eab64
......@@ -42,6 +42,7 @@ class SendOp : public framework::OperatorBase {
auto epmap = Attr<std::vector<std::string>>("epmap");
int sync_send = Attr<int>("sync_mode");
auto trainer_id = Attr<int>("trainer_id");
auto send_varnames = Attr<std::vector<std::string>>("send_varnames");
auto height_sections = Attr<std::vector<int64_t>>("sections");
......@@ -51,7 +52,7 @@ class SendOp : public framework::OperatorBase {
if (distributed::Communicator::GetInstance() == nullptr) {
auto send_functor = distributed::ParameterSend<float>();
auto rpc_ctx = distributed::RpcContext(ins[0], send_varnames, epmap,
height_sections);
height_sections, trainer_id);
send_functor(rpc_ctx, scope, true);
} else {
distributed::Communicator::GetInstance()->Send(ins[0], scope);
......@@ -62,8 +63,7 @@ class SendOp : public framework::OperatorBase {
auto& ctx = *pool.Get(place);
distributed::RPCClient* rpc_client =
distributed::RPCClient::GetInstance<RPCCLIENT_T>(
Attr<int>("trainer_id"));
distributed::RPCClient::GetInstance<RPCCLIENT_T>(trainer_id);
std::vector<distributed::VarHandlePtr> rets;
for (size_t i = 0; i < ins.size(); i++) {
......
paddle/fluid/operators/distributed_ops/split_byref_op.cc
浏览文件 @ 431eab64
......@@ -31,14 +31,16 @@ class SplitByrefOp : public framework::OperatorWithKernel {
auto in_dims = ctx->GetInputDim("X");
auto outs_names = ctx->Outputs("Out");
size_t num = static_cast<size_t>(ctx->Attrs().Get<int>("num"));
std::vector<int> sections = static_cast<std::vector<int>>(
ctx->Attrs().Get<std::vector<int>>("sections"));
auto sections = ctx->Attrs().Get<std::vector<int>>("sections");
const size_t outs_number = outs_names.size();
std::vector<framework::DDim> outs_dims;
outs_dims.reserve(outs_number);
if (num > 0) {
int64_t in_axis_dim = in_dims[0];
int64_t in_axis_dim = 0;
if (ctx->IsRuntime()) {
in_axis_dim = in_dims[0];
}
PADDLE_ENFORCE_EQ(in_axis_dim % num, 0,
"tensor split does not result"
" in an equal division");
......
paddle/fluid/operators/fake_quantize_op.cu
浏览文件 @ 431eab64
......@@ -235,11 +235,13 @@ struct FindRangeAbsMaxFunctor<platform::CUDADeviceContext, T> {
int g_find_max;
memory::Copy(platform::CPUPlace(), &g_find_max, gpu_place, find_max,
sizeof(int), 0);
sizeof(int), ctx.stream());
ctx.Wait();
if (g_find_max) {
int len;
memory::Copy(platform::CPUPlace(), &len, gpu_place, out_size_data,
sizeof(int), 0);
sizeof(int), ctx.stream());
ctx.Wait();
FindAbsMaxFunctor<platform::CUDADeviceContext, T>()(ctx, scale_arr, len,
out_scale_data);
}
......@@ -258,25 +260,26 @@ struct FindMovingAverageAbsMaxFunctor<platform::CUDADeviceContext, T> {
const auto gpu_place = boost::get<platform::CUDAPlace>(ctx.GetPlace());
T accum;
memory::Copy(platform::CPUPlace(), &accum, gpu_place, in_accum.data<T>(),
sizeof(T), 0);
T state;
memory::Copy(platform::CPUPlace(), &state, gpu_place, in_state.data<T>(),
sizeof(T), 0);
T scale;
memory::Copy(platform::CPUPlace(), &accum, gpu_place, in_accum.data<T>(),
sizeof(T), ctx.stream());
memory::Copy(platform::CPUPlace(), &state, gpu_place, in_state.data<T>(),
sizeof(T), ctx.stream());
memory::Copy(platform::CPUPlace(), &scale, gpu_place, cur_scale, sizeof(T),
0);
ctx.stream());
ctx.Wait();
state = rate * state + 1;
accum = rate * accum + scale;
scale = accum / state;
memory::Copy(gpu_place, out_accum->mutable_data<T>(gpu_place),
platform::CPUPlace(), &accum, sizeof(T), 0);
platform::CPUPlace(), &accum, sizeof(T), ctx.stream());
memory::Copy(gpu_place, out_state->mutable_data<T>(gpu_place),
platform::CPUPlace(), &state, sizeof(T), 0);
platform::CPUPlace(), &state, sizeof(T), ctx.stream());
memory::Copy(gpu_place, out_scale->mutable_data<T>(gpu_place),
platform::CPUPlace(), &scale, sizeof(T), 0);
platform::CPUPlace(), &scale, sizeof(T), ctx.stream());
ctx.Wait();
}
};
......
paddle/fluid/operators/grid_sampler_op.cc
浏览文件 @ 431eab64
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/grid_sampler_op.h"
#include <memory>
#include "paddle/fluid/framework/op_registry.h"
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/cudnn_helper.h"
......@@ -40,10 +41,12 @@ class GridSampleOp : public framework::OperatorWithKernel {
"Input(X) of GridSampleOp should be 4-D Tensor.");
PADDLE_ENFORCE(grid_dims.size() == 4,
"Input(Grid) of GridSampleOp should be 4-D Tensor.");
PADDLE_ENFORCE(grid_dims[3] == 2, "Input(Grid) dims[3] should be 2.");
PADDLE_ENFORCE_EQ(grid_dims[0], x_dims[0],
"Input(X) and Input(Grid) dims[0] should be equal.");
if (ctx->IsRuntime() || grid_dims[3] > 0) {
PADDLE_ENFORCE(grid_dims[3] == 2, "Input(Grid) dims[3] should be 2.");
}
if (ctx->IsRuntime()) {
PADDLE_ENFORCE_EQ(grid_dims[0], x_dims[0],
"Input(X) and Input(Grid) dims[0] should be equal.");
PADDLE_ENFORCE_EQ(
grid_dims[1], x_dims[2],
"Input(X) dims[2] and Input(Grid) dims[1] should be equal.");
......
paddle/fluid/operators/hierarchical_sigmoid_op.h
浏览文件 @ 431eab64
......@@ -238,6 +238,8 @@ class HierarchicalSigmoidGradOpKernel : public framework::OpKernel<T> {
zero(dev_ctx, w_grad, static_cast<T>(0.0));
bit_code->MulGradWeight(pre_out_grad, w_grad, in);
} else {
PADDLE_ENFORCE(path != nullptr,
"Sparse mode should not be used without custom tree!");
framework::Vector<int64_t> real_rows = PathToRows(*path);
auto* w_grad =
ctx.Output<framework::SelectedRows>(framework::GradVarName("W"));
......
paddle/fluid/operators/interpolate_op.cc
浏览文件 @ 431eab64
......@@ -45,9 +45,14 @@ class InterpolateOp : public framework::OperatorWithKernel {
// round down
out_h = static_cast<int>(dim_x[2] * scale);
out_w = static_cast<int>(dim_x[3] * scale);
// protect when input shape is -1
out_h = out_h > 0 ? out_h : -1;
out_w = out_w > 0 ? out_w : -1;
} else {
out_h = ctx->Attrs().Get<int>("out_h");
out_w = ctx->Attrs().Get<int>("out_w");
PADDLE_ENFORCE_GT(out_h, 0, "out_h should be greater than 0.");
PADDLE_ENFORCE_GT(out_w, 0, "out_w should be greater than 0.");
}
if (ctx->HasInput("OutSize") && ctx->IsRuntime()) {
......@@ -58,6 +63,7 @@ class InterpolateOp : public framework::OperatorWithKernel {
ctx->ShareLoD("X", "Out");
return;
}
std::vector<int64_t> dim_out({dim_x[0], dim_x[1], out_h, out_w});
ctx->SetOutputDim("Out", framework::make_ddim(dim_out));
}
......
paddle/fluid/operators/kldiv_loss_op.cc
浏览文件 @ 431eab64
......@@ -35,8 +35,10 @@ class KLDivLossOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ(dim_x.size(), dim_target.size(),
"Input(X) rank and Input(Target) rank should be same.");
for (int i = 0; i < dim_x.size(); i++) {
PADDLE_ENFORCE_EQ(dim_x[i], dim_target[i],
"Input(X) and Input(Target) should in same shape.");
if (ctx->IsRuntime() || (dim_x[i] > 0 && dim_target[i] > 0)) {
PADDLE_ENFORCE_EQ(dim_x[i], dim_target[i],
"Input(X) and Input(Target) should in same shape.");
}
}
auto reduction = ctx->Attrs().Get<std::string>("reduction");
......
paddle/fluid/operators/lod_reset_op.cc
浏览文件 @ 431eab64
......@@ -30,10 +30,10 @@ class LoDResetOp : public framework::OperatorWithKernel {
if (!ctx->HasInput("Y")) {
auto level0 = ctx->Attrs().Get<std::vector<int>>("target_lod");
PADDLE_ENFORCE_GT(level0.size(), 1,
PADDLE_ENFORCE_GT(level0.size(), 0,
"If Input(Y) not provided, the target lod should be "
"specified by attribute `target_lod`.");
} else {
} else if (ctx->IsRuntime()) {
ctx->ShareLoD("Y", "Out");
}
......@@ -48,6 +48,23 @@ class LoDResetOp : public framework::OperatorWithKernel {
}
};
class LoDResetOpVarTypeInference : public framework::VarTypeInference {
public:
void operator()(framework::InferVarTypeContext *ctx) const override {
auto x_var_name = ctx->Input("X").front();
auto out_var_name = ctx->Output("Out").front();
if (ctx->HasInput("Y")) {
auto y_var_name = ctx->Input("Y").front();
auto y_lod_level = std::max(ctx->GetLoDLevel(y_var_name), 1);
ctx->SetLoDLevel(out_var_name, y_lod_level);
} else {
ctx->SetLoDLevel(out_var_name, 1);
}
ctx->SetDataType(out_var_name, ctx->GetDataType(x_var_name));
ctx->SetType(out_var_name, paddle::framework::proto::VarType::LOD_TENSOR);
}
};
class LoDResetOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
......@@ -177,9 +194,10 @@ DECLARE_NO_NEED_BUFFER_VARS_INFERENCE(LoDResetGradNoNeedBufferVarInference,
namespace ops = paddle::operators;
REGISTER_OPERATOR(lod_reset, ops::LoDResetOp, ops::LoDResetOpMaker,
ops::LoDResetGradDescMaker);
ops::LoDResetGradDescMaker, ops::LoDResetOpVarTypeInference);
REGISTER_OPERATOR(lod_reset_grad, ops::LoDResetGradOp,
ops::LoDResetGradNoNeedBufferVarInference);
REGISTER_OP_CPU_KERNEL(
lod_reset, ops::LoDResetKernel<paddle::platform::CPUPlace, float>,
ops::LoDResetKernel<paddle::platform::CPUPlace, double>,
......
paddle/fluid/operators/lod_reset_op.h
浏览文件 @ 431eab64
......@@ -63,7 +63,7 @@ class LoDResetKernel : public framework::OpKernel<T> {
"Target LoD should be a vector end with the "
"first dimension of Input(X).");
for (size_t i = 0; i < level0.size() - 1; ++i) {
PADDLE_ENFORCE(level0[i + 1] > level0[i],
PADDLE_ENFORCE(level0[i + 1] >= level0[i],
"Target LoD should be an ascending vector.");
}
......
paddle/fluid/operators/lstmp_op.cc
浏览文件 @ 431eab64
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/lstmp_op.h"
#include <memory>
#include <string>
namespace paddle {
......@@ -45,6 +46,7 @@ class LSTMPOp : public framework::OperatorWithKernel {
"Output(BatchHidden) of LSTMP operator should not be null.");
auto in_dims = ctx->GetInputDim("Input");
PADDLE_ENFORCE_EQ(in_dims.size(), 2,
"Input(X)'s rank of LSTMP operator must be 2.");
......@@ -269,13 +271,47 @@ Users can choose to use fully-connected operator before LSTMP operator.
}
};
class LSTMPGradMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
auto* grad_op = new framework::OpDesc();
grad_op->SetType("lstmp_grad");
grad_op->SetInput("Weight", Input("Weight"));
grad_op->SetInput("ProjWeight", Input("ProjWeight"));
grad_op->SetInput("Bias", Input("Bias"));
grad_op->SetInput("Projection", Output("Projection"));
grad_op->SetInput("Cell", Output("Cell"));
grad_op->SetInput("BatchGate", Output("BatchGate"));
grad_op->SetInput("BatchCellPreAct", Output("BatchCellPreAct"));
grad_op->SetInput("BatchHidden", Output("BatchHidden"));
grad_op->SetInput("H0", Input("H0"));
grad_op->SetInput("C0", Input("C0"));
grad_op->SetInput(framework::GradVarName("Projection"),
OutputGrad("Projection"));
grad_op->SetOutput(framework::GradVarName("Input"), InputGrad("Input"));
grad_op->SetOutput(framework::GradVarName("Weight"), InputGrad("Weight"));
grad_op->SetOutput(framework::GradVarName("ProjWeight"),
InputGrad("ProjWeight"));
grad_op->SetOutput(framework::GradVarName("Bias"), InputGrad("Bias"));
grad_op->SetOutput(framework::GradVarName("H0"), InputGrad("H0"));
grad_op->SetOutput(framework::GradVarName("C0"), InputGrad("C0"));
grad_op->SetAttrMap(Attrs());
return std::unique_ptr<framework::OpDesc>(grad_op);
}
};
class LSTMPGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Input"),
"Input(Input) of LSTMP operator should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Projection"),
"Input(Projection) of LSTMP operator should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Cell"),
......@@ -298,7 +334,8 @@ class LSTMPGradOp : public framework::OperatorWithKernel {
ctx->SetOutputDim(g_name, ctx->GetInputDim(name));
};
SetOutGradDim("Input");
ctx->SetOutputDim(framework::GradVarName("Input"),
ctx->GetInputDim("BatchGate"));
SetOutGradDim("Weight");
SetOutGradDim("ProjWeight");
SetOutGradDim("Bias");
......@@ -310,7 +347,8 @@ class LSTMPGradOp : public framework::OperatorWithKernel {
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
ctx.Input<framework::LoDTensor>("Input")->type(), ctx.device_context());
ctx.Input<framework::LoDTensor>("BatchGate")->type(),
ctx.device_context());
}
};
......@@ -318,8 +356,7 @@ class LSTMPGradOp : public framework::OperatorWithKernel {
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(lstmp, ops::LSTMPOp, ops::LSTMPOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(lstmp, ops::LSTMPOp, ops::LSTMPOpMaker, ops::LSTMPGradMaker);
REGISTER_OPERATOR(lstmp_grad, ops::LSTMPGradOp);
REGISTER_OP_CPU_KERNEL(
lstmp, ops::LSTMPKernel<paddle::platform::CPUDeviceContext, float>,
......
paddle/fluid/operators/lstmp_op.h
浏览文件 @ 431eab64
......@@ -267,7 +267,6 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
}
void Compute(const framework::ExecutionContext& ctx) const override {
auto* input = ctx.Input<LoDTensor>("Input");
auto* weight = ctx.Input<Tensor>("Weight");
auto* proj_weight = ctx.Input<Tensor>("ProjWeight");
auto* bias = ctx.Input<Tensor>("Bias");
......@@ -323,7 +322,8 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
ordered_c0_g.mutable_data<T>(c0_g->dims(), ctx.GetPlace());
}
auto in_dims = input->dims();
// batch_gate dims equal to input dims
auto in_dims = batch_gate->dims();
auto out_dims = cell_out->dims();
framework::DDim proj_dims({in_dims[0], proj_weight->dims()[1]});
int frame_size = static_cast<int>(in_dims[1] / 4);
......
paddle/fluid/operators/math/selected_rows_functor.cc
浏览文件 @ 431eab64
......@@ -296,6 +296,7 @@ struct MergeAdd<platform::CPUDeviceContext, T> {
auto input_height = has_value_input->height();
framework::SelectedRows& out = *output;
std::set<int64_t> merged_row_set;
size_t row_num = 0;
for (auto* input : inputs) {
if (input->rows().size() == 0) {
continue;
......@@ -305,42 +306,71 @@ struct MergeAdd<platform::CPUDeviceContext, T> {
"dimension except for the first one");
PADDLE_ENFORCE_EQ(input_height, input->height(),
"all input should have same height");
row_num += input->rows().size();
merged_row_set.insert(input->rows().begin(), input->rows().end());
}
std::vector<int64_t> merge_rows(merged_row_set.begin(),
merged_row_set.end());
if (sorted_result) {
std::sort(merge_rows.begin(), merge_rows.end());
}
std::unordered_map<int64_t, size_t> rows_to_id;
for (size_t i = 0; i < merge_rows.size(); ++i) {
rows_to_id[merge_rows[i]] = i;
}
out.set_rows(merge_rows);
out.set_height(input_height);
out.mutable_value()->mutable_data<T>(
framework::make_ddim(
{static_cast<int64_t>(merge_rows.size()), input_width}),
{static_cast<int64_t>(merged_row_set.size()), input_width}),
context.GetPlace());
auto* out_data = out.mutable_value()->data<T>();
math::SetConstant<platform::CPUDeviceContext, T> constant_functor;
constant_functor(context, out.mutable_value(), 0.0);
if (merged_row_set.size() == row_num && !sorted_result) {
// no duplicated ids, just concat the result together
std::vector<int64_t> merge_rows;
merge_rows.reserve(row_num);
// concat rows
for (auto* in : inputs) {
merge_rows.insert(merge_rows.end(), in->rows().begin(),
in->rows().end());
}
out.set_rows(merge_rows);
auto in_place = inputs[0]->place();
auto out_place = out.place();
int64_t copied_numel = 0;
for (auto* in : inputs) {
auto* in_data = in->value().data<T>();
auto in_numel = in->value().numel();
memory::Copy(boost::get<platform::CPUPlace>(out_place),
out_data + copied_numel,
boost::get<platform::CPUPlace>(in_place), in_data,
in_numel * sizeof(T));
copied_numel += in_numel;
}
} else {
std::vector<int64_t> merge_rows(merged_row_set.begin(),
merged_row_set.end());
auto* out_data = out.mutable_value()->data<T>();
if (sorted_result) {
std::sort(merge_rows.begin(), merge_rows.end());
}
auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
for (auto* input : inputs) {
if (input->rows().size() == 0) {
continue;
out.set_rows(merge_rows);
math::SetConstant<platform::CPUDeviceContext, T> constant_functor;
constant_functor(context, out.mutable_value(), 0.0);
std::unordered_map<int64_t, size_t> rows_to_id;
for (size_t i = 0; i < merge_rows.size(); ++i) {
rows_to_id[merge_rows[i]] = i;
}
auto* input_data = input->value().data<T>();
auto& input_rows = input->rows();
for (size_t i = 0; i < input_rows.size(); i++) {
size_t out_i = rows_to_id[input_rows[i]];
elementwise_add_to<platform::CPUDeviceContext, T>(
context, &blas, static_cast<size_t>(input_width),
&input_data[i * input_width], &out_data[out_i * input_width]);
auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
for (auto* input : inputs) {
if (input->rows().size() == 0) {
continue;
}
auto* input_data = input->value().data<T>();
auto& input_rows = input->rows();
for (size_t i = 0; i < input_rows.size(); i++) {
size_t out_i = rows_to_id[input_rows[i]];
elementwise_add_to<platform::CPUDeviceContext, T>(
context, &blas, static_cast<size_t>(input_width),
&input_data[i * input_width], &out_data[out_i * input_width]);
}
}
}
}
......
paddle/fluid/operators/math/selected_rows_functor_test.cc
浏览文件 @ 431eab64
......@@ -13,8 +13,11 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/math/selected_rows_functor.h"
#include <memory>
#include <vector>
#include "gtest/gtest.h"
#include "paddle/fluid/operators/math/math_function.h"
TEST(selected_rows_functor, cpu_add) {
......@@ -360,6 +363,69 @@ TEST(selected_rows_functor, cpu_merge_add_multi) {
}
}
TEST(selected_rows_functor, cpu_merge_add_multi_noduplicated) {
paddle::platform::CPUPlace cpu_place;
paddle::platform::CPUDeviceContext ctx(cpu_place);
paddle::operators::math::SetConstant<paddle::platform::CPUDeviceContext,
float>
set_const;
int64_t height = 10;
int64_t row_numel = 8;
std::vector<int64_t> rows1{1, 3, 5, 7, 9};
std::unique_ptr<paddle::framework::SelectedRows> selected_rows1{
new paddle::framework::SelectedRows(rows1, height)};
auto* in1_value = selected_rows1->mutable_value();
in1_value->mutable_data<float>(
paddle::framework::make_ddim(
{static_cast<int64_t>(rows1.size()), row_numel}),
cpu_place);
set_const(ctx, in1_value, 1.0);
std::vector<int64_t> rows2{0, 2, 4, 6, 8};
std::unique_ptr<paddle::framework::SelectedRows> selected_rows2{
new paddle::framework::SelectedRows(rows2, height)};
auto* in2_value = selected_rows2->mutable_value();
in2_value->mutable_data<float>(
paddle::framework::make_ddim(
{static_cast<int64_t>(rows2.size()), row_numel}),
cpu_place);
set_const(ctx, in2_value, 2.0);
std::unique_ptr<paddle::framework::SelectedRows> output{
new paddle::framework::SelectedRows()};
output->set_height(height);
paddle::operators::math::scatter::MergeAdd<paddle::platform::CPUDeviceContext,
float>
merge_add_functor;
std::vector<const paddle::framework::SelectedRows*> inputs;
inputs.push_back(selected_rows1.get());
inputs.push_back(selected_rows2.get());
merge_add_functor(ctx, inputs, output.get());
EXPECT_EQ(output->height(), height);
EXPECT_EQ(output->value().dims(),
paddle::framework::make_ddim({10, row_numel}));
std::vector<int64_t> ret_rows{1, 3, 5, 7, 9, 0, 2, 4, 6, 8};
EXPECT_EQ(output->rows(), ret_rows);
auto* out_data = output->value().data<float>();
for (size_t i = 0; i < ret_rows.size(); ++i) {
float data_value = 0;
if (i < 5) {
data_value = 1.0;
} else {
data_value = 2.0;
}
for (size_t j = 0; j < static_cast<size_t>(row_numel); ++j) {
EXPECT_EQ(out_data[i * row_numel + j], data_value);
}
}
}
TEST(selected_rows_functor, cpu_sum_to) {
paddle::platform::CPUPlace cpu_place;
paddle::platform::CPUDeviceContext ctx(cpu_place);
......
paddle/fluid/operators/merge_lod_tensor_op.cc
浏览文件 @ 431eab64
......@@ -164,7 +164,9 @@ class MergeLoDTensorInferShape : public framework::InferShapeBase {
auto mask_dim = context->GetInputDim("Mask");
PADDLE_ENFORCE_EQ(mask_dim.size(), 2);
PADDLE_ENFORCE_EQ(mask_dim[1], 1);
if (context->IsRuntime() || mask_dim[1] > 0) {
PADDLE_ENFORCE_EQ(mask_dim[1], 1);
}
context->SetOutputDim("Out", context->GetInputDim("InTrue"));
}
......
paddle/fluid/operators/mkldnn/batch_norm_mkldnn_op.cc
浏览文件 @ 431eab64
......@@ -39,13 +39,9 @@ struct bn_type_traits {
class BatchNormMKLDNNHandler : public platform::MKLDNNHandler {
public:
BatchNormMKLDNNHandler(
std::shared_ptr<batch_norm_fwd::primitive_desc> batch_norm_pd,
const platform::MKLDNNDeviceContext &dev_ctx, mkldnn::engine engine,
const std::string &base_key)
: platform::MKLDNNHandler(dev_ctx, engine, base_key) {
batch_norm_pd_ = batch_norm_pd;
}
BatchNormMKLDNNHandler(const platform::MKLDNNDeviceContext &dev_ctx,
mkldnn::engine engine, const std::string &base_key)
: platform::MKLDNNHandler(dev_ctx, engine, base_key) {}
std::shared_ptr<memory> AcquireScaleshiftMemoryFromPrimitive(void *ptr) {
return this->AcquireMemoryFromPrimitive(
......@@ -62,6 +58,26 @@ class BatchNormMKLDNNHandler : public platform::MKLDNNHandler {
batch_norm_pd_->variance_primitive_desc(), ptr, "@variance_mem_p");
}
std::shared_ptr<batch_norm_fwd::primitive_desc>
AcquireBatchNormPrimitiveDescriptor(const batch_norm_fwd::desc &bn_fwd_desc,
const mkldnn::engine &engine) {
const std::string key_batch_norm_fwd_pd = key_ + "@bn_fwd_pd";
auto batch_norm_pd =
std::static_pointer_cast<batch_norm_fwd::primitive_desc>(
dev_ctx_.GetBlob(key_batch_norm_fwd_pd));
if (batch_norm_pd == nullptr) {
batch_norm_pd_.reset(
new batch_norm_fwd::primitive_desc(bn_fwd_desc, engine));
dev_ctx_.SetBlob(key_batch_norm_fwd_pd, batch_norm_pd_);
} else {
batch_norm_pd_ = batch_norm_pd;
is_reusing_ = true;
}
return batch_norm_pd_;
}
std::shared_ptr<batch_norm_fwd> AcquireTestTrainingBatchNormFwd(
std::shared_ptr<memory> src_memory,
std::shared_ptr<memory> scaleshift_memory,
......@@ -213,7 +229,7 @@ class BatchNormMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
const std::string key = BatchNormMKLDNNHandler::GetHash(
src_tz, epsilon, flags, global_stats, input_format,
ctx.op().Output("SavedMean"));
const std::string key_batch_norm_fwd_pd = key + "@bn_fwd_pd";
BatchNormMKLDNNHandler handler(dev_ctx, mkldnn_engine, key);
auto user_src_md = platform::MKLDNNMemDesc(
{src_tz}, platform::MKLDNNGetDataType<T>(), input_format);
......@@ -222,13 +238,9 @@ class BatchNormMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
using bn_fwd_types = bn_type_traits<mkldnn::batch_normalization_forward>;
auto batch_norm_fwd_desc =
bn_fwd_types::op_desc{propagation, user_src_md, epsilon, flags};
auto batch_norm_fwd_pd = std::make_shared<batch_norm_fwd::primitive_desc>(
batch_norm_fwd_desc, mkldnn_engine);
// Save conv_pd/src_memory/weights_memory for backward pass
dev_ctx.SetBlob(key_batch_norm_fwd_pd, batch_norm_fwd_pd);
BatchNormMKLDNNHandler handler(batch_norm_fwd_pd, dev_ctx, mkldnn_engine,
key);
auto batch_norm_fwd_pd = handler.AcquireBatchNormPrimitiveDescriptor(
batch_norm_fwd_desc, mkldnn_engine);
auto src_memory =
handler.AcquireSrcMemory(user_src_md, to_void_cast(x_data));
......
paddle/fluid/operators/mkldnn/conv_mkldnn_op.cc
浏览文件 @ 431eab64
......@@ -144,7 +144,6 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
const std::string key = platform::ConvMKLDNNHandler::GetHash(
src_tz, weights_tz, strides, paddings, dilations, groups,
ctx.op().Input("Input") + ctx.op().Input("Filter"));
const std::string key_conv_pd = key + "@conv_pd";
std::vector<primitive> pipeline;
......@@ -183,6 +182,8 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
auto dst_md = platform::MKLDNNMemDesc(
dst_tz, platform::MKLDNNGetDataType<T>(), chosen_memory_format);
platform::ConvMKLDNNHandler handler(dev_ctx, mkldnn_engine, key);
// create a conv primitive descriptor and save it for usage in backward
std::shared_ptr<mkldnn::convolution_forward::primitive_desc> conv_pd;
auto fwd_prop_kind = is_test ? mkldnn::prop_kind::forward_inference
......@@ -191,18 +192,14 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
bias_tz = paddle::framework::vectorize2int(bias->dims());
auto bias_md = platform::MKLDNNMemDesc(
bias_tz, platform::MKLDNNGetDataType<T>(), memory::format::x);
conv_pd = ConvFwdPrimitiveDesc(
conv_pd = handler.AcquireConvolutionPrimitiveDescriptor(
src_md, weights_md, bias_md, dst_md, strides, paddings, mkldnn_engine,
fuse_relu, fuse_residual_conn, fwd_prop_kind);
} else {
conv_pd = ConvFwdPrimitiveDesc(src_md, weights_md, dst_md, strides,
paddings, mkldnn_engine, fuse_relu,
fuse_residual_conn, fwd_prop_kind);
conv_pd = handler.AcquireConvolutionPrimitiveDescriptor(
src_md, weights_md, boost::none, dst_md, strides, paddings,
mkldnn_engine, fuse_relu, fuse_residual_conn, fwd_prop_kind);
}
// Save conv_pd/src_memory/weights_memory for backward pass
if (!is_test) dev_ctx.SetBlob(key_conv_pd, conv_pd);
platform::ConvMKLDNNHandler handler(conv_pd, dev_ctx, mkldnn_engine, key);
// create mkldnn memory from input tensors (data/weights)
auto user_src_memory_p =
......@@ -633,31 +630,6 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
}
private:
mkldnn::primitive_attr CreatePostOps(bool fuse_relu,
bool fuse_residual_conn) const {
mkldnn::primitive_attr conv_attr;
mkldnn::post_ops post_operations;
// Fusion with Elementwise layer relies on adding a sum post-operation with
// the scale parameter. It is assumed that when fuse_residual_connection is
// true, the output tensor contains the data coming from residual
// connection. The result of this post_op is:
// Output = scale * Output + Conv_Out.
if (fuse_residual_conn) {
post_operations.append_sum(1.0f);
}
// Fusion with ReLU layer is executed through the PostOps feature. Create a
// PostOps object and configure it to execute an eltwise relu operation.
if (fuse_relu) {
constexpr float scale = 1.0f;
constexpr float negative_slope = 0.0f;
constexpr float placeholder = 0.0f;
post_operations.append_eltwise(scale, mkldnn::algorithm::eltwise_relu,
negative_slope, placeholder);
}
conv_attr.set_post_ops(post_operations);
return conv_attr;
}
mkldnn::primitive_attr CreatePostOps(
bool fuse_relu, bool fuse_residual_conn,
const std::vector<float> output_shift_scale, float sum_scale) const {
......@@ -679,30 +651,6 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
return conv_attr;
}
std::unique_ptr<mkldnn::convolution_forward::primitive_desc>
ConvFwdPrimitiveDesc(const memory::desc& src, const memory::desc& weights,
const memory::desc& dst, const std::vector<int>& strides,
const std::vector<int>& paddings,
const mkldnn::engine& engine, const bool fuse_relu,
const bool fuse_residual_conn,
mkldnn::prop_kind fwd_prop_kind) const {
memory::dims stride_dims = strides;
memory::dims padding_dims = paddings;
auto conv_desc = mkldnn::convolution_forward::desc(
fwd_prop_kind, mkldnn::convolution_direct, src, weights, dst,
stride_dims, padding_dims, padding_dims, mkldnn::padding_kind::zero);
mkldnn::primitive_attr conv_attr =
CreatePostOps(fuse_relu, fuse_residual_conn);
auto p_conv_pd = new mkldnn::convolution_forward::primitive_desc(
conv_desc, conv_attr, engine);
return std::unique_ptr<mkldnn::convolution_forward::primitive_desc>(
p_conv_pd);
}
std::unique_ptr<mkldnn::convolution_forward::primitive_desc>
ConvFwdPrimitiveDesc(const memory::desc& src, const memory::desc& weights,
const memory::desc& dst, const std::vector<int>& strides,
......@@ -731,31 +679,6 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
p_conv_pd);
}
std::unique_ptr<mkldnn::convolution_forward::primitive_desc>
ConvFwdPrimitiveDesc(const memory::desc& src, const memory::desc& weights,
const memory::desc& bias, const memory::desc& dst,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const mkldnn::engine& engine, const bool fuse_relu,
const bool fuse_residual_conn,
mkldnn::prop_kind fwd_prop_kind) const {
memory::dims stride_dims = strides;
memory::dims padding_dims = paddings;
auto conv_desc = mkldnn::convolution_forward::desc(
fwd_prop_kind, mkldnn::convolution_direct, src, weights, bias, dst,
stride_dims, padding_dims, padding_dims, mkldnn::padding_kind::zero);
mkldnn::primitive_attr conv_attr =
CreatePostOps(fuse_relu, fuse_residual_conn);
auto p_conv_pd = new mkldnn::convolution_forward::primitive_desc(
conv_desc, conv_attr, engine);
return std::unique_ptr<mkldnn::convolution_forward::primitive_desc>(
p_conv_pd);
}
std::unique_ptr<mkldnn::convolution_forward::primitive_desc>
ConvFwdPrimitiveDesc(const memory::desc& src, const memory::desc& weights,
const memory::desc& bias, const memory::desc& dst,
......
paddle/fluid/operators/mkldnn/conv_transpose_mkldnn_op.cc
浏览文件 @ 431eab64
......@@ -12,6 +12,7 @@
See the License for the specific language governing permissions and
limitations under the License. */
#include "boost/optional.hpp"
#include "paddle/fluid/framework/data_layout_transform.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/memory/malloc.h"
......@@ -124,7 +125,6 @@ class ConvTransposeMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
const std::string key = platform::ConvTransposeMKLDNNHandler::GetHash(
src_tz, weights_tz, strides, paddings, dilations, groups,
ctx.op().Output("Output"));
const std::string key_conv_transpose_pd = key + "@conv_transpose_pd";
std::vector<mkldnn::primitive> pipeline;
......@@ -153,6 +153,7 @@ class ConvTransposeMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
auto dst_md = platform::MKLDNNMemDesc(
dst_tz, platform::MKLDNNGetDataType<T>(), chosen_memory_format);
platform::ConvTransposeMKLDNNHandler handler(dev_ctx, mkldnn_engine, key);
// create a deconv(conv transpose) primitive descriptor and save it for
// usage in backward
std::shared_ptr<mkldnn::deconvolution_forward::primitive_desc>
......@@ -163,19 +164,14 @@ class ConvTransposeMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
bias_tz = paddle::framework::vectorize2int(bias->dims());
auto bias_md = platform::MKLDNNMemDesc(
bias_tz, platform::MKLDNNGetDataType<T>(), mkldnn::memory::format::x);
conv_transpose_pd = ConvTransposeFwdPrimitiveDesc(
conv_transpose_pd = handler.AcquireConvolutionPrimitiveDescriptor(
src_md, weights_md, bias_md, dst_md, strides, paddings, mkldnn_engine,
fuse_relu, fwd_prop_kind);
fuse_relu, false, fwd_prop_kind);
} else {
conv_transpose_pd = ConvTransposeFwdPrimitiveDesc(
src_md, weights_md, dst_md, strides, paddings, mkldnn_engine,
fuse_relu, fwd_prop_kind);
conv_transpose_pd = handler.AcquireConvolutionPrimitiveDescriptor(
src_md, weights_md, boost::none, dst_md, strides, paddings,
mkldnn_engine, fuse_relu, false, fwd_prop_kind);
}
// Save conv_pd/src_memory/weights_memory for backward pass
if (!is_test) dev_ctx.SetBlob(key_conv_transpose_pd, conv_transpose_pd);
platform::ConvTransposeMKLDNNHandler handler(conv_transpose_pd, dev_ctx,
mkldnn_engine, key);
// create mkldnn memory from input tensors (data/weights)
auto user_src_memory_p = handler.AcquireSrcMemory(
......@@ -224,70 +220,6 @@ class ConvTransposeMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
output->set_layout(DataLayout::kMKLDNN);
output->set_format(platform::GetMKLDNNFormat(*dst_memory_p));
}
private:
mkldnn::primitive_attr CreatePostOps(bool fuse_relu) const {
mkldnn::primitive_attr conv_attr;
mkldnn::post_ops post_operations;
// Fusion with ReLU layer is executed through the PostOps feature. Create a
// PostOps object and configure it to execute an eltwise relu operation.
if (fuse_relu) {
constexpr float scale = 1.0f;
constexpr float negative_slope = 0.0f;
constexpr float placeholder = 0.0f;
post_operations.append_eltwise(scale, mkldnn::algorithm::eltwise_relu,
negative_slope, placeholder);
}
conv_attr.set_post_ops(post_operations);
return conv_attr;
}
std::unique_ptr<mkldnn::deconvolution_forward::primitive_desc>
ConvTransposeFwdPrimitiveDesc(
const mkldnn::memory::desc& src, const mkldnn::memory::desc& weights,
const mkldnn::memory::desc& dst, const std::vector<int>& strides,
const std::vector<int>& paddings, const mkldnn::engine& engine,
const bool fuse_relu, mkldnn::prop_kind fwd_prop_kind) const {
mkldnn::memory::dims stride_dims = {strides[0], strides[1]};
mkldnn::memory::dims padding_dims = {paddings[0], paddings[1]};
auto deconv_desc = mkldnn::deconvolution_forward::desc(
fwd_prop_kind, mkldnn::deconvolution_direct, src, weights, dst,
stride_dims, padding_dims, padding_dims, mkldnn::padding_kind::zero);
mkldnn::primitive_attr deconv_attr = CreatePostOps(fuse_relu);
auto p_conv_transpose_pd =
new mkldnn::deconvolution_forward::primitive_desc(deconv_desc,
deconv_attr, engine);
return std::unique_ptr<mkldnn::deconvolution_forward::primitive_desc>(
p_conv_transpose_pd);
}
std::unique_ptr<mkldnn::deconvolution_forward::primitive_desc>
ConvTransposeFwdPrimitiveDesc(
const mkldnn::memory::desc& src, const mkldnn::memory::desc& weights,
const mkldnn::memory::desc& bias, const mkldnn::memory::desc& dst,
const std::vector<int>& strides, const std::vector<int>& paddings,
const mkldnn::engine& engine, const bool fuse_relu,
mkldnn::prop_kind fwd_prop_kind) const {
mkldnn::memory::dims stride_dims = {strides[0], strides[1]};
mkldnn::memory::dims padding_dims = {paddings[0], paddings[1]};
auto deconv_desc = mkldnn::deconvolution_forward::desc(
fwd_prop_kind, mkldnn::deconvolution_direct, src, weights, bias, dst,
stride_dims, padding_dims, padding_dims, mkldnn::padding_kind::zero);
mkldnn::primitive_attr deconv_attr = CreatePostOps(fuse_relu);
auto p_conv_transpose_pd =
new mkldnn::deconvolution_forward::primitive_desc(deconv_desc,
deconv_attr, engine);
return std::unique_ptr<mkldnn::deconvolution_forward::primitive_desc>(
p_conv_transpose_pd);
}
};
} // namespace operators
......
paddle/fluid/operators/mkldnn/softmax_mkldnn_op.cc
浏览文件 @ 431eab64
......@@ -34,12 +34,9 @@ using platform::to_void_cast;
class SoftmaxMKLDNNHandler : public platform::MKLDNNHandler {
public:
SoftmaxMKLDNNHandler(
std::shared_ptr<mkldnn::softmax_forward::primitive_desc> softmax_pd,
const platform::MKLDNNDeviceContext& dev_ctx, mkldnn::engine engine,
const std::string& base_key)
: platform::MKLDNNHandler(dev_ctx, engine, base_key),
softmax_pd_(softmax_pd) {}
SoftmaxMKLDNNHandler(const platform::MKLDNNDeviceContext& dev_ctx,
mkldnn::engine engine, const std::string& base_key)
: platform::MKLDNNHandler(dev_ctx, engine, base_key) {}
SoftmaxMKLDNNHandler(
std::shared_ptr<mkldnn::softmax_forward::primitive_desc> softmax_pd,
......@@ -54,6 +51,26 @@ class SoftmaxMKLDNNHandler : public platform::MKLDNNHandler {
key_ += "-BWD";
}
std::shared_ptr<softmax_forward::primitive_desc>
AcquireSoftmaxPrimitiveDescriptor(const softmax_forward::desc& softmax_desc,
const mkldnn::engine& engine) {
const std::string key_softmax_pd = key_ + "@softmax_pd";
auto softmax_pd = std::static_pointer_cast<softmax_forward::primitive_desc>(
dev_ctx_.GetBlob(key_softmax_pd));
if (softmax_pd == nullptr) {
softmax_pd_.reset(
new softmax_forward::primitive_desc(softmax_desc, engine));
dev_ctx_.SetBlob(key_softmax_pd, softmax_pd_);
} else {
softmax_pd_ = softmax_pd;
is_reusing_ = true;
}
return softmax_pd_;
}
std::shared_ptr<mkldnn::softmax_forward> AcquireSoftmax(
std::shared_ptr<mkldnn::memory> dst_memory_p,
std::shared_ptr<mkldnn::memory> src_memory_p) {
......@@ -138,19 +155,18 @@ class SoftmaxMKLDNNKernel : public paddle::framework::OpKernel<T> {
// Generate keys for storing/retriving primitives for this operator
const std::string key =
platform::MKLDNNHandler::GetHash(softmax_tz, ctx.op().Output("Out"));
const std::string key_softmax_pd = key + "@softmax_pd";
SoftmaxMKLDNNHandler handler(dev_ctx, mkldnn_engine, key);
// Currently only NC data format is supported
auto softmax_md = MKLDNNMemDesc(
{softmax_tz}, platform::MKLDNNGetDataType<T>(), memory::format::nc);
// Normalization is made after innermost dimension eg. C out of NC
auto softmax_desc = softmax_forward::desc(prop_kind::forward_scoring,
softmax_md, 1 /*dim: C*/);
auto softmax_pd = std::make_shared<mkldnn::softmax_forward::primitive_desc>(
softmax_desc, mkldnn_engine);
dev_ctx.SetBlob(key_softmax_pd, softmax_pd);
SoftmaxMKLDNNHandler handler(softmax_pd, dev_ctx, mkldnn_engine, key);
auto softmax_pd =
handler.AcquireSoftmaxPrimitiveDescriptor(softmax_desc, mkldnn_engine);
auto softmax_src_memory_p =
handler.AcquireSrcMemory(softmax_md, to_void_cast<T>(input_data));
auto softmax_dst_memory_p =
......
paddle/fluid/operators/pad2d_op.cc
浏览文件 @ 431eab64
......@@ -483,8 +483,10 @@ class Pad2dOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ(
paddings_dim.size(), 1,
"Size of Input(Paddings)'s dimension should be equal to 1.");
PADDLE_ENFORCE_EQ(paddings_dim[0], 4,
"Shape of Input(Paddings) should be equal to [4].");
if (ctx->IsRuntime()) {
PADDLE_ENFORCE_EQ(paddings_dim[0], 4,
"Shape of Input(Paddings) should be equal to [4].");
}
out_dims[1] = x_dim[1];
out_dims[2] = x_dim[2];
out_dims[3] = x_dim[3];
......@@ -504,11 +506,7 @@ class Pad2dOp : public framework::OperatorWithKernel {
}
ctx->SetOutputDim("Out", framework::make_ddim(out_dims));
if (out_dims[0] == x_dim[0]) {
// Only pass LoD when the first dimension is equal between
// output and input.
ctx->ShareLoD("X", /*->*/ "Out");
}
ctx->ShareLoD("X", /*->*/ "Out");
}
protected:
......
paddle/fluid/operators/recurrent_op.cc
浏览文件 @ 431eab64
......@@ -23,6 +23,7 @@ constexpr char kInitialStates[] = "initial_states";
constexpr char kParameters[] = "parameters";
constexpr char kOutputs[] = "outputs";
constexpr char kStepScopes[] = "step_scopes";
constexpr char kHasStates[] = "has_states";
constexpr char kExStates[] = "ex_states";
constexpr char kStates[] = "states";
constexpr char kStepBlock[] = "sub_block";
......@@ -241,11 +242,16 @@ class RecurrentOp : public RecurrentBase {
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
bool has_state = Attr<bool>(kHasStates);
auto seq_len = static_cast<size_t>(this->GetSequenceLength(scope));
VLOG(3) << "Static RNN input sequence length = " << seq_len;
StepScopes scopes = CreateStepScopes(scope, seq_len);
auto reverse = Attr<bool>(kReverse);
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
framework::Executor executor(place);
auto *block = Attr<framework::BlockDesc *>(kStepBlock);
......@@ -269,15 +275,17 @@ class RecurrentOp : public RecurrentBase {
inside->Resize(framework::make_ddim(dims));
});
if (i == 0) {
// Link initial states --> ex_states
LinkTensor(scope, Inputs(kInitialStates), &cur_scope,
Attr<std::vector<std::string>>(kExStates));
} else {
auto &ex_scope = scopes.ExScope();
// Link ex_scope::state --> cur_scope::ex_state
LinkTensor(ex_scope, Attr<std::vector<std::string>>(kStates),
&cur_scope, Attr<std::vector<std::string>>(kExStates));
if (has_state) {
if (i == 0) {
// Link initial states --> ex_states
LinkTensor(scope, Inputs(kInitialStates), &cur_scope,
Attr<std::vector<std::string>>(kExStates));
} else {
auto &ex_scope = scopes.ExScope();
// Link ex_scope::state --> cur_scope::ex_state
LinkTensor(ex_scope, Attr<std::vector<std::string>>(kStates),
&cur_scope, Attr<std::vector<std::string>>(kExStates));
}
}
// Every inputs are linked now, execute!
......@@ -286,11 +294,6 @@ class RecurrentOp : public RecurrentBase {
std::vector<std::string>() /*skip_ref_cnt_vars*/,
true /*force_disable_gc*/);
// get device context from pool
platform::DeviceContextPool &pool =
platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
// Copy inside::output -> outside::output
// outside::output[seq_offset: seq_offset + 1] = inside::output
this->LinkTensorWithCallback(
......@@ -333,13 +336,13 @@ class RecurrentGradOp : public RecurrentBase {
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto seq_len = static_cast<size_t>(GetSequenceLength(scope));
bool has_state = Attr<bool>(kHasStates);
const size_t seq_len = static_cast<size_t>(GetSequenceLength(scope));
StepScopes scopes = CreateStepScopes(scope, seq_len);
auto reverse = Attr<bool>(kReverse);
framework::Executor executor(place);
auto *block = Attr<framework::BlockDesc *>(kStepBlock);
auto *program = block->Program();
// get device context from pool
......@@ -350,6 +353,7 @@ class RecurrentGradOp : public RecurrentBase {
size_t seq_offset = reverse ? step_id : seq_len - step_id - 1;
VLOG(3) << "Recurrent backward operate at the time step " << seq_offset;
auto &cur_scope = scopes.CurScope();
// Link outside::output_grads --> inside::output_grads
// inside::output_grad = outside::output_grad[seq_offset:seq_offset+1]
LinkTensorWithCallback(
......@@ -370,30 +374,32 @@ class RecurrentGradOp : public RecurrentBase {
VLOG(10) << " RNN output gradients = [" << sout.str() << "]";
}
// Link states
// if cur_scope::cur_state_grad in out_grads:
// cur_scope::cur_state_grad += ex_scope::ex_state_grad
// else:
// ex_scope::ex_state_grad --> cur_scope::cur_state_grad
if (step_id != 0) { // not at beginning
auto &ex_scope = scopes.ExScope();
auto ex_state_grads =
GradVarLists(Attr<std::vector<std::string>>(kExStates));
auto cur_state_grads =
GradVarLists(Attr<std::vector<std::string>>(kStates));
PADDLE_ENFORCE_EQ(ex_state_grads.size(), cur_state_grads.size());
for (size_t i = 0; i < ex_state_grads.size(); ++i) {
auto &cur_grad = cur_state_grads[i];
auto &ex_grad = ex_state_grads[i];
auto &ex_tensor =
ex_scope.FindVar(ex_grad)->Get<framework::LoDTensor>();
VLOG(10) << " RNN link " << cur_grad << " from " << ex_grad;
auto *cur_grad_var = cur_scope.Var(cur_grad);
auto cur_grad_tensor =
cur_grad_var->GetMutable<framework::LoDTensor>();
framework::TensorCopy(ex_tensor, place, dev_ctx, cur_grad_tensor);
if (has_state) {
// Link states
// if cur_scope::cur_state_grad in out_grads:
// cur_scope::cur_state_grad += ex_scope::ex_state_grad
// else:
// ex_scope::ex_state_grad --> cur_scope::cur_state_grad
if (step_id != 0) { // not at beginning
auto &ex_scope = scopes.ExScope();
auto ex_state_grads =
GradVarLists(Attr<std::vector<std::string>>(kExStates));
auto cur_state_grads =
GradVarLists(Attr<std::vector<std::string>>(kStates));
PADDLE_ENFORCE_EQ(ex_state_grads.size(), cur_state_grads.size());
for (size_t i = 0; i < ex_state_grads.size(); ++i) {
auto &cur_grad = cur_state_grads[i];
auto &ex_grad = ex_state_grads[i];
auto &ex_tensor =
ex_scope.FindVar(ex_grad)->Get<framework::LoDTensor>();
VLOG(10) << " RNN link " << cur_grad << " from " << ex_grad;
auto *cur_grad_var = cur_scope.Var(cur_grad);
auto cur_grad_tensor =
cur_grad_var->GetMutable<framework::LoDTensor>();
framework::TensorCopy(ex_tensor, place, dev_ctx, cur_grad_tensor);
}
}
}
......@@ -442,8 +448,8 @@ class RecurrentGradOp : public RecurrentBase {
}
auto new_inside_name = cur_scope.Rename(inside_grad_name);
// sum gradient
// sum gradient
auto sum_op = framework::OpRegistry::CreateOp(
"sum", {{"X", {pg_names[param_id], new_inside_name}}},
{{"Out", {pg_names[param_id]}}},
......@@ -475,22 +481,33 @@ class RecurrentGradOp : public RecurrentBase {
true /*is_backward*/);
VLOG(5) << "Link outside gradient finished ";
if (step_id + 1 == seq_len) { // at_end
// copy initialize states gradient from inside to outside
LinkTensorWithCallback(
cur_scope, GradVarLists(Attr<std::vector<std::string>>(kExStates)),
scope, Outputs(kInitStateGrads),
[&](const framework::LoDTensor &inside,
framework::LoDTensor *outside) {
outside->Resize(inside.dims());
outside->mutable_data(place, inside.type());
framework::TensorCopy(inside, place, dev_ctx, outside);
},
true /*is_backward*/);
VLOG(5) << "Link initialize state gradient finished ";
if (has_state) {
if (step_id + 1 == seq_len) { // at_end
// copy initialize states gradient from inside to outside
LinkTensorWithCallback(
cur_scope,
GradVarLists(Attr<std::vector<std::string>>(kExStates)), scope,
Outputs(kInitStateGrads),
[&](const framework::LoDTensor &inside,
framework::LoDTensor *outside) {
outside->Resize(inside.dims());
outside->mutable_data(place, inside.type());
framework::TensorCopy(inside, place, dev_ctx, outside);
},
true /*is_backward*/);
VLOG(5) << "Link initialize state gradient finished ";
}
}
scopes.Next();
}
// Delete the scope of StepScopes
dev_ctx.Wait();
auto *var = scope.FindVar(Input(kStepScopes));
PADDLE_ENFORCE(var != nullptr);
auto step_scopes = var->GetMutable<StepScopeVar>();
for (auto *sub_scope : *step_scopes) {
const_cast<framework::Scope &>(scope).DeleteScope(sub_scope);
}
}
private:
......@@ -541,6 +558,7 @@ class RecurrentOpProtoMaker : public framework::OpProtoAndCheckerMaker {
.AsDuplicable();
AddOutput(kStepScopes,
"StepScopes contain all local variables in each time step.");
AddAttr<bool>(kHasStates, "Whether has states.").SetDefault(false);
AddAttr<std::vector<std::string>>(kExStates,
string::Sprintf(
R"DOC(The ex-state variable names.
......@@ -624,20 +642,44 @@ class RecurrentGradOpDescMaker : public framework::SingleGradOpDescMaker {
class RecurrentGradOpShapeInference : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *ctx) const override {
std::vector<std::string> input{kInputs, kInitialStates};
std::vector<std::string> output{kOutputs};
for (auto &s : input) {
// NOTE(zcd): In some case, some of kInputs doesn't have gradient.
PADDLE_ENFORCE(ctx->HasInputs(s));
}
for (auto &s : output) {
PADDLE_ENFORCE(ctx->HasInputs(s));
// In some case the kInitialStates is empty.
// If the kInitialStates is empty, all the states should be empty.
if (!ctx->HasInputs(kInitialStates)) {
PADDLE_ENFORCE_EQ(
ctx->Attrs().Get<std::vector<std::string>>(kExStates).size(), 0,
"The Attr(%s) should be empty.", kExStates);
PADDLE_ENFORCE_EQ(
ctx->Attrs().Get<std::vector<std::string>>(kStates).size(), 0,
"The Attr(%s) should be empty.", kStates);
}
for (auto &s : input) {
ctx->SetOutputsDim(framework::GradVarName(s), ctx->GetInputsDim(s));
PADDLE_ENFORCE(ctx->HasInputs(kInputs),
"The input(%s) should not be empty.", kInputs);
PADDLE_ENFORCE(ctx->HasInputs(kOutputs),
"The input(%s) should not be empty.", kOutputs);
// In some case the kInitialStates is empty.
if (ctx->HasInputs(kInitialStates)) {
PADDLE_ENFORCE(ctx->HasOutputs(framework::GradVarName(kInitialStates)),
"The output of(%s) should not be empty.",
framework::GradVarName(kInitialStates));
ctx->SetOutputsDim(framework::GradVarName(kInitialStates),
ctx->GetInputsDim(kInitialStates));
}
PADDLE_ENFORCE(ctx->HasOutputs(framework::GradVarName(kInputs)),
"The output of(%s) should not be empty.",
framework::GradVarName(kInputs));
ctx->SetOutputsDim(framework::GradVarName(kInputs),
ctx->GetInputsDim(kInputs));
// In some case the kParameters is empty.
if (ctx->HasInputs(kParameters)) {
PADDLE_ENFORCE(ctx->HasOutputs(framework::GradVarName(kParameters)));
PADDLE_ENFORCE(ctx->HasOutputs(framework::GradVarName(kParameters)),
"The output of(%s) should not be empty.",
framework::GradVarName(kParameters));
ctx->SetOutputsDim(framework::GradVarName(kParameters),
ctx->GetInputsDim(kParameters));
}
......
paddle/fluid/operators/reduce_ops/reduce_all_op.cc
浏览文件 @ 431eab64
......@@ -14,7 +14,7 @@
#include "paddle/fluid/operators/reduce_ops/reduce_all_op.h"
REGISTER_REDUCE_OP(reduce_all);
REGISTER_REDUCE_OP_WITHOUT_GRAD(reduce_all);
REGISTER_OP_CPU_KERNEL(reduce_all,
ops::ReduceKernel<paddle::platform::CPUDeviceContext,
bool, ops::AllFunctor>);
paddle/fluid/operators/reduce_ops/reduce_any_op.cc
浏览文件 @ 431eab64
......@@ -14,7 +14,7 @@
#include "paddle/fluid/operators/reduce_ops/reduce_any_op.h"
REGISTER_REDUCE_OP(reduce_any);
REGISTER_REDUCE_OP_WITHOUT_GRAD(reduce_any);
REGISTER_OP_CPU_KERNEL(reduce_any,
ops::ReduceKernel<paddle::platform::CPUDeviceContext,
bool, ops::AnyFunctor>);
paddle/fluid/operators/reduce_ops/reduce_op.h
浏览文件 @ 431eab64
......@@ -270,3 +270,12 @@ namespace ops = paddle::operators;
REGISTER_OPERATOR(op_name, ops::ReduceOp, __##op_name##Maker__, \
paddle::framework::DefaultGradOpDescMaker<true>); \
REGISTER_OPERATOR(op_name##_grad, ops::ReduceGradOp)
#define REGISTER_REDUCE_OP_WITHOUT_GRAD(op_name) \
class __##op_name##Maker__ : public ops::ReduceOpMaker { \
protected: \
virtual std::string GetName() const { return #op_name; } \
virtual std::string GetOpType() const { return "Reduce " #op_name; } \
}; \
REGISTER_OPERATOR(op_name, ops::ReduceOp, __##op_name##Maker__, \
paddle::framework::EmptyGradOpMaker);
paddle/fluid/operators/rnn_memory_helper_op.cc
浏览文件 @ 431eab64
......@@ -40,9 +40,12 @@ class RNNMemoryHelperOp : public framework::OperatorBase {
"Cannot find out_var in scope, out_var_name is %s",
out_name);
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(dev_place);
auto *out_tensor = out_var->GetMutable<framework::LoDTensor>();
auto &mem_tensor = mem_var->Get<framework::LoDTensor>();
framework::TensorCopySync(mem_tensor, dev_place, out_tensor);
framework::TensorCopy(mem_tensor, dev_place, dev_ctx, out_tensor);
out_tensor->set_lod(mem_tensor.lod());
}
};
......@@ -92,6 +95,9 @@ class RNNMemoryHelperGradOp : public framework::OperatorBase {
"Cannot find in_grad_var in scope, name is %s",
in_grad_var_name);
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(dev_place);
if (out_grad_var == nullptr) {
VLOG(5) << "Using fill constant 0 as starting gradient";
auto in_var_name = Input("X");
......@@ -109,7 +115,8 @@ class RNNMemoryHelperGradOp : public framework::OperatorBase {
} else {
auto &out_grad_tensor = out_grad_var->Get<framework::LoDTensor>();
auto *in_grad_tensor = in_grad_var->GetMutable<framework::LoDTensor>();
framework::TensorCopySync(out_grad_tensor, dev_place, in_grad_tensor);
framework::TensorCopy(out_grad_tensor, dev_place, dev_ctx,
in_grad_tensor);
in_grad_tensor->set_lod(out_grad_tensor.lod());
}
}
......
paddle/fluid/operators/roi_align_op.cc
浏览文件 @ 431eab64
......@@ -37,9 +37,11 @@ class ROIAlignOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE(rois_dims.size() == 2,
"ROIs should be a 2-D LoDTensor of shape (num_rois, 4)"
"given as [[x1, y1, x2, y2], ...].");
PADDLE_ENFORCE(rois_dims[1] == 4,
"ROIs should be a 2-D LoDTensor of shape (num_rois, 4)"
"given as [[x1, y1, x2, y2], ...].");
if (ctx->IsRuntime()) {
PADDLE_ENFORCE(rois_dims[1] == 4,
"ROIs should be a 2-D LoDTensor of shape (num_rois, 4)"
"given as [[x1, y1, x2, y2], ...].");
}
int pooled_height = ctx->Attrs().Get<int>("pooled_height");
int pooled_width = ctx->Attrs().Get<int>("pooled_width");
float spatial_scale = ctx->Attrs().Get<float>("spatial_scale");
......
paddle/fluid/operators/row_conv_op.cc
浏览文件 @ 431eab64
......@@ -45,9 +45,12 @@ class RowConvOp : public framework::OperatorWithKernel {
auto filter_dims = ctx->GetInputDim("Filter");
PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank should be 2.");
PADDLE_ENFORCE_EQ(filter_dims.size(), 2, "Input(Y)'s rank should be 2.");
PADDLE_ENFORCE_EQ(
x_dims[1], filter_dims[1],
"The 2nd dimension of Input(X) and Input(Filter) should be same.");
if (ctx->IsRuntime() || (x_dims[1] > 0 && filter_dims[1] > 0)) {
PADDLE_ENFORCE_EQ(
x_dims[1], filter_dims[1],
"The 2nd dimension of Input(X) and Input(Filter) should be same.");
}
ctx->SetOutputDim("Out", x_dims);
ctx->ShareLoD("X", "Out");
}
......
paddle/fluid/operators/sample_logits_op.cc
浏览文件 @ 431eab64
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/sample_logits_op.h"
#include <memory>
#include "paddle/fluid/operators/math/sample_prob.h"
namespace paddle {
......@@ -60,6 +61,10 @@ class SampleLogitsOpMaker : public framework::OpProtoAndCheckerMaker {
"(Tensor, default: Tensor<float>), A 2-D tensor with shape [N, NT + S]."
"The probabilites of sampled positive and negtive labels.")
.AsIntermediate();
AddOutput("LogitsDim", "Store dim information of Logits for gradient op")
.AsIntermediate();
AddOutput("LabelsDim", "Store dim information of Logits for gradient op")
.AsIntermediate();
AddOutput("SampledLogits",
"(Tensor, default: Tensor<float>), A 2-D tensor with shape"
"[N, NT + S]. The outputs value of sampled logits, which will be"
......@@ -121,6 +126,10 @@ class SampleLogitsOp : public framework::OperatorWithKernel {
"Output(SampledLogits) should be not null.");
PADDLE_ENFORCE(ctx->HasOutput("SampledLabels"),
"Output(SampledLabels) should be not null.");
PADDLE_ENFORCE(ctx->HasOutput("LogitsDim"),
"Output(LogitsDim) should be not null.");
PADDLE_ENFORCE(ctx->HasOutput("LabelsDim"),
"Output(LabelsDim) should be not null.");
auto logits_dims = ctx->GetInputDim("Logits");
auto labels_dims = ctx->GetInputDim("Labels");
......@@ -137,6 +146,15 @@ class SampleLogitsOp : public framework::OperatorWithKernel {
ctx->SetOutputDim("Probabilities", {logits_dims[0], num_sampled_classes});
ctx->SetOutputDim("SampledLogits", {logits_dims[0], num_sampled_classes});
ctx->SetOutputDim("SampledLabels", {logits_dims[0], labels_dims[1]});
// append 0 to shape variable to avoid optimized by memory optimize pass
auto logits_dim_vec = framework::vectorize(logits_dims);
logits_dim_vec.push_back(0);
ctx->SetOutputDim("LogitsDim", framework::make_ddim(logits_dim_vec));
auto labels_dim_vec = framework::vectorize(labels_dims);
labels_dim_vec.push_back(0);
ctx->SetOutputDim("LabelsDim", framework::make_ddim(labels_dim_vec));
}
protected:
......@@ -155,28 +173,27 @@ class SampleLogitsOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Logits"),
"Input(Logits) should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Labels"),
"Input(Labels) should be not null.");
PADDLE_ENFORCE(ctx->HasInput("LogitsDim"),
"Input(LogitsDim) should not be null.");
PADDLE_ENFORCE(ctx->HasInput("LabelsDim"),
"Input(LabelsDim) should be not null.");
PADDLE_ENFORCE(ctx->HasInput("Samples"),
"Input(Samples) should be not null.");
PADDLE_ENFORCE(ctx->HasInput("SampledLogits"),
"Input(SampledLogits) should be not null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("SampledLogits")),
"Input(SampledLogits@Grad) should not be null.");
PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("Logits")),
"Output(Logits@Grad) should be not null.");
auto logit_dims = ctx->GetInputDim("Logits");
auto label_dims = ctx->GetInputDim("Labels");
PADDLE_ENFORCE_EQ(label_dims.size(), 2UL,
auto logits_dims = ctx->GetInputDim("LogitsDim");
logits_dims = framework::DDim(logits_dims.Get(), logits_dims.size() - 1);
auto labels_dims = ctx->GetInputDim("LabelsDim");
labels_dims = framework::DDim(labels_dims.Get(), labels_dims.size() - 1);
PADDLE_ENFORCE_EQ(labels_dims.size(), 2UL,
"The label should be a 2-D tensor.");
PADDLE_ENFORCE_EQ(logit_dims.size(), 2UL,
PADDLE_ENFORCE_EQ(logits_dims.size(), 2UL,
"The logits should be a 2-D tensor.");
ctx->SetOutputDim(framework::GradVarName("Logits"),
ctx->GetInputDim("Logits"));
ctx->SetOutputDim(framework::GradVarName("Logits"), logits_dims);
}
protected:
......@@ -199,10 +216,9 @@ class SampleLogitsGradMaker : public framework::SingleGradOpDescMaker {
std::unique_ptr<framework::OpDesc> Apply() const override {
auto* grad_op = new framework::OpDesc();
grad_op->SetType("sample_logits_grad");
grad_op->SetInput("Logits", Input("Logits"));
grad_op->SetInput("Labels", Input("Labels"));
grad_op->SetInput("LogitsDim", Output("LogitsDim"));
grad_op->SetInput("LabelsDim", Output("LabelsDim"));
grad_op->SetInput("Samples", Output("Samples"));
grad_op->SetInput("SampledLogits", Output("SampledLogits"));
grad_op->SetInput(framework::GradVarName("SampledLogits"),
OutputGrad("SampledLogits"));
grad_op->SetOutput(framework::GradVarName("Logits"), InputGrad("Logits"));
......
paddle/fluid/operators/scatter_op.cc
浏览文件 @ 431eab64
......@@ -42,10 +42,6 @@ class ScatterOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ(ctx->GetInputDim("Updates")[0],
ctx->GetInputDim("Ids")[0],
"Updates and Ids should have same batch-size.");
framework::DDim data_dim(updates_dims);
for (int i = 1; i < data_dim.size(); ++i) {
PADDLE_ENFORCE_EQ(data_dim[i], updates_dims[i]);
}
ctx->SetOutputDim("Out", ref_dims);
}
......
paddle/fluid/operators/sigmoid_cross_entropy_with_logits_op.cc
浏览文件 @ 431eab64
......@@ -34,15 +34,22 @@ class SigmoidCrossEntropyWithLogitsOp : public framework::OperatorWithKernel {
auto x_dims = ctx->GetInputDim("X");
auto labels_dims = ctx->GetInputDim("Label");
PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank should be 2.");
PADDLE_ENFORCE_EQ(labels_dims.size(), 2,
"Input(Label)'s rank should be 2.");
PADDLE_ENFORCE_EQ(x_dims[0], labels_dims[0],
"The 1st dimension of Input(X) and Input(Label) should "
"be equal.");
PADDLE_ENFORCE_EQ(x_dims[1], labels_dims[1],
"The 2nd dimension of Input(X) and Input(Label) should "
"be equal.");
int rank = x_dims.size();
PADDLE_ENFORCE_EQ(rank, labels_dims.size(),
"Input(X) and Input(Label) shall have the same rank.");
bool check = true;
if ((!ctx->IsRuntime()) && (framework::product(x_dims) <= 0 ||
framework::product(labels_dims) <= 0)) {
check = false;
}
if (check) {
PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 0, rank),
framework::slice_ddim(labels_dims, 0, rank),
"Input(X) and Input(Label) shall have the same shape "
"except the last dimension.");
}
ctx->ShareDim("X", /*->*/ "Out");
ctx->ShareLoD("X", /*->*/ "Out");
......@@ -65,23 +72,24 @@ class SigmoidCrossEntropyWithLogitsGradOp
auto x_dims = ctx->GetInputDim("X");
auto labels_dims = ctx->GetInputDim("Label");
auto dout_dims = ctx->GetInputDim(framework::GradVarName("Out"));
PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank should be 2.");
PADDLE_ENFORCE_EQ(labels_dims.size(), 2,
"Input(Label)'s rank should be 2.");
PADDLE_ENFORCE_EQ(dout_dims.size(), 2,
"Input(Out@Grad)'s rank should be 2.");
PADDLE_ENFORCE_EQ(x_dims[0], labels_dims[0],
"The 1st dimension of Input(X) and Input(Label) should "
"be equal.");
PADDLE_ENFORCE_EQ(x_dims[1], labels_dims[1],
"The 2nd dimension of Input(X) and Input(Label) should "
"be equal.");
PADDLE_ENFORCE_EQ(x_dims[0], dout_dims[0],
"The 1st dimension of Input(X) and Input(Out@Grad) "
"should be equal.");
PADDLE_ENFORCE_EQ(x_dims[1], dout_dims[1],
"The 2nd dimension of Input(X) and Input(Out@Grad) "
"should be equal.");
int rank = x_dims.size();
bool check = true;
if ((!ctx->IsRuntime()) && (framework::product(x_dims) <= 0 ||
framework::product(labels_dims) <= 0)) {
check = false;
}
if (check) {
PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 0, rank),
framework::slice_ddim(labels_dims, 0, rank),
"Input(X) and Input(Label) shall have the same shape.");
PADDLE_ENFORCE_EQ(
framework::slice_ddim(x_dims, 0, rank),
framework::slice_ddim(dout_dims, 0, rank),
"Input(X) and Input(Out@Grad) shall have the same shape.");
}
ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
}
......
paddle/fluid/operators/spectral_norm_op.cc
浏览文件 @ 431eab64
......@@ -56,13 +56,19 @@ class SpectralNormOp : public framework::OperatorWithKernel {
}
auto dim_u = ctx->GetInputDim("U");
auto dim_v = ctx->GetInputDim("V");
PADDLE_ENFORCE_EQ(dim_u[0], h,
"Input(U) dims[0] should be equal to "
"Input(Weight) dims[Attr(dim)]");
PADDLE_ENFORCE_EQ(
dim_v[0], w,
"Input(V) dims[0] should be equal to "
"the product of Input(Weight) dims except dims[Attr(dim)]");
if (ctx->IsRuntime() || (dim_u[0] > 0 && h > 0)) {
PADDLE_ENFORCE_EQ(dim_u[0], h,
"Input(U) dims[0] should be equal to "
"Input(Weight) dims[Attr(dim)]");
}
if (ctx->IsRuntime() || (dim_v[0] > 0 && w > 0)) {
PADDLE_ENFORCE_EQ(
dim_v[0], w,
"Input(V) dims[0] should be equal to "
"the product of Input(Weight) dims except dims[Attr(dim)]");
}
ctx->SetOutputDim("Out", dim_weight);
ctx->ShareLoD("Weight", /*->*/ "Out");
......
paddle/fluid/operators/split_op.cc
浏览文件 @ 431eab64
......@@ -39,14 +39,22 @@ class SplitOp : public framework::OperatorWithKernel {
if (num > 0) {
int64_t in_axis_dim = in_dims[axis];
PADDLE_ENFORCE_EQ(in_axis_dim % num, 0,
"tensor split does not result"
" in an equal division");
size_t out_axis_dim = in_axis_dim / num;
for (size_t i = 0; i < outs_number; ++i) {
auto dim = in_dims;
dim[axis] = out_axis_dim;
outs_dims.push_back(dim);
if (ctx->IsRuntime() || in_axis_dim > 0) {
PADDLE_ENFORCE_EQ(in_axis_dim % num, 0,
"tensor split does not result"
" in an equal division");
size_t out_axis_dim = in_axis_dim / num;
for (size_t i = 0; i < outs_number; ++i) {
auto dim = in_dims;
dim[axis] = out_axis_dim;
outs_dims.push_back(dim);
}
} else {
for (size_t i = 0; i < outs_number; ++i) {
auto dim = in_dims;
dim[axis] = -1;
outs_dims.push_back(dim);
}
}
} else if (sections.size() > 0) {
PADDLE_ENFORCE_EQ(sections.size(), outs_number,
......
paddle/fluid/operators/sum_op.cc
浏览文件 @ 431eab64
......@@ -65,7 +65,21 @@ class SumOp : public framework::OperatorWithKernel {
if (framework::product(in_dim) == 0) {
in_dim = x_dim;
} else {
PADDLE_ENFORCE_EQ(in_dim, x_dim, "Input tensors must have same shape");
if (ctx->IsRuntime()) {
PADDLE_ENFORCE_EQ(in_dim, x_dim,
"Input tensors must have same shape");
} else {
PADDLE_ENFORCE_EQ(in_dim.size(), x_dim.size(),
"Input tensors must have same shape size");
// if in_dim or x_dim has -1, not check equal
for (int i = 0; i < x_dim.size(); ++i) {
if (x_dim[i] == -1 || in_dim[i] == -1) {
continue;
}
PADDLE_ENFORCE_EQ(in_dim[i], x_dim[i],
"Input tensors must have same shape if not -1");
}
}
}
}
ctx->SetOutputDim("Out", in_dim);
......
paddle/fluid/operators/unpool_op.cc
浏览文件 @ 431eab64
......@@ -99,10 +99,15 @@ class UnpoolOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE(in_x_dims.size() == 4,
"Unpooling intput must be of 4-dimensional.");
PADDLE_ENFORCE_EQ(in_x_dims, in_y_dims);
std::vector<int64_t> output_shape({in_x_dims[0], in_x_dims[1]});
for (size_t i = 0; i < ksize.size(); ++i) {
output_shape.push_back(UnpoolOutputSize(in_x_dims[i + 2], ksize[i],
paddings[i], strides[i]));
if (!ctx->IsRuntime() && in_x_dims[i + 2] <= 0) {
output_shape.push_back(-1);
} else {
output_shape.push_back(UnpoolOutputSize(in_x_dims[i + 2], ksize[i],
paddings[i], strides[i]));
}
}
ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
}
......
paddle/fluid/platform/lodtensor_printer.cc
浏览文件 @ 431eab64
......@@ -52,16 +52,26 @@ void PrintVar(framework::Scope* scope, const std::string& var_name,
return;
}
#define PrintLoDTensorCallback(cpp_type, proto_type) \
do { \
if (tensor->type() == proto_type) { \
print_lod_tensor<cpp_type>(var_name, *tensor, print_info); \
return; \
} \
framework::LoDTensor printed_tensor;
printed_tensor.set_lod(tensor->lod());
printed_tensor.Resize(tensor->dims());
if (platform::is_cpu_place(tensor->place())) {
printed_tensor.ShareDataWith(*tensor);
} else {
platform::CPUPlace place;
framework::TensorCopy(*tensor, place, &printed_tensor);
}
#define PrintLoDTensorCallback(cpp_type, proto_type) \
do { \
if (tensor->type() == proto_type) { \
print_lod_tensor<cpp_type>(var_name, printed_tensor, print_info); \
return; \
} \
} while (0)
_ForEachDataType_(PrintLoDTensorCallback);
VLOG(1) << "PrintVar: unrecognized data type:" << tensor->type();
VLOG(1) << "PrintVar: unrecognized data type:" << printed_tensor.type();
}
} // end namespace platform
......
paddle/fluid/platform/mkldnn_reuse.h
浏览文件 @ 431eab64
......@@ -16,6 +16,7 @@ limitations under the License. */
#include <memory>
#include <string>
#include <vector>
#include "boost/optional.hpp"
#include "paddle/fluid/framework/data_layout_transform.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/platform/mkldnn_helper.h"
......@@ -395,9 +396,28 @@ class TransposeMKLDNNHandler : public MKLDNNHandler {
std::vector<int> logical_axis_;
};
template <typename T>
struct convolutional_algorithm;
template <>
struct convolutional_algorithm<mkldnn::convolution_forward> {
static constexpr mkldnn::algorithm T = mkldnn::algorithm::convolution_direct;
};
template <>
struct convolutional_algorithm<mkldnn::deconvolution_forward> {
static constexpr mkldnn::algorithm T =
mkldnn::algorithm::deconvolution_direct;
};
template <class forward_t, class backward_data_t, class backward_weights_t>
class ConvMKLDNNTemplateHandler : public MKLDNNHandler {
public:
ConvMKLDNNTemplateHandler(const platform::MKLDNNDeviceContext& dev_ctx,
mkldnn::engine engine, const std::string& base_key)
: platform::MKLDNNHandler(dev_ctx, engine, base_key) {}
// TODO(jczaja): remove after conv int8 is adapted
ConvMKLDNNTemplateHandler(
std::shared_ptr<typename forward_t::primitive_desc> conv_pd,
const platform::MKLDNNDeviceContext& dev_ctx, mkldnn::engine engine,
......@@ -542,6 +562,73 @@ class ConvMKLDNNTemplateHandler : public MKLDNNHandler {
scale_data, mask);
}
mkldnn::primitive_attr CreatePostOps(bool fuse_relu,
bool fuse_residual_conn = false) const {
mkldnn::primitive_attr conv_attr;
mkldnn::post_ops post_operations;
// Fusion with Elementwise layer relies on adding a sum post-operation with
// the scale parameter. It is assumed that when fuse_residual_connection is
// true, the output tensor contains the data coming from residual
// connection. The result of this post_op is:
// Output = scale * Output + Conv_Out.
if (fuse_residual_conn) {
post_operations.append_sum(1.0f);
}
// Fusion with ReLU layer is executed through the PostOps feature. Create a
// PostOps object and configure it to execute an eltwise relu operation.
if (fuse_relu) {
constexpr float scale = 1.0f;
constexpr float negative_slope = 0.0f;
constexpr float placeholder = 0.0f;
post_operations.append_eltwise(scale, mkldnn::algorithm::eltwise_relu,
negative_slope, placeholder);
}
conv_attr.set_post_ops(post_operations);
return conv_attr;
}
std::shared_ptr<typename forward_t::primitive_desc>
AcquireConvolutionPrimitiveDescriptor(
const mkldnn::memory::desc& src, const mkldnn::memory::desc& weights,
boost::optional<const mkldnn::memory::desc&> bias,
const mkldnn::memory::desc& dst, const std::vector<int>& strides,
const std::vector<int>& paddings, const mkldnn::engine& engine,
const bool fuse_relu, const bool fuse_residual_conn,
mkldnn::prop_kind fwd_prop_kind) {
const std::string key_conv_pd = key_ + "@conv_pd";
auto conv_pd = std::static_pointer_cast<typename forward_t::primitive_desc>(
dev_ctx_.GetBlob(key_conv_pd));
if (conv_pd == nullptr) {
mkldnn::memory::dims stride_dims = strides;
mkldnn::memory::dims padding_dims = paddings;
auto conv_desc =
bias ? typename forward_t::desc(
fwd_prop_kind, convolutional_algorithm<forward_t>::T, src,
weights, *bias, dst, stride_dims, padding_dims,
padding_dims, mkldnn::padding_kind::zero)
: typename forward_t::desc(
fwd_prop_kind, convolutional_algorithm<forward_t>::T, src,
weights, dst, stride_dims, padding_dims, padding_dims,
mkldnn::padding_kind::zero);
mkldnn::primitive_attr conv_attr =
CreatePostOps(fuse_relu, fuse_residual_conn);
conv_pd_.reset(
new typename forward_t::primitive_desc(conv_desc, conv_attr, engine));
// Save conv_pd/src_memory/weights_memory for backward pass
dev_ctx_.SetBlob(key_conv_pd, conv_pd_);
} else {
conv_pd_ = conv_pd;
is_reusing_ = true;
}
return conv_pd_;
}
std::shared_ptr<forward_t> AcquireConvolution(
std::shared_ptr<mkldnn::memory> src_memory_p,
std::shared_ptr<mkldnn::memory> weights_memory_p,
......
paddle/scripts/paddle_build.sh
浏览文件 @ 431eab64
......@@ -446,7 +446,8 @@ function assert_api_spec_approvals() {
BRANCH="develop"
fi
API_FILES=("paddle/fluid/API.spec"
API_FILES=("CMakeLists.txt"
"paddle/fluid/API.spec"
"paddle/fluid/op_use_default_grad_op_maker.spec"
"python/paddle/fluid/parallel_executor.py"
"paddle/fluid/framework/operator.h"
......@@ -469,24 +470,29 @@ 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.
# approval_user_list: velconia 1979255,panyx0718 2887803,XiaoguangHu01 46782768,chengduoZH 30176695,Xreki 12538138,luotao1 6836917,sneaxiy 32832641,tensor-tang 21351065,jacquesqiao 3048612,typhoonzero 13348433,shanyi15 35982308.
# approval_user_list: velconia 1979255,XiaoguangHu01 46782768,chengduoZH 30176695,Xreki 12538138,luotao1 6836917,sneaxiy 32832641,tensor-tang 21351065,jacquesqiao 3048612,typhoonzero 13348433,shanyi15 35982308.
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 46782768 30176695`
python ${PADDLE_ROOT}/tools/check_pr_approval.py 2 35982308 46782768 30176695`
if [ "${APPROVALS}" == "TRUE" ];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 ${PADDLE_ROOT}/tools/check_pr_approval.py 1 35982308`
fi
elif [ "$API_FILE" == "CMakeLists.txt" ];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 6836917 46782768 30176695`
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 1979255 21351065 3048612 13348433 46782768 30176695 12538138 6836917 32832641`
python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 1979255 21351065 3048612 13348433 46782768 30176695 12538138 6836917 32832641`
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 one RD (panyx0718 or chengduoZH or XiaoguangHu01) and one PM (shanyi15) approval for the api change! ${API_FILE}"
echo "You must have one RD (chengduoZH or XiaoguangHu01) and one PM (shanyi15) approval for the api change! ${API_FILE}"
elif [ "$API_FILE" == "CMakeLists.txt" ];then
echo "You must have one RD (luotao1 or chengduoZH or XiaoguangHu01) approval for the cmakelist change! ${API_FILE}"
else
echo "You must have one RD (velconia,panyx0718,XiaoguangHu01,chengduoZH,Xreki,luotao1,sneaxiy,tensor-tang,jacquesqiao,typhoonzero) approval for the api change! ${API_FILE}"
echo "You must have one RD (velconia,XiaoguangHu01,chengduoZH,Xreki,luotao1,sneaxiy,tensor-tang,jacquesqiao,typhoonzero) approval for the api change! ${API_FILE}"
fi
exit 1
fi
......@@ -496,10 +502,10 @@ function assert_api_spec_approvals() {
HAS_CONST_CAST=`git diff -U0 upstream/$BRANCH |grep -o -m 1 "const_cast" || true`
if [ ${HAS_CONST_CAST} ] && [ "${GIT_PR_ID}" != "" ]; 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 2887803 1979255 21351065 3048612 13348433 46782768 30176695 12538138 6836917 32832641`
python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 1979255 21351065 3048612 13348433 46782768 30176695 12538138 6836917 32832641`
echo "current pr ${GIT_PR_ID} got approvals: ${APPROVALS}"
if [ "${APPROVALS}" == "FALSE" ]; then
echo "You must have one RD (velconia,panyx0718,XiaoguangHu01,chengduoZH,Xreki,luotao1,sneaxiy,tensor-tang,jacquesqiao,typhoonzero) approval for the api change! ${API_FILE}"
echo "You must have one RD (velconia,XiaoguangHu01,chengduoZH,Xreki,luotao1,sneaxiy,tensor-tang,jacquesqiao,typhoonzero) approval for the api change! ${API_FILE}"
exit 1
fi
fi
......
python/paddle/fluid/__init__.py
浏览文件 @ 431eab64
......@@ -175,6 +175,7 @@ def __bootstrap__():
read_env_flags.append('communicator_thread_pool_size')
read_env_flags.append('communicator_max_merge_var_num')
read_env_flags.append('communicator_fake_rpc')
read_env_flags.append('communicator_send_wait_times')
if core.is_compiled_with_brpc():
read_env_flags.append('max_body_size')
#set brpc max body size
......
python/paddle/fluid/contrib/tests/test_calibration.py
浏览文件 @ 431eab64
......@@ -147,10 +147,11 @@ class TestCalibrationForResnet50(unittest.TestCase):
self.data_cache_folder)
os.system(cmd)
self.batch_size = 1
self.sample_iterations = 50
self.batch_size = 1 if os.environ.get('DATASET') == 'full' else 50
self.sample_iterations = 50 if os.environ.get(
'DATASET') == 'full' else 1
self.infer_iterations = 50000 if os.environ.get(
'DATASET') == 'full' else 50
'DATASET') == 'full' else 1
def cache_unzipping(self, target_folder, zip_path):
if not os.path.exists(target_folder):
......@@ -279,15 +280,15 @@ class TestCalibrationForResnet50(unittest.TestCase):
def test_calibration(self):
self.download_model()
print("Start FP32 inference for {0} on {1} images ...").format(
self.model, self.infer_iterations)
self.model, self.infer_iterations * self.batch_size)
(fp32_throughput, fp32_latency,
fp32_acc1) = self.run_program(self.model_cache_folder + "/model")
print("Start INT8 calibration for {0} on {1} images ...").format(
self.model, self.sample_iterations)
self.model, self.sample_iterations * self.batch_size)
self.run_program(
self.model_cache_folder + "/model", True, algo=self.algo)
print("Start INT8 inference for {0} on {1} images ...").format(
self.model, self.infer_iterations)
self.model, self.infer_iterations * self.batch_size)
(int8_throughput, int8_latency,
int8_acc1) = self.run_program("calibration_out")
delta_value = fp32_acc1 - int8_acc1
......
python/paddle/fluid/device_worker.py
浏览文件 @ 431eab64
......@@ -26,8 +26,8 @@ class DeviceWorker(object):
"""
Init.
"""
self.program_ = None
self.infer_ = None
self._program = None
self._infer = None
def _set_infer(self, infer=False):
"""
......@@ -36,7 +36,7 @@ class DeviceWorker(object):
Args:
infer(bool): whether to do inference
"""
self.infer_ = infer
self._infer = infer
def _set_fleet_desc(self, fleet_desc):
"""
......@@ -45,7 +45,7 @@ class DeviceWorker(object):
Args:
fleet_desc(PSParameter): pslib.PSParameter object
"""
self.fleet_desc_ = fleet_desc
self._fleet_desc = fleet_desc
def _set_program(self, program):
"""
......@@ -54,7 +54,7 @@ class DeviceWorker(object):
Args:
program(Program): a Program object
"""
self.program_ = program
self._program = program
def _gen_worker_desc(self, trainer_desc):
"""
......@@ -88,7 +88,7 @@ class Hogwild(DeviceWorker):
trainer_desc(TrainerDesc): a TrainerDesc object
"""
trainer_desc.device_worker_name = "HogwildWorker"
if self.infer_:
if self._infer:
# just ignore feed op for inference model
trainer_desc.hogwild_param.skip_ops.extend(["feed"])
......@@ -113,11 +113,11 @@ class DownpourSGD(DeviceWorker):
trainer_desc(TrainerDesc): a TrainerDesc object
"""
dense_table_set = set()
program_id = str(id(self.program_))
if self.program_ == None:
program_id = str(id(self._program))
if self._program == None:
print("program of current device worker is not configured")
exit(-1)
opt_info = self.program_._fleet_opt
opt_info = self._program._fleet_opt
program_configs = opt_info["program_configs"]
downpour = trainer_desc.downpour_param
......@@ -140,7 +140,7 @@ class DownpourSGD(DeviceWorker):
trainer_desc.device_worker_name = "DownpourWorker"
pull_thread = trainer_desc.pull_dense_param
pull_thread.device_num = trainer_desc.thread_num
for i in self.fleet_desc_.trainer_param.dense_table:
for i in self._fleet_desc.trainer_param.dense_table:
if i.table_id in dense_table_set:
dense_table = pull_thread.dense_table.add()
dense_table.dense_value_name.extend(i.dense_variable_name)
......@@ -148,29 +148,29 @@ class DownpourSGD(DeviceWorker):
i.table_id
sparse_table = downpour.sparse_table.add()
sparse_table.table_id = \
self.fleet_desc_.trainer_param.sparse_table[0].table_id
self._fleet_desc.trainer_param.sparse_table[0].table_id
sparse_table.sparse_key_name.extend(
self.fleet_desc_.trainer_param.sparse_table[0].slot_key)
self._fleet_desc.trainer_param.sparse_table[0].slot_key)
sparse_table.sparse_value_name.extend(
self.fleet_desc_.trainer_param.sparse_table[0].slot_value)
self._fleet_desc.trainer_param.sparse_table[0].slot_value)
sparse_table.sparse_grad_name.extend(
self.fleet_desc_.trainer_param.sparse_table[0].slot_gradient)
self._fleet_desc.trainer_param.sparse_table[0].slot_gradient)
sparse_table.emb_dim = \
self.fleet_desc_.server_param.downpour_server_param.downpour_table_param[
self._fleet_desc.server_param.downpour_server_param.downpour_table_param[
0].accessor.fea_dim - 2
sparse_table.fea_dim = sparse_table.emb_dim + 2
# TODO(guru4elephant): hard code here, need to improve
sparse_table.label_var_name = "click"
for i in self.fleet_desc_.trainer_param.dense_table:
for i in self._fleet_desc.trainer_param.dense_table:
if i.table_id in dense_table_set:
dense_table = downpour.dense_table.add()
dense_table.table_id = i.table_id
dense_table.dense_value_name.extend(i.dense_variable_name)
dense_table.dense_grad_name.extend(
i.dense_gradient_variable_name)
downpour.skip_ops.extend(self.fleet_desc_.trainer_param.skip_op)
if self.infer_:
downpour.skip_ops.extend(self._fleet_desc.trainer_param.skip_op)
if self._infer:
downpour.push_dense = False
downpour.push_sparse = False
......
python/paddle/fluid/dygraph/layers.py
浏览文件 @ 431eab64
......@@ -48,6 +48,12 @@ class Layer(core.Layer):
self._helper = LayerObjectHelper(self._full_name)
def train(self):
framework._dygraph_tracer()._train_mode()
def eval(self):
framework._dygraph_tracer()._eval_mode()
def full_name(self):
"""Full name for this layers.
......@@ -254,6 +260,12 @@ class PyLayer(core.PyLayer):
def __init__(self):
super(PyLayer, self).__init__()
def train(self):
framework._dygraph_tracer()._train_mode()
def eval(self):
framework._dygraph_tracer()._eval_mode()
@classmethod
def _do_forward(cls, inputs):
return cls._to_tuple(cls.forward(inputs))
......
python/paddle/fluid/dygraph/tracer.py
浏览文件 @ 431eab64
......@@ -24,7 +24,9 @@ __all__ = ['Tracer']
def release_op(op):
del framework._dygraph_tracer()._ops[op._trace_id]
del framework._dygraph_tracer()._ops[op._trace_id].inputs
del framework._dygraph_tracer()._ops[op._trace_id].outputs
del framework._dygraph_tracer()._ops[op._trace_id].backward_refs
class Tracer(core.Tracer):
......@@ -38,6 +40,7 @@ class Tracer(core.Tracer):
self._ops = defaultdict()
self._vars = defaultdict()
self._trace_id = 0
self._train_mode = True
def trace_var(self, name, var):
self._vars[name] = var
......@@ -46,15 +49,57 @@ class Tracer(core.Tracer):
return list((item for name, item in six.iteritems(self._vars)
if isinstance(item, framework.Parameter)))
def trace_op(self, op, stop_gradient=False):
def trace_op(self, op, inputs, outputs, stop_gradient=False):
# TODO(minqiyang): remove this line after we take apart all
# backward grads and forward variables
if self._train_mode:
op.inputs = inputs
inps = defaultdict(list)
for k, vars in six.iteritems(inputs):
if isinstance(vars, framework.Variable):
inps[k].append(vars._ivar)
elif isinstance(vars, list) or isinstance(vars, tuple):
for var in vars:
inps[k].append(var._ivar)
op.outputs = outputs
outs = defaultdict(list)
for k, vars in six.iteritems(outputs):
if isinstance(vars, framework.Variable):
outs[k].append(vars._ivar)
elif isinstance(vars, list) or isinstance(vars, tuple):
for var in vars:
outs[k].append(var._ivar)
else:
inps = defaultdict(list)
for k, vars in six.iteritems(inputs):
if isinstance(vars, framework.Variable):
op.previous_ops.append(vars.op)
inps[k].append(vars._ivar)
elif isinstance(vars, list) or isinstance(vars, tuple):
for var in vars:
op.previous_ops.append(var.op)
inps[k].append(var._ivar)
op.outputs = outputs
outs = defaultdict(list)
for k, vars in six.iteritems(outputs):
if isinstance(vars, framework.Variable):
vars.op = op
outs[k].append(vars._ivar)
elif isinstance(vars, list) or isinstance(vars, tuple):
for var in vars:
var.op = op
outs[k].append(var._ivar)
# record op's trace id
op.iop._trace_id = self._trace_id
backward_refs = self.trace(op.iop, op.inputs, op.outputs, op.attrs,
backward_refs = self.trace(op.iop, inps, outs, op.attrs,
framework._current_expected_place(),
stop_gradient)
if not stop_gradient:
if not stop_gradient and self._train_mode:
self._trace_id += 1
self._ops[op.iop._trace_id] = op
......@@ -65,10 +110,16 @@ class Tracer(core.Tracer):
# TODO(minqiyang): remove all inputs and outputs after separate
# var and grad
op.backward_refs = defaultdict(list)
for k, v in six.iteritems(op.inputs):
for k, v in six.iteritems(inputs):
if k in backward_refs:
op.backward_refs[k] = op.inputs[k]
op.backward_refs[k] = inputs[k]
for k, v in six.iteritems(op.outputs):
for k, v in six.iteritems(outputs):
if k in backward_refs:
op.backward_refs[k] = op.outputs[k]
op.backward_refs[k] = outputs[k]
def _train_mode(self):
self._train_mode = True
def _eval_mode(self):
self._train_mode = False
python/paddle/fluid/framework.py
浏览文件 @ 431eab64
......@@ -411,6 +411,7 @@ class Variable(object):
if persistable else False)
if persistable:
_dygraph_tracer().trace_var(name, self)
self.op = None
else:
self.error_clip = error_clip
......@@ -939,24 +940,7 @@ class Operator(object):
raise ValueError(
"`type` to initialized an Operator can not be None.")
self.iop = core.OpBase(type)
# TODO(minqiyang): remove these lines after we take apart all
# backward grads and forward variables
self.inputs = defaultdict(list)
if inputs is not None:
for k, v in six.iteritems(inputs):
if isinstance(v, Variable):
self.inputs[k].append(v._ivar)
elif isinstance(v, list) or isinstance(v, tuple):
self.inputs[k].extend([var._ivar for var in v])
self.outputs = defaultdict(list)
if outputs is not None:
for k, v in six.iteritems(outputs):
if isinstance(v, Variable):
self.outputs[k].append(v._ivar)
elif isinstance(v, list) or isinstance(v, tuple):
self.outputs[k].extend([var._ivar for var in v])
self.previous_ops = []
self.attrs = attrs if attrs else {}
else:
......@@ -1647,15 +1631,18 @@ class Block(object):
block=self,
desc=None,
type=kwargs.get("type", None),
inputs=kwargs.get("inputs", None),
outputs=kwargs.get("outputs", None),
attrs=kwargs.get("attrs", None))
inputs=None,
outputs=None,
attrs=kwargs.get("attrs", {}))
# record ops in tracer rather than blocks
#
# TODO(minqiyang): add op stop_gradient support in static mode too.
# currently, we only support stop_gradient in dygraph mode.
_dygraph_tracer().trace_op(op, kwargs.get("stop_gradient", False))
_dygraph_tracer().trace_op(op,
kwargs.get("inputs", {}),
kwargs.get("outputs", {}),
kwargs.get("stop_gradient", False))
else:
op_desc = self.desc.append_op()
op = Operator(
......@@ -1719,10 +1706,14 @@ class Block(object):
self,
None,
type=kwargs.get("type", None),
inputs=kwargs.get("inputs", None),
outputs=kwargs.get("outputs", None),
attrs=kwargs.get("attrs", None))
_dygraph_tracer().trace_op(op, kwargs.get("stop_gradient", False))
inputs=None,
outputs=None,
attrs=kwargs.get("attrs", {}))
_dygraph_tracer().trace_op(op,
kwargs.get("inputs", {}),
kwargs.get("outputs", {}),
kwargs.get("stop_gradient", False))
else:
op_desc = self.desc._prepend_op()
op = Operator(
......
python/paddle/fluid/incubate/fleet/base/role_maker.py
浏览文件 @ 431eab64
......@@ -23,10 +23,10 @@ class RoleMakerBase(object):
"""
def __init__(self):
self.role_maker_name_ = ""
self.trainer_endpoints_ = []
self.pserver_endpoints_ = []
self.role_is_generated_ = False
self._role_maker_name = ""
self._trainer_endpoints = []
self._pserver_endpoints = []
self._role_is_generated = False
def _is_worker(self):
"""
......@@ -45,20 +45,20 @@ class RoleMakerBase(object):
return get local ip
"""
import socket
self.ip_ = socket.gethostbyname(socket.gethostname())
return self.ip_
self._ip = socket.gethostbyname(socket.gethostname())
return self._ip
def _get_trainer_endpoints(self):
"""
return trainer endpoints
"""
return self.trainer_endpoints_
return self._trainer_endpoints
def _get_pserver_endpoints(self):
"""
return pserver endpoints
"""
return self.pserver_endpoints_
return self._pserver_endpoints
def _generate_role(self):
"""
......@@ -76,59 +76,59 @@ class MPIRoleMaker(RoleMakerBase):
def __init__(self):
super(MPIRoleMaker, self).__init__()
from mpi4py import MPI
self.comm_ = MPI.COMM_WORLD
self._comm = MPI.COMM_WORLD
self.MPI = MPI
self.ips_ = None
self._ips = None
def _get_rank(self):
"""
return rank
"""
self.rank_ = self.comm_.Get_rank()
return self.rank_
self._rank = self._comm.Get_rank()
return self._rank
def _get_size(self):
"""
return size
"""
self.size_ = self.comm_.Get_size()
return self.size_
self._size = self._comm.Get_size()
return self._size
def _all_gather(self, obj):
"""
all_gather(obj) will call MPI's allgather function
"""
self._barrier_all()
return self.comm_.allgather(obj)
return self._comm.allgather(obj)
def _worker_gather(self, obj):
"""
worker_gather(obj) will call MPI's allgather function
"""
if self._is_worker():
self.node_type_comm_.barrier()
return self.node_type_comm_.allgather(obj)
self._node_type_comm.barrier()
return self._node_type_comm.allgather(obj)
return None
def _barrier_all(self):
"""
barrier_all() will call MPI's barrier_all function
"""
self.comm_.barrier()
self._comm.barrier()
def _get_ips(self):
"""
collect current distributed job's ip list
"""
if self.ips_ == None:
self.ips_ = self.comm_.allgather(self._get_local_ip())
return self.ips_
if self._ips == None:
self._ips = self._comm.allgather(self._get_local_ip())
return self._ips
def _finalize(self):
"""
finalize the current MPI instance.
"""
self.comm_.finalize()
self._comm.finalize()
class MPISymetricRoleMaker(MPIRoleMaker):
......@@ -140,11 +140,11 @@ class MPISymetricRoleMaker(MPIRoleMaker):
def __init__(self):
super(MPISymetricRoleMaker, self).__init__()
self.node_type_ = None
self.proc_per_node_ = 2
self._node_type = None
self._proc_per_node = 2
def _check_role_generation(self):
if not self.role_is_generated_:
if not self._role_is_generated:
sys.stderr.write("generate_role() should be called first")
sys.exit(-1)
return False
......@@ -163,7 +163,7 @@ class MPISymetricRoleMaker(MPIRoleMaker):
return whether current process is worker assigned by role maker
"""
if self._check_role_generation():
return self.node_type_ == 1
return self._node_type == 1
return False
def _is_server(self):
......@@ -171,7 +171,7 @@ class MPISymetricRoleMaker(MPIRoleMaker):
return whether current process is server assigned by role maker
"""
if self._check_role_generation():
return self.node_type_ == 0
return self._node_type == 0
return False
def _worker_num(self):
......@@ -197,7 +197,7 @@ class MPISymetricRoleMaker(MPIRoleMaker):
return the index of worker
"""
if self._check_role_generation():
return self.rank_ / self.proc_per_node_
return self._rank / self._proc_per_node
return 0
def _server_index(self):
......@@ -205,7 +205,7 @@ class MPISymetricRoleMaker(MPIRoleMaker):
return the index of server
"""
if self._check_role_generation():
return self.rank_ / self.proc_per_node_
return self._rank / self._proc_per_node
return 0
def _barrier_worker(self):
......@@ -214,7 +214,7 @@ class MPISymetricRoleMaker(MPIRoleMaker):
"""
if self._check_role_generation():
if self._is_worker():
self.node_type_comm_.barrier()
self._node_type_comm.barrier()
def _barrier_server(self):
"""
......@@ -222,20 +222,20 @@ class MPISymetricRoleMaker(MPIRoleMaker):
"""
if self._check_role_generation():
if self._is_server():
self.node_type_comm_.barrier()
self._node_type_comm.barrier()
def _generate_role(self):
"""
generate currently process's role
"""
if not self.role_is_generated_:
if not self._role_is_generated:
# TODO(guru4elephant): only allow to be called once
self.trainer_endpoints_ = self._get_ips()
self.pserver_endpoints_ = self._get_ips()
self._trainer_endpoints = self._get_ips()
self._pserver_endpoints = self._get_ips()
if 0 == self._get_rank() % self.proc_per_node_ % 2:
self.node_type_ = 0
if 0 == self._get_rank() % self._proc_per_node % 2:
self._node_type = 0
else:
self.node_type_ = 1
self.node_type_comm_ = self.comm_.Split(self.node_type_)
self.role_is_generated_ = True
self._node_type = 1
self._node_type_comm = self._comm.Split(self._node_type)
self._role_is_generated = True
python/paddle/fluid/incubate/fleet/parameter_server/__init__.py
浏览文件 @ 431eab64
......@@ -64,9 +64,9 @@ class Fleet(object):
def __init__(self):
self._opt_info = None # for fleet only
self.role_maker_ = None
self.local_ip_ = 0
self.is_initialized_ = False
self._role_maker = None
self._local_ip = 0
self._is_initialized = False
def init(self):
# TODO(guru4elephant)
......@@ -78,22 +78,22 @@ class Fleet(object):
current node's role, e.g. worker, server, etc.
"""
if not self.is_initialized_:
self.role_maker_ = MPISymetricRoleMaker()
self.role_maker_._generate_role()
self._role_maker = MPISymetricRoleMaker()
self._role_maker._generate_role()
self._fleet_ptr = fluid.core.Fleet()
self.is_initialized_ = True
self._is_initialized = True
def stop(self):
"""
stop(): will be called after a user finishes his/her training task. Fleet instance will be
destroyed when stop() is called.
"""
self.role_maker_._barrier_worker()
if self.role_maker_._is_first_worker():
self._role_maker._barrier_worker()
if self._role_maker._is_first_worker():
self._fleet_ptr.stop_server()
self.role_maker_._barrier_worker()
self.role_maker_._barrier_all()
self.role_maker_._finalize()
self._role_maker._barrier_worker()
self._role_maker._barrier_all()
self._role_maker._finalize()
def init_pserver(self):
"""
......@@ -110,15 +110,15 @@ class Fleet(object):
sys.exit(-1)
self._fleet_ptr.init_server(self._dist_desc_str,
self.role_maker_._get_rank())
self.local_ip_ = self._fleet_ptr.run_server()
self._local_ip = self._fleet_ptr.run_server()
# barrier_all for init_server
self.role_maker_._barrier_all()
self.all_ips_ = self.role_maker_._all_gather(self.local_ip_)
self._role_maker._barrier_all()
self._all_ips = self._role_maker._all_gather(self.local_ip_)
self._fleet_ptr.gather_servers(self.all_ips_,
self.role_maker_._get_size())
self._fleet_ptr.gather_servers(self._all_ips,
self._role_maker._get_size())
# barrier_all for init_worker, wait all workers start
self.role_maker_._barrier_all()
self._role_maker._barrier_all()
else:
print("You should run DistributedOptimizer.minimize() first")
sys.exit(-1)
......@@ -151,21 +151,21 @@ class Fleet(object):
print("You should run DistributedOptimizer.minimize() first")
sys.exit(-1)
# barrier_all for init_server, wait for server starts
self.role_maker_._barrier_all()
self.all_ips_ = self.role_maker_._all_gather(self.local_ip_)
self._fleet_ptr.init_worker(self._dist_desc_str, self.all_ips_,
self.role_maker_._get_size(),
self.role_maker_._get_rank())
self._role_maker._barrier_all()
self._all_ips = self._role_maker._all_gather(self.local_ip_)
self._fleet_ptr.init_worker(self._dist_desc_str, self._all_ips,
self._role_maker._get_size(),
self._role_maker._get_rank())
# barrier_all for init_worker
self.role_maker_._barrier_all()
self._role_maker._barrier_all()
# prepare for client to client communication
info = self._fleet_ptr.get_clients_info()
all_info = self.role_maker_._worker_gather(info[0])
all_info = self._role_maker._worker_gather(info[0])
self._fleet_ptr.gather_clients(all_info)
self._fleet_ptr.create_client2client_connection()
# barrier for init model
self.role_maker_._barrier_worker()
if self.role_maker_._is_first_worker():
self._role_maker._barrier_worker()
if self._role_maker._is_first_worker():
tables = self._dist_desc.trainer_param.dense_table
for prog, scope in zip(programs, scopes):
prog_id = str(id(prog))
......@@ -192,7 +192,7 @@ class Fleet(object):
int(table.table_id),
var_name_list)
# barrier for init model done
self.role_maker_._barrier_worker()
self._role_maker._barrier_worker()
else:
print("You should run DistributedOptimizer.minimize() first")
sys.exit(-1)
......@@ -201,39 +201,39 @@ class Fleet(object):
"""
return the number of current job's worker num
"""
return self.role_maker_._worker_num()
return self._role_maker._worker_num()
def get_server_num(self):
"""
return the number of current job's server num
"""
return self.role_maker_._server_num()
return self._role_maker._server_num()
def get_worker_index(self):
"""
return the mpi rank of current worker
"""
return self.role_maker_._worker_index()
return self._role_maker._worker_index()
def is_worker(self):
"""
return whether current node is a worker
"""
return self.role_maker_._is_worker()
return self._role_maker._is_worker()
def is_server(self):
"""
return whether current node is pserver
"""
return self.role_maker_._is_server()
return self._role_maker._is_server()
def init_pserver_model(self):
"""
init pserver model called from pserver
"""
if self.role_maker_._is_first_worker():
if self._role_maker._is_first_worker():
self._fleet_ptr.init_model()
self.role_maker_._barrier_worker()
self._role_maker._barrier_worker()
def save_pserver_model(self, save_path):
"""
......
python/paddle/fluid/incubate/fleet/parameter_server/node.py
浏览文件 @ 431eab64
......@@ -42,13 +42,13 @@ class DownpourServer(Server):
"""
def __init__(self):
self.server_ = pslib.ServerParameter()
self.server_.downpour_server_param.service_param.start_server_port = 0
self.server_.downpour_server_param.service_param.server_class = "DownpourBrpcPsServer"
self.server_.downpour_server_param.service_param.client_class = "DownpourBrpcPsClient"
self.server_.downpour_server_param.service_param.service_class = "DownpourPsService"
self.server_.downpour_server_param.service_param.start_server_port = 0
self.server_.downpour_server_param.service_param.server_thread_num = 12
self._server = pslib.ServerParameter()
self._server.downpour_server_param.service_param.start_server_port = 0
self._server.downpour_server_param.service_param.server_class = "DownpourBrpcPsServer"
self._server.downpour_server_param.service_param.client_class = "DownpourBrpcPsClient"
self._server.downpour_server_param.service_param.service_class = "DownpourPsService"
self._server.downpour_server_param.service_param.start_server_port = 0
self._server.downpour_server_param.service_param.server_thread_num = 12
def add_sparse_table(self, table_id, learning_rate, slot_key_vars,
slot_value_var):
......@@ -62,7 +62,7 @@ class DownpourServer(Server):
Returns:
return None
"""
table = self.server_.downpour_server_param.downpour_table_param.add()
table = self._server.downpour_server_param.downpour_table_param.add()
table.table_id = table_id
table.table_class = "DownpourSparseTable"
table.type = pslib.PS_SPARSE_TABLE
......@@ -123,7 +123,7 @@ class DownpourServer(Server):
Returns:
return None
"""
table = self.server_.downpour_server_param.downpour_table_param.add()
table = self._server.downpour_server_param.downpour_table_param.add()
table.table_id = table_id
table.table_class = "DownpourDenseTable"
table.type = pslib.PS_DENSE_TABLE
......@@ -140,7 +140,7 @@ class DownpourServer(Server):
"""
Return downpour server program_desc
"""
return self.server_
return self._server
class DownpourWorker(Worker):
......@@ -155,7 +155,7 @@ class DownpourWorker(Worker):
def __init__(self, window):
self.window = window
self.worker_ = pslib.DownpourTrainerParameter()
self._worker = pslib.DownpourTrainerParameter()
def add_sparse_table(self, table_id, learning_rate, slot_key_vars,
slot_value_vars):
......@@ -187,7 +187,7 @@ class DownpourWorker(Worker):
Returns:
return None
"""
table = self.worker_.dense_table.add()
table = self._worker.dense_table.add()
table.table_id = table_id
table.dense_variable_name.extend(
filter(lambda x: x.find("embedding") == -1,
......@@ -200,4 +200,4 @@ class DownpourWorker(Worker):
"""
Return downpour worker program_desc
"""
return self.worker_
return self._worker
python/paddle/fluid/incubate/fleet/parameter_server/optimizer_factory.py
浏览文件 @ 431eab64
......@@ -24,9 +24,9 @@ from .node import DownpourWorker, DownpourServer
class DistributedOptimizerImplBase(object):
def __init__(self, optimizer):
self.optimizer_ = optimizer
self.learning_rate_ = optimizer._learning_rate
self.regularization_ = optimizer.regularization
self._optimizer = optimizer
self._learning_rate = optimizer._learning_rate
self._regularization = optimizer.regularization
def minimize(self,
losses,
......@@ -41,7 +41,7 @@ class DistributedAdam(DistributedOptimizerImplBase):
# todo(guru4elephant): add more optimizers here as argument
# todo(guru4elephant): make learning_rate as a variable
super(DistributedAdam, self).__init__(optimizer)
self.window_ = 1
self._window = 1
self.type = "downpour"
self.data_norm_name = [
".batch_size", ".batch_square_sum", ".batch_sum",
......@@ -79,9 +79,9 @@ class DistributedAdam(DistributedOptimizerImplBase):
server = DownpourServer()
worker = DownpourWorker(self.window_)
sparse_table_index = 0
server.add_sparse_table(sparse_table_index, self.learning_rate_,
server.add_sparse_table(sparse_table_index, self._learning_rate,
prefetch_slots, prefetch_slots_emb)
worker.add_sparse_table(sparse_table_index, self.learning_rate_,
worker.add_sparse_table(sparse_table_index, self._learning_rate,
prefetch_slots, prefetch_slots_emb)
dense_table_index = 1
program_configs = {}
......@@ -124,9 +124,9 @@ class DistributedAdam(DistributedOptimizerImplBase):
data_norm_grads.append(i[1])
if not is_data_norm_data:
grads.append(i[1])
server.add_dense_table(dense_table_index, self.learning_rate_,
server.add_dense_table(dense_table_index, self._learning_rate,
params, grads)
worker.add_dense_table(dense_table_index, self.learning_rate_,
worker.add_dense_table(dense_table_index, self._learning_rate,
params, grads)
program_configs[program_id]["pull_dense"] = [dense_table_index]
program_configs[program_id]["push_dense"] = [dense_table_index]
......@@ -135,9 +135,9 @@ class DistributedAdam(DistributedOptimizerImplBase):
if len(data_norm_params) != 0 and len(data_norm_grads) != 0:
dense_table_index += 1
server.add_data_norm_table(dense_table_index,
self.learning_rate_,
self._learning_rate,
data_norm_params, data_norm_grads)
worker.add_dense_table(dense_table_index, self.learning_rate_,
worker.add_dense_table(dense_table_index, self._learning_rate,
data_norm_params, data_norm_grads)
#program_config.pull_dense_table_id.extend([dense_table_index])
#program_config.push_dense_table_id.extend([dense_table_index])
......
python/paddle/fluid/layers/control_flow.py
浏览文件 @ 431eab64
......@@ -29,7 +29,8 @@ from functools import reduce
__all__ = [
'While', 'Switch', 'increment', 'array_write', 'create_array', 'less_than',
'equal', 'array_read', 'array_length', 'IfElse', 'DynamicRNN', 'StaticRNN',
'less_equal', 'greater_than', 'greater_equal', 'equal', 'not_equal',
'array_read', 'array_length', 'IfElse', 'DynamicRNN', 'StaticRNN',
'reorder_lod_tensor_by_rank', 'Print', 'is_empty'
]
......@@ -189,6 +190,7 @@ def Print(input,
'print_tensor_lod': print_tensor_lod,
'print_phase': print_phase.upper()
})
return input
class BlockGuard(object):
......@@ -267,8 +269,44 @@ class StaticRNN(object):
"""
StaticRNN class.
StaticRNN class is used to create a StaticRNN. The RNN will have its
own parameters like inputs, outputs, memories, status and length.
The StaticRNN can process a batch of sequence data. The length of each
sample sequence must be equal. The StaticRNN will have its own parameters
like inputs, outputs, memories. **Note that the first dimension of inputs
represents sequence length, and all the sequence length of inputs must be
the same. And the meaning of each axis of input and output are the same.**
Examples:
>>> import paddle.fluid as fluid
>>> import paddle.fluid.layers as layers
>>>
>>> vocab_size, hidden_size=10000, 200
>>> x = layers.data(name="x", shape=[-1, 1, 1], dtype='int64')
>>> x_emb = layers.embedding(
>>> input=x,
>>> size=[vocab_size, hidden_size],
>>> dtype='float32',
>>> is_sparse=False)
>>> x_emb = layers.transpose(x_emb, perm=[1, 0, 2])
>>>
>>> rnn = fluid.layers.StaticRNN()
>>> with rnn.step():
>>> word = rnn.step_input(x_emb)
>>> prev = rnn.memory(shape=[-1, hidden_size], batch_ref = word)
>>> hidden = fluid.layers.fc(input=[word, prev], size=hidden_size, act='relu')
>>> rnn.update_memory(prev, hidden) # set prev to hidden
>>> rnn.step_output(hidden)
>>>
>>> result = rnn()
The StaticRNN will unfold sequence into time steps. Users need to define
how to process each time step during the :code:`with` step.
The :code:`memory` is used as a staging data cross time step. The initial
value of memory can be a variable that is filled with a constant value or
a specified variable.
The StaticRNN can mark multiple variables as its output. Use `rnn()` to
get the output sequence.
"""
BEFORE_RNN_BLOCK = 0
IN_RNN_BLOCK = 1
......@@ -284,6 +322,9 @@ class StaticRNN(object):
self.seq_len = None
def step(self):
"""
The block for user to define operators in RNN.
"""
return BlockGuardWithCompletion(self)
def _assert_in_rnn_block_(self, method):
......@@ -298,13 +339,28 @@ class StaticRNN(object):
init_batch_dim_idx=0,
ref_batch_dim_idx=1):
"""
Create a memory variable for static rnn.
If the :code:`init` is not None, :code:`memory` will be initialized by
this Variable. If the :code:`init` is None, :code:`shape` and :code:`batch_ref`
must be set, and this function will initialize a :code:`init` Variable.
Args:
init: boot memory, if not set, a shape, batch_ref must be provided
shape: shape of the boot memory
batch_ref: batch size reference variable
init_value: the init value of boot memory
init_batch_dim_idx: the index of batch size in init's dimension
ref_batch_dim_idx: the index of batch size in batch_ref's dimension
init(Variable|None): The initialized variable. If it is not set,
:code:`shape` and :code:`batch_ref` must be provided.
Default: None.
shape(list|tuple): The shape of the boot memory. NOTE the shape
does not contain batch_size. Default: None.
batch_ref(Variable|None): The batch size reference Variable.
Default: None.
init_value(float): the init value of boot memory. Default: 0.0.
init_batch_dim_idx(int): the batch_size axis of the
:code:`init` Variable. Default: 0.
ref_batch_dim_idx(int): the batch_size axis of the
:code:`batch_ref` Variable. Default: 1.
Returns:
The memory variable.
"""
self._assert_in_rnn_block_('memory')
if init is None:
......@@ -343,6 +399,16 @@ class StaticRNN(object):
return pre_mem
def step_input(self, x):
"""
Mark a sequence as a StaticRNN input.
Args:
x(Variable): The input sequence, the shape of x
should be [seq_len, ...].
Returns:
The current time step in the input sequence.
"""
self._assert_in_rnn_block_('step_input')
if not isinstance(x, Variable):
raise TypeError("step input takes a Variable")
......@@ -357,6 +423,15 @@ class StaticRNN(object):
return ipt
def step_output(self, o):
"""
Mark a sequence as a StaticRNN output.
Args:
o(Variable): The output sequence.
Returns:
None.
"""
self._assert_in_rnn_block_('step_output')
if not isinstance(o, Variable):
raise TypeError("step output takes a Variable")
......@@ -376,10 +451,30 @@ class StaticRNN(object):
self.outputs.append(out_var)
def output(self, *outputs):
"""
Mark the StaticRNN output variables.
Args:
outputs: The output Variables.
Returns:
None
"""
for each in outputs:
self.step_output(each)
def update_memory(self, mem, var):
"""
Update the memory from ex_mem to new_mem. NOTE that the shape and data
type of :code:`ex_mem` and :code:`new_mem` must be same.
Args:
mem(Variable): the memory variable.
var(Variable): the plain variable generated in RNN block.
Returns:
None
"""
if not isinstance(mem, Variable) or not isinstance(var, Variable):
raise TypeError("update memory should take variables")
self.memories[mem.name].mem = var
......@@ -419,6 +514,9 @@ class StaticRNN(object):
for m in self.memories:
local_inputs.add(m)
# NOTE(zcd): the params have two categories of variables.
# - the variables that are the out of StaticRnn.
# - the variables that are the parameters of some layers, for example, conv2d.
params = list()
for op in rnn_block.ops:
assert isinstance(op, Operator)
......@@ -435,17 +533,19 @@ class StaticRNN(object):
inlinks = [parent_block.var(i.name) for i in self.inputs]
outlinks = self.outputs
# NOTE(zcd): the states maybe empty in some case.
boot_memories = []
pre_memories = []
memories = []
for _, mem in six.iteritems(self.memories):
boot_memories.append(mem.init)
pre_memories.append(mem.pre_mem.name)
assert mem.mem is not None, "%s should be updated in every step." % (
mem.init.name)
mem_var = rnn_block.var(mem.mem.name)
assert isinstance(mem_var, Variable)
new_mem = self.helper.create_variable_for_type_inference(
dtype=mem_var.dtype)
rnn_block.append_op(
type='rnn_memory_helper',
inputs={'X': [mem_var]},
......@@ -464,6 +564,7 @@ class StaticRNN(object):
outputs={'outputs': outlinks,
'step_scopes': [step_scope]},
attrs={
'has_states': len(pre_memories) > 0,
'ex_states': pre_memories,
'states': memories,
'sub_block': rnn_block
......@@ -872,6 +973,114 @@ def less_than(x, y, force_cpu=None, cond=None):
return cond
@templatedoc()
def less_equal(x, y, cond=None):
"""
This layer returns the truth value of :math:`x <= y` elementwise, which is equivalent to the overloaded operator `<=`.
Args:
x(Variable): First operand of *less_equal*
y(Variable): Second operand of *less_equal*
cond(Variable|None): Optional output variable to store the result of *less_equal*
Returns:
Variable: The tensor variable storing the output of *less_equal*.
Examples:
.. code-block:: python
out = fluid.layers.less_equal(x=label, y=limit)
"""
helper = LayerHelper("less_equal", **locals())
if cond is None:
cond = helper.create_variable_for_type_inference(dtype='bool')
cond.stop_gradient = True
attrs = dict()
if force_init_on_cpu():
attrs['force_cpu'] = force_init_on_cpu()
helper.append_op(
type='less_equal',
inputs={'X': [x],
'Y': [y]},
outputs={'Out': [cond]},
attrs=attrs)
return cond
@templatedoc()
def greater_than(x, y, cond=None):
"""
This layer returns the truth value of :math:`x > y` elementwise, which is equivalent to the overloaded operator `>`.
Args:
x(Variable): First operand of *greater_than*
y(Variable): Second operand of *greater_than*
cond(Variable|None): Optional output variable to store the result of *greater_than*
Returns:
Variable: The tensor variable storing the output of *greater_than*.
Examples:
.. code-block:: python
out = fluid.layers.greater_than(x=label, y=limit)
"""
helper = LayerHelper("greater_than", **locals())
if cond is None:
cond = helper.create_variable_for_type_inference(dtype='bool')
cond.stop_gradient = True
attrs = dict()
if force_init_on_cpu():
attrs['force_cpu'] = force_init_on_cpu()
helper.append_op(
type='greater_than',
inputs={'X': [x],
'Y': [y]},
outputs={'Out': [cond]},
attrs=attrs)
return cond
@templatedoc()
def greater_equal(x, y, cond=None):
"""
This layer returns the truth value of :math:`x >= y` elementwise, which is equivalent to the overloaded operator `>=`.
Args:
x(Variable): First operand of *greater_equal*
y(Variable): Second operand of *greater_equal*
cond(Variable|None): Optional output variable to store the result of *greater_equal*
Returns:
Variable: The tensor variable storing the output of *greater_equal*.
Examples:
.. code-block:: python
out = fluid.layers.greater_equal(x=label, y=limit)
"""
helper = LayerHelper("greater_equal", **locals())
if cond is None:
cond = helper.create_variable_for_type_inference(dtype='bool')
cond.stop_gradient = True
attrs = dict()
if force_init_on_cpu():
attrs['force_cpu'] = force_init_on_cpu()
helper.append_op(
type='greater_equal',
inputs={'X': [x],
'Y': [y]},
outputs={'Out': [cond]},
attrs=attrs)
return cond
def equal(x, y, cond=None):
"""
This layer returns the truth value of :math:`x == y` elementwise.
......@@ -900,6 +1109,34 @@ def equal(x, y, cond=None):
return cond
def not_equal(x, y, cond=None):
"""
This layer returns the truth value of :math:`x != y` elementwise, which is equivalent to the overloader operator `!=`.
Args:
x(Variable): First operand of *not_equal*
y(Variable): Second operand of *not_equal*
cond(Variable|None): Optional output variable to store the result of *not_equal*
Returns:
Variable: The tensor variable storing the output of *not_equal*.
Examples:
.. code-block:: python
out = fluid.layers.not_equal(x=label, y=limit)
"""
helper = LayerHelper("not_equal", **locals())
if cond is None:
cond = helper.create_variable_for_type_inference(dtype='bool')
cond.stop_gradient = True
helper.append_op(
type='not_equal', inputs={'X': [x],
'Y': [y]}, outputs={'Out': [cond]})
return cond
def array_read(array, i):
"""
This function performs the operation to read the data in as an
......
python/paddle/fluid/layers/detection.py
浏览文件 @ 431eab64
......@@ -509,14 +509,14 @@ def polygon_box_transform(input, name=None):
@templatedoc(op_type="yolov3_loss")
def yolov3_loss(x,
gtbox,
gtlabel,
gt_box,
gt_label,
anchors,
anchor_mask,
class_num,
ignore_thresh,
downsample_ratio,
gtscore=None,
gt_score=None,
use_label_smooth=True,
name=None):
"""
......@@ -524,12 +524,12 @@ def yolov3_loss(x,
Args:
x (Variable): ${x_comment}
gtbox (Variable): groud truth boxes, should be in shape of [N, B, 4],
gt_box (Variable): groud truth boxes, should be in shape of [N, B, 4],
in the third dimenstion, x, y, w, h should be stored
and x, y, w, h should be relative value of input image.
N is the batch number and B is the max box number in
an image.
gtlabel (Variable): class id of ground truth boxes, shoud be in shape
gt_label (Variable): class id of ground truth boxes, shoud be in shape
of [N, B].
anchors (list|tuple): ${anchors_comment}
anchor_mask (list|tuple): ${anchor_mask_comment}
......@@ -537,7 +537,7 @@ def yolov3_loss(x,
ignore_thresh (float): ${ignore_thresh_comment}
downsample_ratio (int): ${downsample_ratio_comment}
name (string): the name of yolov3 loss. Default None.
gtscore (Variable): mixup score of ground truth boxes, shoud be in shape
gt_score (Variable): mixup score of ground truth boxes, shoud be in shape
of [N, B]. Default None.
use_label_smooth (bool): ${use_label_smooth_comment}
......@@ -558,13 +558,13 @@ def yolov3_loss(x,
.. code-block:: python
x = fluid.layers.data(name='x', shape=[255, 13, 13], dtype='float32')
gtbox = fluid.layers.data(name='gtbox', shape=[6, 4], dtype='float32')
gtlabel = fluid.layers.data(name='gtlabel', shape=[6], dtype='int32')
gtscore = fluid.layers.data(name='gtscore', shape=[6], dtype='float32')
gt_box = fluid.layers.data(name='gt_box', shape=[6, 4], dtype='float32')
gt_label = fluid.layers.data(name='gt_label', shape=[6], dtype='int32')
gt_score = fluid.layers.data(name='gt_score', shape=[6], dtype='float32')
anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326]
anchor_mask = [0, 1, 2]
loss = fluid.layers.yolov3_loss(x=x, gtbox=gtbox, gtlabel=gtlabel,
gtscore=gtscore, anchors=anchors,
loss = fluid.layers.yolov3_loss(x=x, gt_box=gt_box, gt_label=gt_label,
gt_score=gt_score, anchors=anchors,
anchor_mask=anchor_mask, class_num=80,
ignore_thresh=0.7, downsample_ratio=32)
"""
......@@ -572,11 +572,11 @@ def yolov3_loss(x,
if not isinstance(x, Variable):
raise TypeError("Input x of yolov3_loss must be Variable")
if not isinstance(gtbox, Variable):
if not isinstance(gt_box, Variable):
raise TypeError("Input gtbox of yolov3_loss must be Variable")
if not isinstance(gtlabel, Variable):
if not isinstance(gt_label, Variable):
raise TypeError("Input gtlabel of yolov3_loss must be Variable")
if gtscore is not None and not isinstance(gtscore, Variable):
if gt_score is not None and not isinstance(gt_score, Variable):
raise TypeError("Input gtscore of yolov3_loss must be Variable")
if not isinstance(anchors, list) and not isinstance(anchors, tuple):
raise TypeError("Attr anchors of yolov3_loss must be list or tuple")
......@@ -602,11 +602,11 @@ def yolov3_loss(x,
inputs = {
"X": x,
"GTBox": gtbox,
"GTLabel": gtlabel,
"GTBox": gt_box,
"GTLabel": gt_label,
}
if gtscore:
inputs["GTScore"] = gtscore
if gt_score:
inputs["GTScore"] = gt_score
attrs = {
"anchors": anchors,
......@@ -1542,7 +1542,7 @@ def multi_box_head(inputs,
.. code-block:: python
mbox_locs, mbox_confs, box, var = fluid.layers.multi_box_head(
inputs=[conv1, conv2, conv3, conv4, conv5, conv5],
inputs=[conv1, conv2, conv3, conv4, conv5, conv6],
image=images,
num_classes=21,
min_ratio=20,
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 431eab64
......@@ -196,6 +196,7 @@ __all__ = [
'npair_loss',
'pixel_shuffle',
'fsp_matrix',
'continuous_value_model',
]
kIgnoreIndex = -100
......@@ -5720,12 +5721,21 @@ def hsigmoid(input,
raise ValueError(
"num_classes must not be less than 2 with default tree")
if (not is_custom) and (is_sparse):
print("Sparse mode should not be used without custom tree")
is_sparse = False
if (not is_custom) and ((path_table is not None) or
(path_code is not None)):
raise ValueError(
"only num_classes should be passed without custom tree")
if (is_custom) and (path_code is None):
raise ValueError("path_code should not be None with costum tree")
raise ValueError("path_code should not be None with custom tree")
elif (is_custom) and (path_table is None):
raise ValueError("path_table should not be None with costum tree")
raise ValueError("path_table should not be None with custom tree")
elif (is_custom) and (num_classes is None):
raise ValueError("num_classes should not be None with costum tree")
raise ValueError("num_classes should not be None with custom tree")
else:
pass
......@@ -6268,6 +6278,8 @@ def sampled_softmax_with_cross_entropy(logits,
sampled_label = helper.create_variable_for_type_inference(dtype='int64')
sampled_softlabel = helper.create_variable_for_type_inference(
dtype=logits.dtype)
logits_dim = helper.create_variable_for_type_inference(dtype=logits.dtype)
labels_dim = helper.create_variable_for_type_inference(dtype=label.type)
helper.append_op(
type='sample_logits',
......@@ -6281,7 +6293,9 @@ def sampled_softmax_with_cross_entropy(logits,
'Samples': samples,
'Probabilities': probabilities,
'SampledLabels': sampled_label,
'SampledLogits': sampled_logits
'SampledLogits': sampled_logits,
'LogitsDim': logits_dim,
'LabelsDim': labels_dim
},
attrs={
'use_customized_samples': use_customized_samples,
......@@ -11202,3 +11216,54 @@ def fsp_matrix(x, y):
input_param_name='x'))
helper.append_op(type='fsp', inputs={'X': x, 'Y': y}, outputs={'Out': out})
return out
def continuous_value_model(input, cvm, use_cvm=True):
"""
**continuous_value_model layers**
continuous value model(cvm). Now, it only considers show and click value in CTR project.
We assume that input is an embedding vector with cvm_feature, whose shape is [N * D] (D is 2 + embedding dim).
If use_cvm is True, it will log(cvm_feature), and output shape is [N * D].
If use_cvm is False, it will remove cvm_feature from input, and output shape is [N * (D - 2)].
This layer accepts a tensor named input which is ID after embedded(lod level is 1), cvm is a show_click info.
Args:
input (Variable): a 2-D LodTensor with shape [N x D], where N is the batch size, D is 2 + the embedding dim. lod level = 1.
cvm (Variable): a 2-D Tensor with shape [N x 2], where N is the batch size, 2 is show and click.
use_cvm (bool): use cvm or not. if use cvm, the output dim is the same as input
if don't use cvm, the output dim is input dim - 2(remove show and click)
(cvm op is a customized op, which input is a sequence has embedd_with_cvm default, so we need an op named cvm to decided whever use it or not.)
Returns:
Variable: A 2-D LodTensor with shape [N x D], if use cvm, D is equal to input dim, if don't use cvm, D is equal to input dim - 2.
Examples:
.. code-block:: python
input = fluid.layers.data(name="input", shape=[-1, 1], lod_level=1, append_batch_size=False, dtype="int64")#, stop_gradient=False)
label = fluid.layers.data(name="label", shape=[-1, 1], append_batch_size=False, dtype="int64")
embed = fluid.layers.embedding(
input=input,
size=[100, 11],
dtype='float32')
ones = fluid.layers.fill_constant_batch_size_like(input=label, shape=[-1, 1], dtype="int64", value=1)
show_clk = fluid.layers.cast(fluid.layers.concat([ones, label], axis=1), dtype='float32')
show_clk.stop_gradient = True
input_with_cvm = fluid.layers.continuous_value_model(embed, show_clk, True)
"""
helper = LayerHelper('cvm', **locals())
out = helper.create_variable(dtype=input.dtype)
helper.append_op(
type='cvm',
inputs={'X': [input],
'CVM': [cvm]},
outputs={'Y': [out]},
attrs={"use_cvm": use_cvm})
return out
python/paddle/fluid/layers/ops.py
浏览文件 @ 431eab64
......@@ -27,6 +27,7 @@ __activations_noattr__ = [
'tanh_shrink',
'softshrink',
'sqrt',
'rsqrt',
'abs',
'ceil',
'floor',
......@@ -81,8 +82,8 @@ def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0):
Examples:
.. code-block:: python
result = fluid.layers.uniform_random(shape=[32, 784])
result = fluid.layers.uniform_random(shape=[32, 784])
"""
locals_var = locals().keys()
......
python/paddle/fluid/layers/tensor.py
浏览文件 @ 431eab64
......@@ -28,7 +28,7 @@ __all__ = [
'tensor_array_to_tensor', 'concat', 'sums', 'assign',
'fill_constant_batch_size_like', 'fill_constant', 'argmin', 'argmax',
'argsort', 'ones', 'zeros', 'reverse', 'has_inf', 'has_nan', 'isfinite',
'range', 'linspace'
'range', 'linspace', 'zeros_like'
]
......@@ -853,3 +853,34 @@ def linspace(start, stop, num, dtype):
'Num': num},
outputs={'Out': [out]})
return out
def zeros_like(x, out=None):
"""
**zeros_like**
This function creates a zeros tensor which has identical shape and dtype
with `x`.
Args:
x(Variable): The input tensor which specifies shape and dtype.
out(Variable): The output tensor.
Returns:
Variable: The tensor variable storing the output.
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', dtype='float32', shape=[3], append_batch_size=False)
data = fluid.layers.zeros_like(x) # [0.0, 0.0, 0.0]
"""
helper = LayerHelper("zeros_like", **locals())
if out is None:
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='fill_zeros_like', inputs={'X': [x]}, outputs={'Out': [out]})
out.stop_gradient = True
return out
python/paddle/fluid/optimizer.py
浏览文件 @ 431eab64
......@@ -275,15 +275,26 @@ class Optimizer(object):
self._create_global_learning_rate()
optimize_ops = []
for param_and_grad in parameters_and_grads:
if param_and_grad[1] is None:
continue
with param_and_grad[0].block.program._optimized_guard(
param_and_grad), name_scope("optimizer"):
if param_and_grad[0].trainable is True:
optimize_op = self._append_optimize_op(global_block,
param_and_grad)
optimize_ops.append(optimize_op)
if framework.in_dygraph_mode():
for param_and_grad in parameters_and_grads:
if param_and_grad[1] is None:
continue
with param_and_grad[0].block.program._optimized_guard(
param_and_grad):
if param_and_grad[0].trainable is True:
optimize_op = self._append_optimize_op(global_block,
param_and_grad)
optimize_ops.append(optimize_op)
else:
for param_and_grad in parameters_and_grads:
if param_and_grad[1] is None:
continue
with param_and_grad[0].block.program._optimized_guard(
param_and_grad), name_scope("optimizer"):
if param_and_grad[0].trainable is True:
optimize_op = self._append_optimize_op(global_block,
param_and_grad)
optimize_ops.append(optimize_op)
# Get custom finish ops for subclasses
# FIXME: Need to fix this once we figure out how to handle dependencies
......
python/paddle/fluid/tests/book/high-level-api/cifar10_small_test_set.py
浏览文件 @ 431eab64
......@@ -88,3 +88,19 @@ def train10(batch_size=None):
paddle.dataset.common.download(CIFAR10_URL, 'cifar', CIFAR10_MD5),
'data_batch',
batch_size=batch_size)
def test10(batch_size=None):
"""
CIFAR-10 test set creator.
It returns a reader creator, each sample in the reader is image pixels in
[0, 1] and label in [0, 9].
:return: Test reader creator.
:rtype: callable
"""
return reader_creator(
paddle.dataset.common.download(CIFAR10_URL, 'cifar', CIFAR10_MD5),
'test_batch',
batch_size=batch_size)
python/paddle/fluid/tests/book/high-level-api/test_image_classification_vgg_new_api.py
浏览文件 @ 431eab64
......@@ -89,9 +89,11 @@ def train(use_cuda, train_program, parallel, params_dirname):
cifar10_small_test_set.train10(batch_size=10), buf_size=128 * 10),
batch_size=BATCH_SIZE,
drop_last=False)
# Use only part of the test set data validation program
test_reader = paddle.batch(
paddle.dataset.cifar.test10(), batch_size=BATCH_SIZE, drop_last=False)
cifar10_small_test_set.test10(BATCH_SIZE),
batch_size=BATCH_SIZE,
drop_last=False)
def event_handler(event):
if isinstance(event, EndStepEvent):
......
python/paddle/fluid/tests/test_detection.py
浏览文件 @ 431eab64
......@@ -474,17 +474,17 @@ class TestYoloDetection(unittest.TestCase):
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[30, 7, 7], dtype='float32')
gtbox = layers.data(name='gtbox', shape=[10, 4], dtype='float32')
gtlabel = layers.data(name='gtlabel', shape=[10], dtype='int32')
gtscore = layers.data(name='gtscore', shape=[10], dtype='float32')
gt_box = layers.data(name='gt_box', shape=[10, 4], dtype='float32')
gt_label = layers.data(name='gt_label', shape=[10], dtype='int32')
gt_score = layers.data(name='gt_score', shape=[10], dtype='float32')
loss = layers.yolov3_loss(
x,
gtbox,
gtlabel, [10, 13, 30, 13], [0, 1],
gt_box,
gt_label, [10, 13, 30, 13], [0, 1],
10,
0.7,
32,
gtscore=gtscore,
gt_score=gt_score,
use_label_smooth=False)
self.assertIsNotNone(loss)
......
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @ 431eab64
......@@ -25,7 +25,6 @@ endif()
list(REMOVE_ITEM TEST_OPS test_seq_concat_op) # FIXME(helin): https://github.com/PaddlePaddle/Paddle/issues/8290
list(REMOVE_ITEM TEST_OPS test_modified_huber_loss_op) # FIXME(qijun) https://github.com/PaddlePaddle/Paddle/issues/5184
list(REMOVE_ITEM TEST_OPS test_lstm_unit_op) # # FIXME(qijun) https://github.com/PaddlePaddle/Paddle/issues/5185
list(REMOVE_ITEM TEST_OPS test_recurrent_op) # FIXME(qijun) https://github.com/PaddlePaddle/Paddle/issues/6152
list(REMOVE_ITEM TEST_OPS test_cond_op) # FIXME(qijun): https://github.com/PaddlePaddle/Paddle/issues/5101#issuecomment-339814957
list(REMOVE_ITEM TEST_OPS op_test) # op_test is a helper python file, not a test
......
python/paddle/fluid/tests/unittests/test_activation_op.py
浏览文件 @ 431eab64
......@@ -192,6 +192,23 @@ class TestSqrt(TestActivation):
self.check_grad(['X'], 'Out', max_relative_error=0.007)
class TestRsqrt(TestActivation):
def setUp(self):
self.op_type = "rsqrt"
self.init_dtype()
x = np.random.uniform(0.1, 1, [2, 3]).astype(self.dtype)
out = 1.0 / np.sqrt(x)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.outputs = {'Out': out}
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out', max_relative_error=0.0005)
class TestAbs(TestActivation):
def setUp(self):
self.op_type = "abs"
......
python/paddle/fluid/tests/unittests/test_cvm_op.py 0 → 100644
浏览文件 @ 431eab64
# 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 numpy as np
from math import log
from math import exp
from op_test import OpTest
import unittest
class TestCVMOp(OpTest):
"""
Test cvm op with discrete one-hot labels.
"""
def setUp(self):
self.op_type = "cvm"
batch_size = 4
dims = 11
lod = [[1]]
self.inputs = {
'X': (np.random.uniform(0, 1, [1, dims]).astype("float32"), lod),
'CVM': np.array([[0.6, 0.4]]).astype("float32"),
}
self.attrs = {'use_cvm': False}
out = []
for index, emb in enumerate(self.inputs["X"][0]):
out.append(emb[2:])
self.outputs = {'Y': (np.array(out), lod)}
def test_check_output(self):
self.check_output()
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_dygraph_multi_forward.py 0 → 100644
浏览文件 @ 431eab64
# 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 contextlib
import unittest
import numpy as np
import six
import paddle
import paddle.fluid as fluid
from paddle.fluid import core
from paddle.fluid.optimizer import SGDOptimizer
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, FC
from paddle.fluid.dygraph.base import to_variable
from test_imperative_base import new_program_scope
class SimpleImgConvPool(fluid.dygraph.Layer):
def __init__(self,
name_scope,
num_channels,
num_filters,
filter_size,
pool_size,
pool_stride,
pool_padding=0,
pool_type='max',
global_pooling=False,
conv_stride=1,
conv_padding=0,
conv_dilation=1,
conv_groups=1,
act=None,
use_cudnn=False,
param_attr=None,
bias_attr=None):
super(SimpleImgConvPool, self).__init__(name_scope)
self._conv2d = Conv2D(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters,
filter_size=filter_size,
stride=conv_stride,
padding=conv_padding,
dilation=conv_dilation,
groups=conv_groups,
param_attr=None,
bias_attr=None,
use_cudnn=use_cudnn)
self._pool2d = Pool2D(
self.full_name(),
pool_size=pool_size,
pool_type=pool_type,
pool_stride=pool_stride,
pool_padding=pool_padding,
global_pooling=global_pooling,
use_cudnn=use_cudnn)
def forward(self, inputs):
x = self._conv2d(inputs)
x = self._pool2d(x)
return x
class MNIST(fluid.dygraph.Layer):
def __init__(self, name_scope):
super(MNIST, self).__init__(name_scope)
self._simple_img_conv_pool_1 = SimpleImgConvPool(
self.full_name(), 1, 20, 5, 2, 2, act="relu")
self._simple_img_conv_pool_2 = SimpleImgConvPool(
self.full_name(), 20, 50, 5, 2, 2, act="relu")
pool_2_shape = 50 * 4 * 4
SIZE = 10
scale = (2.0 / (pool_2_shape**2 * SIZE))**0.5
self._fc = FC(self.full_name(),
10,
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.NormalInitializer(
loc=0.0, scale=scale)),
act="softmax")
def forward(self, inputs):
x = self._simple_img_conv_pool_1(inputs)
x = self._simple_img_conv_pool_2(x)
x = self._fc(x)
return x
class TestDygraphMultiForward(unittest.TestCase):
def test_mnist_forward_float32(self):
seed = 90
epoch_num = 1
with fluid.dygraph.guard():
fluid.default_startup_program().random_seed = seed
fluid.default_main_program().random_seed = seed
mnist = MNIST("mnist")
sgd = SGDOptimizer(learning_rate=1e-3)
train_reader = paddle.batch(
paddle.dataset.mnist.train(), batch_size=128, drop_last=True)
dy_param_init_value = {}
mnist.eval()
for epoch in range(epoch_num):
for batch_id, data in enumerate(train_reader()):
dy_x_data = np.array(
[x[0].reshape(1, 28, 28)
for x in data]).astype('float32')
y_data = np.array(
[x[1] for x in data]).astype('int64').reshape(128, 1)
img = to_variable(dy_x_data)
label = to_variable(y_data)
label.stop_gradient = True
cost = mnist(img)
loss = fluid.layers.cross_entropy(cost, label)
avg_loss = fluid.layers.mean(loss)
dy_out = avg_loss.numpy()
if epoch == 0 and batch_id == 0:
for param in mnist.parameters():
dy_param_init_value[param.name] = param.numpy()
with new_program_scope():
fluid.default_startup_program().random_seed = seed
fluid.default_main_program().random_seed = seed
exe = fluid.Executor(fluid.CPUPlace(
) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
mnist = MNIST("mnist")
sgd = SGDOptimizer(learning_rate=1e-3)
train_reader = paddle.batch(
paddle.dataset.mnist.train(), batch_size=128, drop_last=True)
img = fluid.layers.data(
name='pixel', shape=[1, 28, 28], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
cost = mnist(img)
loss = fluid.layers.cross_entropy(cost, label)
avg_loss = fluid.layers.mean(loss)
# initialize params and fetch them
static_param_init_value = {}
static_param_name_list = []
for param in mnist.parameters():
static_param_name_list.append(param.name)
out = exe.run(fluid.default_startup_program(),
fetch_list=static_param_name_list)
for i in range(len(static_param_name_list)):
static_param_init_value[static_param_name_list[i]] = out[i]
for epoch in range(epoch_num):
for batch_id, data in enumerate(train_reader()):
static_x_data = np.array(
[x[0].reshape(1, 28, 28)
for x in data]).astype('float32')
y_data = np.array(
[x[1] for x in data]).astype('int64').reshape([128, 1])
fetch_list = [avg_loss.name]
out = exe.run(
fluid.default_main_program(),
feed={"pixel": static_x_data,
"label": y_data},
fetch_list=fetch_list)
static_out = out[0]
self.assertTrue(np.allclose(dy_x_data.all(), static_x_data.all()))
for key, value in six.iteritems(static_param_init_value):
self.assertTrue(np.allclose(value, dy_param_init_value[key]))
self.assertTrue(np.allclose(static_out, dy_out))
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_dyn_rnn.py
浏览文件 @ 431eab64
......@@ -24,15 +24,15 @@ from paddle.fluid.layers.control_flow import max_sequence_len
from paddle.fluid.layers.control_flow import lod_tensor_to_array
from paddle.fluid.layers.control_flow import array_to_lod_tensor
from paddle.fluid.layers.control_flow import shrink_memory
from fake_reader import fake_imdb_reader
class TestDynRNN(unittest.TestCase):
def setUp(self):
self.word_dict = paddle.dataset.imdb.word_dict()
self.word_dict_len = 5147
self.BATCH_SIZE = 2
self.train_data = paddle.batch(
paddle.dataset.imdb.train(self.word_dict),
batch_size=self.BATCH_SIZE)
reader = fake_imdb_reader(self.word_dict_len, self.BATCH_SIZE * 100)
self.train_data = paddle.batch(reader, batch_size=self.BATCH_SIZE)
def test_plain_while_op(self):
main_program = fluid.Program()
......@@ -42,7 +42,7 @@ class TestDynRNN(unittest.TestCase):
sentence = fluid.layers.data(
name='word', shape=[1], dtype='int64', lod_level=1)
sent_emb = fluid.layers.embedding(
input=sentence, size=[len(self.word_dict), 32], dtype='float32')
input=sentence, size=[self.word_dict_len, 32], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='float32')
......@@ -109,7 +109,7 @@ class TestDynRNN(unittest.TestCase):
sentence = fluid.layers.data(
name='word', shape=[1], dtype='int64', lod_level=1)
sent_emb = fluid.layers.embedding(
input=sentence, size=[len(self.word_dict), 32], dtype='float32')
input=sentence, size=[self.word_dict_len, 32], dtype='float32')
rnn = fluid.layers.DynamicRNN()
......
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @ 431eab64
......@@ -1759,10 +1759,20 @@ class TestBook(LayerTest):
def test_lod_reset(self):
# TODO(minqiyang): dygraph do not support lod now
with self.static_graph():
# case 1
x = layers.data(name='x', shape=[10], dtype='float32')
y = layers.data(
name='y', shape=[10, 20], dtype='float32', lod_level=2)
return (layers.lod_reset(x=x, y=y))
z = layers.lod_reset(x=x, y=y)
self.assertTrue(z.lod_level == 2)
# case 2
lod_tensor_in = layers.data(name='lod_in', shape=[1], dtype='int64')
z = layers.lod_reset(x=x, y=lod_tensor_in)
self.assertTrue(z.lod_level == 1)
# case 3
z = layers.lod_reset(x=x, target_lod=[1, 2, 3])
self.assertTrue(z.lod_level == 1)
return z
def test_affine_grid(self):
with self.static_graph():
......
python/paddle/fluid/tests/unittests/test_parallel_executor_transformer.py
浏览文件 @ 431eab64
......@@ -65,7 +65,9 @@ class ModelHyperParams(object):
# number of head used in multi-head attention.
n_head = 8
# number of sub-layers to be stacked in the encoder and decoder.
n_layer = 6
# NOTE(zcd): the origin number of layer is 6, to make this unit test faster,
# we should reduce the layer number to 4.
n_layer = 4
# dropout rate used by all dropout layers.
dropout = 0.1
......@@ -175,7 +177,7 @@ class TestTransformer(TestParallelExecutorBase):
self.check_network_convergence(transformer, use_cuda=True)
self.check_network_convergence(
transformer, use_cuda=True, enable_sequential_execution=True)
self.check_network_convergence(transformer, use_cuda=False, iter=5)
self.check_network_convergence(transformer, use_cuda=False, iter=2)
if __name__ == '__main__':
......
python/paddle/fluid/tests/unittests/test_recurrent_op.py
浏览文件 @ 431eab64
......@@ -15,7 +15,7 @@
from __future__ import print_function
import unittest
import paddle.fluid as fluid
import paddle.fluid.layers as layers
from paddle.fluid.framework import Program, grad_var_name
from paddle.fluid.executor import Executor
......@@ -115,10 +115,6 @@ class RecurrentOpTest1(unittest.TestCase):
def setup_program(self):
self.main_program = Program()
self.startup_program = Program()
self.p_info = {
"main_program": self.main_program,
"startup_program": self.startup_program
}
self.place = core.CPUPlace()
def setUp(self):
......@@ -129,33 +125,29 @@ class RecurrentOpTest1(unittest.TestCase):
self.output_shape = (self.sent_len, self.batch_size, self.input_dim)
self.py_rnn = PySimpleRNN1(self.input_shape, self.output_shape)
self.output = layers.mean(self.create_rnn_op(), **self.p_info)
with fluid.program_guard(self.main_program, self.startup_program):
self.output = layers.mean(self.create_rnn_op())
def create_rnn_op(self):
x = layers.data(
shape=[self.sent_len, self.batch_size, self.input_dim],
dtype='float32',
name='x',
append_batch_size=False,
**self.p_info)
append_batch_size=False)
x.stop_gradient = False
h_boot = layers.data(
shape=[self.input_dim],
dtype='float32',
name='h_boot',
**self.p_info)
shape=[self.input_dim], dtype='float32', name='h_boot')
h_boot.stop_gradient = False
rnn = layers.StaticRNN(main_program=self.main_program)
rnn = layers.StaticRNN()
with rnn.step():
h_pre = rnn.memory(init=h_boot)
x_t = rnn.step_input(x)
h = layers.scale(
x=layers.elementwise_add(
x=h_pre, y=x_t, **self.p_info),
scale=self.py_rnn.scale,
**self.p_info)
x=h_pre, y=x_t),
scale=self.py_rnn.scale)
rnn.update_memory(h_pre, h)
rnn.output(h)
......@@ -193,7 +185,8 @@ class RecurrentOpTest1(unittest.TestCase):
def test_backward(self):
self.check_forward()
append_backward(self.output)
with fluid.program_guard(self.main_program, self.startup_program):
append_backward(self.output)
ana_grad = [np.array(x) for x in self.backward()]
......@@ -205,12 +198,8 @@ class RecurrentOpTest1(unittest.TestCase):
num_grad[idx], ana_grad[idx], rtol=0.1).all())
def check_forward(self):
print('test recurrent op forward')
pd_output = self.forward()
py_output = self.py_rnn.forward()
print('pd_output', pd_output)
print
print('py_output', py_output)
self.assertEqual(pd_output.shape, py_output.shape)
self.assertTrue(np.isclose(pd_output, py_output, rtol=0.1).all())
......@@ -263,24 +252,21 @@ class RecurrentOpTest2(RecurrentOpTest1):
self.output_shape = (self.sent_len, self.batch_size, self.input_dim)
self.py_rnn = PySimpleRNN2(self.input_shape, self.output_shape)
self.output = layers.mean(self.create_rnn_op(), **self.p_info)
with fluid.program_guard(self.main_program, self.startup_program):
self.output = layers.mean(self.create_rnn_op())
def create_rnn_op(self):
x = layers.data(
shape=[self.sent_len, self.batch_size, self.input_dim],
dtype='float32',
name='x',
append_batch_size=False,
**self.p_info)
append_batch_size=False)
x.stop_gradient = False
h_boot = layers.data(
shape=[self.input_dim],
dtype='float32',
name='h_boot',
**self.p_info)
shape=[self.input_dim], dtype='float32', name='h_boot')
h_boot.stop_gradient = False
rnn = layers.StaticRNN(main_program=self.main_program)
rnn = layers.StaticRNN()
with rnn.step():
h_pre = rnn.memory(init=h_boot)
x_t = rnn.step_input(x)
......@@ -288,18 +274,13 @@ class RecurrentOpTest2(RecurrentOpTest1):
temp_l = layers.fc(input=x_t,
size=self.input_dim,
param_attr='W',
bias_attr=False,
**self.p_info)
bias_attr=False)
temp_r = layers.fc(input=h_pre,
size=self.input_dim,
param_attr='U',
bias_attr=False,
**self.p_info)
bias_attr=False)
h = layers.sigmoid(
x=layers.elementwise_add(
x=temp_l, y=temp_r, **self.p_info),
**self.p_info)
h = layers.sigmoid(x=layers.elementwise_add(x=temp_l, y=temp_r))
rnn.update_memory(h_pre, h)
rnn.output(h)
......@@ -362,40 +343,38 @@ class RecurrentOpMultipleMemoryTest(RecurrentOpTest1):
self.py_rnn = RecurrentOpMultipleMemoryTest.PySimpleRNN3(
self.input_shape, self.output_shape)
self.output = layers.mean(self.create_rnn_op(), **self.p_info)
with fluid.program_guard(self.main_program, self.startup_program):
self.output = layers.mean(self.create_rnn_op())
def create_rnn_op(self):
x = layers.data(
shape=[self.sent_len, self.batch_size, self.input_dim],
dtype='float32',
name='x',
append_batch_size=False,
**self.p_info)
append_batch_size=False)
x.stop_gradient = False
h_boot1 = layers.data(
shape=[self.batch_size, self.input_dim],
dtype='float32',
name='h_boot1',
append_batch_size=False,
**self.p_info)
append_batch_size=False)
h_boot1.stop_gradient = False
h_boot2 = layers.data(
shape=[self.batch_size, self.input_dim],
dtype='float32',
name='h_boot2',
append_batch_size=False,
**self.p_info)
append_batch_size=False)
h_boot2.stop_gradient = False
rnn = layers.StaticRNN(main_program=self.main_program)
rnn = layers.StaticRNN()
with rnn.step():
h_pre1 = rnn.memory(init=h_boot1)
h_pre2 = rnn.memory(init=h_boot2)
x_t = rnn.step_input(x)
mem1 = layers.scale(x=h_pre1, scale=1.0, **self.p_info)
mem2 = layers.scale(x=h_pre2, scale=1.0, **self.p_info)
out = layers.sums(input=[mem1, x_t, mem2], **self.p_info)
mem1 = layers.scale(x=h_pre1, scale=1.0)
mem2 = layers.scale(x=h_pre2, scale=1.0)
out = layers.sums(input=[mem1, x_t, mem2])
rnn.update_memory(h_pre1, mem1)
rnn.update_memory(h_pre2, mem2)
......@@ -446,23 +425,23 @@ class RecurrentOpNoMemBootTest(RecurrentOpTest1):
self.output_shape = (self.sent_len, self.batch_size, self.input_dim)
self.py_rnn = RecurrentOpNoMemBootTest.PySimpleRNN4(self.input_shape,
self.output_shape)
self.output = layers.mean(self.create_rnn_op(), **self.p_info)
print(self.main_program)
with fluid.program_guard(self.main_program, self.startup_program):
self.output = layers.mean(self.create_rnn_op())
def create_rnn_op(self):
x = layers.data(
shape=[self.sent_len, self.batch_size, self.input_dim],
dtype='float32',
name='x',
append_batch_size=False,
**self.p_info)
append_batch_size=False)
x.stop_gradient = False
rnn = layers.StaticRNN(main_program=self.main_program)
rnn = layers.StaticRNN()
with rnn.step():
mem_pre = rnn.memory(shape=[-1, self.input_dim], batch_ref=x)
x_t = rnn.step_input(x)
mem = layers.elementwise_add(x=mem_pre, y=x_t, **self.p_info)
mem = layers.elementwise_add(x=mem_pre, y=x_t)
rnn.update_memory(mem_pre, mem)
rnn.output(mem)
......
python/paddle/fluid/tests/unittests/test_sigmoid_cross_entropy_with_logits_op.py
浏览文件 @ 431eab64
......@@ -149,5 +149,98 @@ class TestSigmoidCrossEntropyWithNorm(OpTest):
self.check_grad(['X'], 'Out')
class TestSigmoidCrossEntropyWithLogitsOp5(OpTest):
"""Test sigmoid_cross_entropy_with_logit_op with probabalistic label
"""
def setUp(self):
self.op_type = "sigmoid_cross_entropy_with_logits"
batch_size = [10, 10]
num_classes = 20
self.inputs = {
'X': logit(
np.random.uniform(0, 1, tuple(batch_size + [num_classes]))
.astype("float32")),
'Label': np.random.uniform(0, 1, tuple(batch_size + [num_classes]))
.astype("float32")
}
# Fw Pass is implemented as elementwise sigmoid followed by
# elementwise logistic loss
# Label * -log(sigmoid(X)) + (1 - label) * -log(1 - sigmoid(X))
sigmoid_X = expit(self.inputs['X'])
term1 = self.inputs['Label'] * np.log(sigmoid_X)
term2 = (1 - self.inputs['Label']) * np.log(1 - sigmoid_X)
self.outputs = {'Out': -term1 - term2}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
class TestSigmoidCrossEntropyWithNorm2(OpTest):
def setUp(self):
self.op_type = "sigmoid_cross_entropy_with_logits"
batch_size = [10, 10]
num_classes = 20
ignore_index = -1
self.inputs = {
'X': logit(
np.random.uniform(0, 1, tuple(batch_size + [num_classes]))
.astype("float32")),
'Label': np.random.randint(-1, 2, tuple(batch_size + [num_classes]))
.astype("float32")
}
self.attrs = {'ignore_index': ignore_index, 'normalize': True}
sigmoid_X = expit(self.inputs['X'])
term1 = self.inputs['Label'] * np.log(sigmoid_X)
term2 = (1 - self.inputs['Label']) * np.log(1 - sigmoid_X)
out = -term1 - term2
out[np.where(self.inputs['Label'] == ignore_index)] = 0
if self.attrs['normalize']:
out = out / float(
np.where(self.inputs['Label'] != ignore_index)[0].size)
self.outputs = {'Out': out}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
class TestSigmoidCrossEntropyWithLogitsOp6(OpTest):
"""Test sigmoid_cross_entropy_with_logit_op with binary label
"""
def setUp(self):
self.op_type = "sigmoid_cross_entropy_with_logits"
batch_size = [10, 10]
num_classes = 20
self.inputs = {
'X': logit(
np.random.uniform(0, 1, tuple(batch_size + [num_classes]))
.astype("float32")),
'Label': np.random.randint(0, 2, tuple(batch_size + [num_classes]))
.astype("float32")
}
# Fw Pass is implemented as elementwise sigmoid followed by
# elementwise logistic loss
# Label * -log(sigmoid(X)) + (1 - label) * -log(1 - sigmoid(X))
sigmoid_X = expit(self.inputs['X'])
term1 = self.inputs['Label'] * np.log(sigmoid_X)
term2 = (1 - self.inputs['Label']) * np.log(1 - sigmoid_X)
self.outputs = {'Out': -term1 - term2}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/trainer_desc.py
浏览文件 @ 431eab64
......@@ -28,10 +28,10 @@ class TrainerDesc(object):
import multiprocessing as mp
# set default thread num == cpu count
self.proto_desc.thread_num = mp.cpu_count()
self.fleet_desc_ = None
self.device_worker_ = None
self.program_ = None
self.infer_ = False
self._fleet_desc = None
self._device_worker = None
self._program = None
self._infer = False
def _set_fetch_var_and_info(self, fetch_vars, fetch_info, print_period):
for i, v in enumerate(fetch_vars):
......@@ -47,19 +47,19 @@ class TrainerDesc(object):
self.proto_desc.thread_num = thread_num
def _set_device_worker(self, device_worker):
self.device_worker_ = device_worker
self._device_worker = device_worker
def _set_infer(self, infer):
self.infer_ = infer
self._infer = infer
def _set_fleet_desc(self, fleet_desc):
self.fleet_desc_ = fleet_desc
self._fleet_desc = fleet_desc
def _gen_trainer_desc(self):
pass
def _set_program(self, program):
self.program_ = program
self._program = program
def _desc(self):
from google.protobuf import text_format
......@@ -73,13 +73,13 @@ class MultiTrainer(TrainerDesc):
def _set_program(self, program):
super(MultiTrainer, self)._set_program(program)
self.program_ = program
self._program = program
def _gen_trainer_desc(self):
super(MultiTrainer, self)._gen_trainer_desc()
self.proto_desc.class_name = "MultiTrainer"
self.device_worker_._set_infer(self.infer_)
self.device_worker_._gen_worker_desc(self.proto_desc)
self._device_worker._set_infer(self._infer)
self._device_worker._gen_worker_desc(self.proto_desc)
class DistMultiTrainer(TrainerDesc):
......@@ -89,13 +89,13 @@ class DistMultiTrainer(TrainerDesc):
def _set_program(self, program):
super(DistMultiTrainer, self)._set_program(program)
self.program_ = program
self._program = program
def _gen_trainer_desc(self):
super(DistMultiTrainer, self)._gen_trainer_desc()
self.proto_desc.class_name = "DistMultiTrainer"
if self.program_ == None:
if self._program == None:
raise RuntimeError("None Program")
self.device_worker_._set_infer(self.infer_)
self.device_worker_._set_program(self.program_)
self.device_worker_._gen_worker_desc(self.proto_desc)
self._device_worker._set_infer(self._infer)
self._device_worker._set_program(self._program)
self._device_worker._gen_worker_desc(self.proto_desc)
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @ 431eab64
......@@ -658,6 +658,7 @@ class DistributeTranspiler(object):
outputs={"Out": splited_var},
attrs={
"epmap": eps,
"trainer_id": self.trainer_id,
RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
})
......@@ -669,6 +670,7 @@ class DistributeTranspiler(object):
outputs={"Out": fetch_barrier_out},
attrs={
"endpoints": self.pserver_endpoints,
"trainer_id": self.trainer_id,
RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
})
......@@ -791,11 +793,15 @@ class DistributeTranspiler(object):
global_ops = []
# sparse grad name to param name
sparse_grad_to_param = []
def __append_optimize_op__(op, block, grad_to_block_id, merged_var,
lr_ops):
if self._is_optimizer_op(op):
self._append_pserver_ops(block, op, endpoint, grad_to_block_id,
self.origin_program, merged_var)
self.origin_program, merged_var,
sparse_grad_to_param)
elif op not in lr_ops:
self._append_pserver_non_opt_ops(block, op)
......@@ -911,6 +917,7 @@ class DistributeTranspiler(object):
"Fanin": self.trainer_num,
"sync_mode": self.sync_mode,
"grad_to_block_id": grad_to_block_id,
"sparse_grad_to_param": sparse_grad_to_param,
}
if self.has_distributed_lookup_table:
......@@ -1779,7 +1786,8 @@ class DistributeTranspiler(object):
return o4
def _append_pserver_ops(self, optimize_block, opt_op, endpoint,
grad_to_block_id, origin_program, merged_var):
grad_to_block_id, origin_program, merged_var,
sparse_grad_to_param):
program = optimize_block.program
pserver_block = program.global_block()
new_inputs = collections.OrderedDict()
......@@ -1863,6 +1871,12 @@ class DistributeTranspiler(object):
outputs=outputs,
attrs=opt_op.all_attrs())
# record sparse grad to param name
if new_inputs["Grad"].type == core.VarDesc.VarType.SELECTED_ROWS:
sparse_grad_to_param.append(
str(new_inputs["Grad"].name) + ":" + str(new_inputs["Param"]
.name))
def _get_pserver_grad_param_var(self, var, var_dict):
"""
Return pserver side grad/param variable, return None
......
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