Merge remote-tracking branch 'upstream/develop' into dev

test=develop

CMakeLists.txt

@@ -71,7 +71,8 @@ option(ANAKIN_BUILD_CROSS_PLANTFORM "Build anakin lib for any nvidia device plan
 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 high-level api interface"  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)

cmake/external/protobuf.cmake

@@ -221,6 +221,7 @@ FUNCTION(build_protobuf TARGET_NAME BUILD_FOR_HOST)
             -DCMAKE_BUILD_TYPE=${THIRD_PARTY_BUILD_TYPE}
             -DCMAKE_INSTALL_PREFIX=${PROTOBUF_INSTALL_DIR}
             -DCMAKE_INSTALL_LIBDIR=lib
+            -DBUILD_SHARED_LIBS=OFF
         CMAKE_CACHE_ARGS
             -DCMAKE_INSTALL_PREFIX:PATH=${PROTOBUF_INSTALL_DIR}
             -DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE}

paddle/fluid/API.spec

@@ -13,6 +13,7 @@ paddle.fluid.name_scope (ArgSpec(args=['prefix'], varargs=None, keywords=None, d
 paddle.fluid.cuda_places (ArgSpec(args=['device_ids'], varargs=None, keywords=None, defaults=(None,)), ('document', '7d9a51fc9cf3c5245b5227080a8064c3'))
 paddle.fluid.cpu_places (ArgSpec(args=['device_count'], varargs=None, keywords=None, defaults=(None,)), ('document', '4c0cd83f0b401fc2ff84c70974e5d210'))
 paddle.fluid.cuda_pinned_places (ArgSpec(args=['device_count'], varargs=None, keywords=None, defaults=(None,)), ('document', 'd0c3ebd813c39958c92b78e3eef7e912'))
+paddle.fluid.in_dygraph_mode (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', 'f06314a1cb30c96b5808dde2219c2dae'))
 paddle.fluid.Executor.__init__ (ArgSpec(args=['self', 'place'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
 paddle.fluid.Executor.close (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'f5369953dd0c443961cf79f7a00e1a03'))
 paddle.fluid.Executor.infer_from_dataset (ArgSpec(args=['self', 'program', 'dataset', 'scope', 'thread', 'debug', 'fetch_list', 'fetch_info', 'print_period'], varargs=None, keywords=None, defaults=(None, None, None, 0, False, None, None, 100)), ('document', '9c7decb955b9c4f718114179c8985581'))
@@ -117,6 +118,8 @@ paddle.fluid.layers.reduce_mean (ArgSpec(args=['input', 'dim', 'keep_dim', 'name
 paddle.fluid.layers.reduce_max (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', '66a622db727551761ce4eb73eaa7f6a4'))
 paddle.fluid.layers.reduce_min (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'd50ac552b5d131468ed466d08bb2d38c'))
 paddle.fluid.layers.reduce_prod (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'fcd8301a0ce15f219c7a4bcd0c1e8eca'))
+paddle.fluid.layers.reduce_all (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', '646ca4d4a2cc16084f59de44b6927eca'))
+paddle.fluid.layers.reduce_any (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'f36661060aeeaf6c6b1331e41b3726fa'))
 paddle.fluid.layers.sequence_first_step (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', '2b290d3d77882bfe9bb8d331cac8cdd3'))
 paddle.fluid.layers.sequence_last_step (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', 'c16a892f44f7fe71bfa5afc32d3f34ce'))
 paddle.fluid.layers.sequence_slice (ArgSpec(args=['input', 'offset', 'length', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'fdcea0e8b5bc7d8d4b1b072c521014e6'))
@@ -124,7 +127,7 @@ paddle.fluid.layers.dropout (ArgSpec(args=['x', 'dropout_prob', 'is_test', 'seed
 paddle.fluid.layers.split (ArgSpec(args=['input', 'num_or_sections', 'dim', 'name'], varargs=None, keywords=None, defaults=(-1, None)), ('document', '652625345c2acb900029c78cc75f8aa6'))
 paddle.fluid.layers.ctc_greedy_decoder (ArgSpec(args=['input', 'blank', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ebbf2adbd79683dc93db03454dfa18c2'))
 paddle.fluid.layers.edit_distance (ArgSpec(args=['input', 'label', 'normalized', 'ignored_tokens'], varargs=None, keywords=None, defaults=(True, None)), ('document', '97f0262f97602644c83142789d784571'))
-paddle.fluid.layers.l2_normalize (ArgSpec(args=['x', 'axis', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(1e-12, None)), ('document', '6e428384ce6a77207fa2c70d9f011990'))
+paddle.fluid.layers.l2_normalize (ArgSpec(args=['x', 'axis', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(1e-12, None)), ('document', '35c6a241bcc1a1fc89508860d82ad62b'))
 paddle.fluid.layers.matmul (ArgSpec(args=['x', 'y', 'transpose_x', 'transpose_y', 'alpha', 'name'], varargs=None, keywords=None, defaults=(False, False, 1.0, None)), ('document', 'b4cbe1ac451005df6dad12e9ffdccca9'))
 paddle.fluid.layers.topk (ArgSpec(args=['input', 'k', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'd3570c02f71bcd78e60b3f31dc8f5b32'))
 paddle.fluid.layers.warpctc (ArgSpec(args=['input', 'label', 'blank', 'norm_by_times', 'use_cudnn'], varargs=None, keywords=None, defaults=(0, False, False)), ('document', 'aaba49c038ba927f0a8e45c0c9a686ab'))
@@ -155,10 +158,10 @@ paddle.fluid.layers.label_smooth (ArgSpec(args=['label', 'prior_dist', 'epsilon'
 paddle.fluid.layers.roi_pool (ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1, 1, 1.0)), ('document', 'c317aa595deb31649083c8faa91cdb97'))
 paddle.fluid.layers.roi_align (ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale', 'sampling_ratio', 'name'], varargs=None, keywords=None, defaults=(1, 1, 1.0, -1, None)), ('document', '12c5bbb8b38c42e623fbc47611d766e1'))
 paddle.fluid.layers.dice_loss (ArgSpec(args=['input', 'label', 'epsilon'], varargs=None, keywords=None, defaults=(1e-05,)), ('document', '1ba0508d573f65feecf3564dce22aa1d'))
-paddle.fluid.layers.image_resize (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'resample', 'actual_shape', 'align_corners', 'align_mode'], varargs=None, keywords=None, defaults=(None, None, None, 'BILINEAR', None, True, 1)), ('document', '7a1966d7c3a48f1fc0881cdaf5d83b0b'))
+paddle.fluid.layers.image_resize (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'resample', 'actual_shape', 'align_corners', 'align_mode'], varargs=None, keywords=None, defaults=(None, None, None, 'BILINEAR', None, True, 1)), ('document', 'd1b08c11bb9277386fcf6ae70b6622d1'))
 paddle.fluid.layers.image_resize_short (ArgSpec(args=['input', 'out_short_len', 'resample'], varargs=None, keywords=None, defaults=('BILINEAR',)), ('document', '06211aefc50c5a3e940d7204d859cdf7'))
-paddle.fluid.layers.resize_bilinear (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners', 'align_mode'], varargs=None, keywords=None, defaults=(None, None, None, None, True, 1)), ('document', 'e4fb4ed511b2293b8f04f7e872afbfd7'))
-paddle.fluid.layers.resize_nearest (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners'], varargs=None, keywords=None, defaults=(None, None, None, None, True)), ('document', '735fa9758a6d7ff3b47d7b827f961c1d'))
+paddle.fluid.layers.resize_bilinear (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners', 'align_mode'], varargs=None, keywords=None, defaults=(None, None, None, None, True, 1)), ('document', 'c45591fbc4f64a178fbca219e1546a58'))
+paddle.fluid.layers.resize_nearest (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners'], varargs=None, keywords=None, defaults=(None, None, None, None, True)), ('document', 'ae6d73cdc7f3a138d8a338ecdb33c1ae'))
 paddle.fluid.layers.gather (ArgSpec(args=['input', 'index'], varargs=None, keywords=None, defaults=None), ('document', '98f1c86716b9b7f4dda83f20e2adeee2'))
 paddle.fluid.layers.scatter (ArgSpec(args=['input', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '65f8e9d8ddfd0b412f940579c4faa342'))
 paddle.fluid.layers.sequence_scatter (ArgSpec(args=['input', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '15b522457dfef103f0c20ca9d397678b'))
@@ -203,6 +206,7 @@ paddle.fluid.layers.gaussian_random_batch_size_like (ArgSpec(args=['input', 'sha
 paddle.fluid.layers.sum (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', 'a418e3ccb5e2ac21bd60f5cc221d5860'))
 paddle.fluid.layers.slice (ArgSpec(args=['input', 'axes', 'starts', 'ends'], varargs=None, keywords=None, defaults=None), ('document', '01dbb91e7c74cb11336cd531013de51a'))
 paddle.fluid.layers.shape (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', '17db0f814eb7bb5a3fac1ca6e60e16d8'))
+paddle.fluid.layers.rank (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', 'ee1386c42ecc8f424fe3fb21862fefc2'))
 paddle.fluid.layers.logical_and (ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'cdcf20c494c92060d10feb9374532f42'))
 paddle.fluid.layers.logical_or (ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '0eae3f726a4afe590757552fa3ced012'))
 paddle.fluid.layers.logical_xor (ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'b0daaa3fa4a0aa62f9b58c43d959eb25'))
@@ -235,7 +239,7 @@ paddle.fluid.layers.huber_loss (ArgSpec(args=['input', 'label', 'delta'], vararg
 paddle.fluid.layers.kldiv_loss (ArgSpec(args=['x', 'target', 'reduction', 'name'], varargs=None, keywords=None, defaults=('mean', None)), ('document', '776d536cac47c89073abc7ee524d5aec'))
 paddle.fluid.layers.tree_conv (ArgSpec(args=['nodes_vector', 'edge_set', 'output_size', 'num_filters', 'max_depth', 'act', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(1, 2, 'tanh', None, None, None)), ('document', '34ea12ac9f10a65dccbc50100d12e607'))
 paddle.fluid.layers.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', 'ad669cdf83e72a69ebc5ed79e36486de'))
+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', '88046160ef4bbd28f18fa6484d95b75c'))
 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'))
@@ -272,6 +276,7 @@ paddle.fluid.layers.has_inf (ArgSpec(args=['x'], varargs=None, keywords=None, de
 paddle.fluid.layers.has_nan (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '2e53e83127dbfd86e7098bdfe9a549e8'))
 paddle.fluid.layers.isfinite (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '0a437011c3906079fd8947ed3e52d292'))
 paddle.fluid.layers.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.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'))
@@ -361,8 +366,7 @@ paddle.fluid.layers.inverse_time_decay (ArgSpec(args=['learning_rate', 'decay_st
 paddle.fluid.layers.polynomial_decay (ArgSpec(args=['learning_rate', 'decay_steps', 'end_learning_rate', 'power', 'cycle'], varargs=None, keywords=None, defaults=(0.0001, 1.0, False)), ('document', '882634f420f626642f0874481263da40'))
 paddle.fluid.layers.piecewise_decay (ArgSpec(args=['boundaries', 'values'], varargs=None, keywords=None, defaults=None), ('document', 'c717d9d1d78a53c809d01b8bc56f3cae'))
 paddle.fluid.layers.noam_decay (ArgSpec(args=['d_model', 'warmup_steps'], varargs=None, keywords=None, defaults=None), ('document', 'd9a95746353fd574be36dc28d8726c28'))
-paddle.fluid.layers.append_LARS (ArgSpec(args=['params_grads', 'learning_rate', 'weight_decay'], varargs=None, keywords=None, defaults=None), ('document', 'd24fa1e7d62ac8a534fc6a86002f84f8'))
-paddle.fluid.layers.cosine_decay (ArgSpec(args=['learning_rate', 'step_each_epoch', 'epochs'], varargs=None, keywords=None, defaults=None), ('document', '9588c64c26ffaef3c466e404a6af9d9b'))
+paddle.fluid.layers.cosine_decay (ArgSpec(args=['learning_rate', 'step_each_epoch', 'epochs'], varargs=None, keywords=None, defaults=None), ('document', 'f8b2727bccf0f368c997d7cf05847e49'))
 paddle.fluid.layers.linear_lr_warmup (ArgSpec(args=['learning_rate', 'warmup_steps', 'start_lr', 'end_lr'], varargs=None, keywords=None, defaults=None), ('document', '2ef3f5ca5cd71ea4217c418e5a7a0565'))
 paddle.fluid.contrib.InitState.__init__ (ArgSpec(args=['self', 'init', 'shape', 'value', 'init_boot', 'need_reorder', 'dtype'], varargs=None, keywords=None, defaults=(None, None, 0.0, None, False, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
 paddle.fluid.contrib.StateCell.__init__ (ArgSpec(args=['self', 'inputs', 'states', 'out_state', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))

paddle/fluid/framework/data_feed.cc

@@ -72,7 +72,6 @@ bool DataFeed::PickOneFile(std::string* filename) {
   }
   VLOG(3) << "file_idx_=" << *file_idx_;
   *filename = filelist_[(*file_idx_)++];
-  // LOG(ERROR) << "pick file:" << *filename;
   return true;
 }
 
@@ -466,6 +465,17 @@ void MultiSlotDataFeed::Init(
     if (slot.is_used()) {
       use_slots_.push_back(all_slots_[i]);
       use_slots_is_dense_.push_back(slot.is_dense());
+      std::vector<int> local_shape;
+      if (slot.is_dense()) {
+        // for batch size holder if is_dense
+        if (slot.shape(0) > 0) {
+          local_shape.push_back(0);
+        }
+      }
+      for (size_t i = 0; i < slot.shape_size(); ++i) {
+        local_shape.push_back(slot.shape(i));
+      }
+      use_slots_shape_.push_back(local_shape);
     }
   }
   feed_vec_.resize(use_slots_.size());
@@ -752,8 +762,8 @@ void MultiSlotDataFeed::PutToFeedVec(
     LoD data_lod{offset};
     feed_vec_[i]->set_lod(data_lod);
     if (use_slots_is_dense_[i]) {
-      int dim = total_instance / batch_size_;
-      feed_vec_[i]->Resize({batch_size_, dim});
+      use_slots_shape_[i][0] = batch_size_;
+      feed_vec_[i]->Resize(framework::make_ddim(use_slots_shape_[i]));
     }
   }
 #endif
@@ -785,6 +795,16 @@ void MultiSlotInMemoryDataFeed::Init(
     if (slot.is_used()) {
       use_slots_.push_back(all_slots_[i]);
       use_slots_is_dense_.push_back(slot.is_dense());
+      std::vector<int> local_shape;
+      if (slot.is_dense()) {
+        if (slot.shape(0) > 0) {
+          local_shape.push_back(0);
+        }
+      }
+      for (size_t i = 0; i < slot.shape_size(); ++i) {
+        local_shape.push_back(slot.shape(i));
+      }
+      use_slots_shape_.push_back(local_shape);
     }
   }
   feed_vec_.resize(use_slots_.size());
@@ -940,8 +960,8 @@ void MultiSlotInMemoryDataFeed::PutToFeedVec(
     LoD data_lod{offset};
     feed_vec_[i]->set_lod(data_lod);
     if (use_slots_is_dense_[i]) {
-      int dim = total_instance / batch_size_;
-      feed_vec_[i]->Resize({batch_size_, dim});
+      use_slots_shape_[i][0] = batch_size_;
+      feed_vec_[i]->Resize(framework::make_ddim(use_slots_shape_[i]));
     }
   }
 #endif

paddle/fluid/framework/data_feed.h

@@ -142,6 +142,7 @@ class DataFeed {
   // object)
   std::vector<std::string> all_slots_;
   std::vector<std::string> all_slots_type_;
+  std::vector<std::vector<int>> use_slots_shape_;
   std::vector<int>
       use_slots_index_;  // -1: not used; >=0: the index of use_slots_
 

paddle/fluid/framework/data_feed.proto

@@ -19,6 +19,7 @@ message Slot {
   required string type = 2;
   optional bool is_dense = 3 [ default = false ];
   optional bool is_used = 4 [ default = false ];
+  repeated int32 shape = 5; // we can define N-D Tensor
 }
 
 message MultiSlotDesc { repeated Slot slots = 1; }

paddle/fluid/framework/details/build_strategy.cc

@@ -150,6 +150,11 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
       AppendPass("runtime_context_cache_pass");
     }
 
+    if (strategy_.cache_expected_kernel_) {
+      VLOG(10) << "Add expected_kernel_cache_pass";
+      AppendPass("expected_kernel_cache_pass");
+    }
+
     AppendMultiDevPass(strategy_);
 
     if (strategy_.fuse_all_reduce_ops_) {
@@ -337,3 +342,4 @@ USE_PASS(fuse_adam_op_pass);
 USE_PASS(fuse_sgd_op_pass);
 USE_PASS(fuse_all_reduce_op_pass);
 USE_PASS(runtime_context_cache_pass);
+USE_PASS(expected_kernel_cache_pass);

paddle/fluid/framework/details/build_strategy.h

@@ -83,11 +83,11 @@ struct BuildStrategy {
 
   bool sync_batch_norm_{false};
 
-  bool memory_optimize_{true};
-  // TODO(dzhwinter):
-  // make enable_inplace, memory_optimize_
-  // memory_early_delete_ true by default
-  bool enable_inplace_{true};
+  // FIXME(liuwei1031) disable memory_optimzie and enable_inplace in 1.4
+  // to open them by default, we need to solve the fetch variable issue
+  bool memory_optimize_{false};
+
+  bool enable_inplace_{false};
 
   bool enable_sequential_execution_{false};
 
@@ -108,6 +108,7 @@ struct BuildStrategy {
   bool remove_unnecessary_lock_{true};
 
   bool cache_runtime_context_{false};
+  bool cache_expected_kernel_{true};
 
   // NOTE:
   // Before you add new options, think if it's a general strategy that works

paddle/fluid/framework/details/inplace_op_pass.cc

@@ -305,6 +305,12 @@ void InplacePass::TryInplaceOpInputOutput(ir::Node* op,
 
     VLOG(4) << "Try to inplace " << in_var_name << " with " << out_var_name;
 
+    if (var_nodes_[in_var_name].back() != in_node) {
+      VLOG(4) << "SKIP since " << in_var_name
+              << " is also used as output by other ops";
+      continue;
+    }
+
     bool can_replace = true;
     if (in_var_name == out_var_name) {
       can_replace = false;
@@ -527,6 +533,9 @@ void GraphView::Build(ir::Graph* g) {
   };
   for (auto& node : g->Nodes()) {
     if (!node->IsOp()) continue;
+    // avoid optimize the variable used in sub-blocks
+    if (OpHasSubBlock(node->Op())) update_skip_set(node);
+
     if (node->Name() == "send") update_skip_set(node);
     if (node->Name() == "recv") update_skip_set(node);
     if (node->Name() == "prefetch") update_skip_set(node);

paddle/fluid/framework/details/op_registry.h

@@ -233,6 +233,12 @@ struct OpInfoFiller<T, kNoNeedBufferVarsInference> {
   }
 };
 
+// A fake OpInfoFiller of void
+template <>
+struct OpInfoFiller<void, kUnknown> {
+  void operator()(const char* op_type, OpInfo* info) const {}
+};
+
 }  // namespace details
 
 }  // namespace framework

paddle/fluid/framework/downpour_worker.cc

@@ -21,40 +21,40 @@ namespace framework {
 
 void DownpourWorker::Initialize(const TrainerDesc& desc) {
   param_ = desc.downpour_param();
-  for (size_t i = 0; i < param_.sparse_table_size(); ++i) {
+  for (int i = 0; i < param_.sparse_table_size(); ++i) {
     uint64_t table_id =
         static_cast<uint64_t>(param_.sparse_table(i).table_id());
     TableParameter table = param_.sparse_table(i);
     sparse_key_names_[table_id].resize(table.sparse_key_name_size());
-    for (size_t j = 0; j < table.sparse_key_name_size(); ++j) {
+    for (int j = 0; j < table.sparse_key_name_size(); ++j) {
       sparse_key_names_[table_id][j] = table.sparse_key_name(j);
     }
     sparse_value_names_[table_id].resize(table.sparse_value_name_size());
-    for (size_t j = 0; j < table.sparse_value_name_size(); ++j) {
+    for (int j = 0; j < table.sparse_value_name_size(); ++j) {
       sparse_value_names_[table_id][j] = table.sparse_value_name(j);
     }
     sparse_grad_names_[table_id].resize(table.sparse_grad_name_size());
-    for (size_t j = 0; j < table.sparse_grad_name_size(); ++j) {
+    for (int j = 0; j < table.sparse_grad_name_size(); ++j) {
       sparse_grad_names_[table_id][j] = table.sparse_grad_name(j);
     }
     label_var_name_[table_id] = table.label_var_name();
   }
 
-  for (size_t i = 0; i < param_.dense_table_size(); ++i) {
+  for (int i = 0; i < param_.dense_table_size(); ++i) {
     uint64_t table_id = static_cast<uint64_t>(param_.dense_table(i).table_id());
     auto table = param_.dense_table(i);
     dense_value_names_[table_id].resize(table.dense_value_name_size());
-    for (size_t j = 0; j < table.dense_value_name_size(); ++j) {
+    for (int j = 0; j < table.dense_value_name_size(); ++j) {
       dense_value_names_[table_id][j] = table.dense_value_name(j);
     }
     dense_grad_names_[table_id].resize(table.dense_grad_name_size());
-    for (size_t j = 0; j < table.dense_grad_name_size(); ++j) {
+    for (int j = 0; j < table.dense_grad_name_size(); ++j) {
       dense_grad_names_[table_id][j] = table.dense_grad_name(j);
     }
   }
 
   skip_ops_.resize(param_.skip_ops_size());
-  for (size_t i = 0; i < param_.skip_ops_size(); ++i) {
+  for (int i = 0; i < param_.skip_ops_size(); ++i) {
     skip_ops_[i] = param_.skip_ops(i);
   }
 
@@ -83,14 +83,14 @@ void DownpourWorker::CollectLabelInfo(size_t table_idx) {
   LoDTensor* tensor = var->GetMutable<LoDTensor>();
   int64_t* label_ptr = tensor->data<int64_t>();
 
-  int global_index = 0;
+  size_t global_index = 0;
   for (size_t i = 0; i < sparse_key_names_[table_id].size(); ++i) {
     VLOG(3) << "sparse_key_names_[" << i
             << "]: " << sparse_key_names_[table_id][i];
     Variable* fea_var = thread_scope_->FindVar(sparse_key_names_[table_id][i]);
     LoDTensor* tensor = fea_var->GetMutable<LoDTensor>();
     int64_t* ids = tensor->data<int64_t>();
-    int fea_idx = 0;
+    size_t fea_idx = 0;
     // tensor->lod()[0].size() == batch_size + 1
     for (auto lod_idx = 1u; lod_idx < tensor->lod()[0].size(); ++lod_idx) {
       for (; fea_idx < tensor->lod()[0][lod_idx]; ++fea_idx) {
@@ -138,7 +138,7 @@ void DownpourWorker::FillSparseValue(size_t table_idx) {
     auto& tensor_lod = tensor->lod()[0];
     LoD data_lod{tensor_lod};
     tensor_emb->set_lod(data_lod);
-    for (auto index = 0u; index < len; ++index) {
+    for (int index = 0; index < len; ++index) {
       if (ids[index] == 0u) {
         memcpy(ptr + table.emb_dim() * index, init_value.data() + 2,
                sizeof(float) * table.emb_dim());
@@ -192,7 +192,7 @@ void DownpourWorker::TrainFilesWithProfiler() {
     read_time += timeline.ElapsedSec();
     total_time += timeline.ElapsedSec();
     VLOG(3) << "program config size: " << param_.program_config_size();
-    for (size_t i = 0; i < param_.program_config(0).pull_sparse_table_id_size();
+    for (int i = 0; i < param_.program_config(0).pull_sparse_table_id_size();
          ++i) {
       uint64_t tid = static_cast<uint64_t>(
           param_.program_config(0).pull_sparse_table_id(i));
@@ -244,8 +244,8 @@ void DownpourWorker::TrainFilesWithProfiler() {
     }
 
     if (need_to_push_sparse_) {
-      for (size_t i = 0;
-           i < param_.program_config(0).push_sparse_table_id_size(); ++i) {
+      for (int i = 0; i < param_.program_config(0).push_sparse_table_id_size();
+           ++i) {
         uint64_t tid = static_cast<uint64_t>(
             param_.program_config(0).push_sparse_table_id(i));
         TableParameter table;
@@ -268,8 +268,8 @@ void DownpourWorker::TrainFilesWithProfiler() {
 
     if (need_to_push_dense_) {
       timeline.Start();
-      for (size_t i = 0;
-           i < param_.program_config(0).push_dense_table_id_size(); ++i) {
+      for (int i = 0; i < param_.program_config(0).push_dense_table_id_size();
+           ++i) {
         uint64_t tid = static_cast<uint64_t>(
             param_.program_config(0).push_dense_table_id(i));
         fleet_ptr_->PushDenseVarsAsync(
@@ -315,8 +315,8 @@ void DownpourWorker::TrainFilesWithProfiler() {
     }
 
     if (need_to_push_dense_) {
-      for (size_t i = 0;
-           i < param_.program_config(0).push_dense_table_id_size(); ++i) {
+      for (int i = 0; i < param_.program_config(0).push_dense_table_id_size();
+           ++i) {
         uint64_t tid = static_cast<uint64_t>(
             param_.program_config(0).push_dense_table_id(i));
         pull_dense_worker_->IncreaseThreadVersion(thread_id_, tid);
@@ -362,7 +362,7 @@ void DownpourWorker::TrainFiles() {
   int cur_batch;
   while ((cur_batch = device_reader_->Next()) > 0) {
     // pull sparse here
-    for (size_t i = 0; i < param_.program_config(0).pull_sparse_table_id_size();
+    for (int i = 0; i < param_.program_config(0).pull_sparse_table_id_size();
          ++i) {
       uint64_t tid = static_cast<uint64_t>(
           param_.program_config(0).pull_sparse_table_id(i));
@@ -397,8 +397,8 @@ void DownpourWorker::TrainFiles() {
 
     if (need_to_push_sparse_) {
       // push gradients here
-      for (size_t i = 0;
-           i < param_.program_config(0).push_sparse_table_id_size(); ++i) {
+      for (int i = 0; i < param_.program_config(0).push_sparse_table_id_size();
+           ++i) {
         uint64_t tid = static_cast<uint64_t>(
             param_.program_config(0).push_sparse_table_id(i));
         TableParameter table;
@@ -416,8 +416,8 @@ void DownpourWorker::TrainFiles() {
     }
 
     if (need_to_push_dense_) {
-      for (size_t i = 0;
-           i < param_.program_config(0).push_dense_table_id_size(); ++i) {
+      for (int i = 0; i < param_.program_config(0).push_dense_table_id_size();
+           ++i) {
         uint64_t tid = static_cast<uint64_t>(
             param_.program_config(0).push_dense_table_id(i));
         fleet_ptr_->PushDenseVarsAsync(
@@ -461,8 +461,8 @@ void DownpourWorker::TrainFiles() {
     }
 
     if (need_to_push_dense_) {
-      for (size_t i = 0;
-           i < param_.program_config(0).push_dense_table_id_size(); ++i) {
+      for (int i = 0; i < param_.program_config(0).push_dense_table_id_size();
+           ++i) {
         uint64_t tid = static_cast<uint64_t>(
             param_.program_config(0).push_dense_table_id(i));
         pull_dense_worker_->IncreaseThreadVersion(thread_id_, tid);

paddle/fluid/framework/ir/expected_kernel_cache_pass.cc

@@ -23,7 +23,7 @@ namespace ir {
 void ExpectedKernelCachePass::ApplyImpl(ir::Graph* graph) const {
   VLOG(3) << "Applies Expected Kernel Cache strategy.";
   for (const Node* n : graph->Nodes()) {
-    if (n->IsOp()) {
+    if (n->IsOp() && n->Op()) {
       n->Op()->SetAttr(kEnableCacheExpectedKernel, true);
     }
   }

paddle/fluid/framework/ir/graph_helper.cc

@@ -31,10 +31,10 @@ namespace paddle {
 namespace framework {
 namespace ir {
 namespace {
-void SortHelper(
-    const std::map<ir::Node *, std::unordered_set<ir::Node *>> &adj_list,
-    ir::Node *node, std::unordered_set<ir::Node *> *visited,
-    std::vector<ir::Node *> *ret) {
+void SortHelper(const std::map<ir::Node *, std::set<ir::Node *, ir::NodeComp>,
+                               ir::NodeComp> &adj_list,
+                ir::Node *node, std::unordered_set<ir::Node *> *visited,
+                std::vector<ir::Node *> *ret) {
   visited->insert(node);
 
   for (auto adj : adj_list.at(node)) {
@@ -50,7 +50,8 @@ void SortHelper(
 
 bool HasCircleHelper(
     ir::Node *node,
-    const std::map<ir::Node *, std::unordered_set<ir::Node *>> &adj_list,
+    const std::map<ir::Node *, std::set<ir::Node *, ir::NodeComp>, ir::NodeComp>
+        &adj_list,
     std::unordered_set<ir::Node *> *visited,
     std::unordered_set<ir::Node *> *in_trace,
     std::vector<std::vector<ir::Node *>> *circles) {
@@ -84,7 +85,8 @@ bool HasCircleHelper(
 }
 
 bool HasCircleInternal(
-    const std::map<ir::Node *, std::unordered_set<ir::Node *>> &adj_list,
+    const std::map<ir::Node *, std::set<ir::Node *, ir::NodeComp>, ir::NodeComp>
+        &adj_list,
     std::vector<std::vector<ir::Node *>> *circles) {
   std::unordered_set<ir::Node *> visited;
   std::unordered_set<ir::Node *> in_trace;
@@ -107,8 +109,8 @@ bool FindCircleSubGraph(const Graph &graph,
 }
 
 std::vector<ir::Node *> TopologySortOperations(const Graph &graph) {
-  std::map<ir::Node *, std::unordered_set<ir::Node *>> adj_list =
-      BuildOperationAdjList(graph);
+  std::map<ir::Node *, std::set<ir::Node *, ir::NodeComp>, ir::NodeComp>
+      adj_list = BuildOperationAdjList(graph);
   PADDLE_ENFORCE(!HasCircleInternal(adj_list, nullptr));
   std::unordered_set<ir::Node *> visited;
   std::vector<ir::Node *> ret;
@@ -117,34 +119,30 @@ std::vector<ir::Node *> TopologySortOperations(const Graph &graph) {
       SortHelper(adj_list, adj.first, &visited, &ret);
     }
   }
+
   return ret;
 }
 
 // Build operator inlink edge table.
-std::map<ir::Node *, std::unordered_set<ir::Node *>> BuildOperationAdjList(
-    const Graph &graph) {
-  std::map<ir::Node *, std::unordered_set<ir::Node *>> adj_list;
+std::map<ir::Node *, std::set<ir::Node *, ir::NodeComp>, ir::NodeComp>
+BuildOperationAdjList(const Graph &graph) {
+  std::map<ir::Node *, std::set<ir::Node *, ir::NodeComp>, ir::NodeComp>
+      adj_list;
 
   for (auto &n : graph.Nodes()) {
     if (!n->IsOp()) continue;
     if (adj_list.find(n) == adj_list.end()) {
-      adj_list[n] = std::unordered_set<ir::Node *>();
+      adj_list[n] = std::set<ir::Node *, ir::NodeComp>();
     }
-    std::vector<ir::Node *> nodes;
     for (auto &var : n->inputs) {
       for (auto &adj_n : var->inputs) {
         PADDLE_ENFORCE(adj_n->NodeType() == ir::Node::Type::kOperation);
         VLOG(4) << "adj " << adj_n->Name() << reinterpret_cast<void *>(adj_n)
                 << " -> " << n->Name() << reinterpret_cast<void *>(n)
                 << "  via " << var->Name() << reinterpret_cast<void *>(var);
-        nodes.push_back(adj_n);
+        adj_list[n].insert(adj_n);
       }
     }
-    std::sort(nodes.begin(), nodes.end(), [](ir::Node *node1, ir::Node *node2) {
-      return node1->id() > node2->id();
-    });
-    adj_list[n].insert(std::make_move_iterator(nodes.begin()),
-                       std::make_move_iterator(nodes.end()));
   }
   return adj_list;
 }

paddle/fluid/framework/ir/graph_helper.h

@@ -16,6 +16,7 @@ limitations under the License. */
 
 #include <map>
 #include <memory>
+#include <set>
 #include <vector>
 
 #include "paddle/fluid/framework/ir/graph.h"
@@ -25,6 +26,13 @@ namespace paddle {
 namespace framework {
 namespace ir {
 
+// Compare nodes via node id.
+struct NodeComp {
+  bool operator()(ir::Node *const &node1, ir::Node *const &node2) const {
+    return node1->id() < node2->id();
+  }
+};
+
 // Test if the graph contains circle.
 bool HasCircle(const Graph &graph);
 
@@ -57,8 +65,8 @@ std::vector<Node *> TopologyVarientSort(const Graph &graph, SortKind sort_kind);
 void CleanIndividualNodes(Graph *graph);
 
 // Build an adjacency list of operations for the `graph`.
-std::map<ir::Node *, std::unordered_set<ir::Node *>> BuildOperationAdjList(
-    const Graph &graph);
+std::map<ir::Node *, std::set<ir::Node *, ir::NodeComp>, ir::NodeComp>
+BuildOperationAdjList(const Graph &graph);
 
 template <typename T>
 std::vector<T *> FilterByNodeWrapper(const Graph &graph) {

paddle/fluid/framework/op_desc.cc

@@ -241,6 +241,7 @@ OpDesc::OpDesc(const std::string &type, const VariableNameMap &inputs,
   outputs_ = outputs;
   attrs_ = attrs;
   need_update_ = true;
+  block_ = nullptr;
 }
 
 OpDesc::OpDesc(const OpDesc &other, BlockDesc *block) {

paddle/fluid/framework/operator.cc

@@ -880,7 +880,16 @@ std::vector<KernelConfig>* OperatorWithKernel::GetKernelConfig(
 
 void OperatorWithKernel::RunImpl(const Scope& scope,
                                  const platform::Place& place) const {
-  if (!HasAttr(kEnableCacheRuntimeContext)) {
+  // To reduce the elapsed time of HasAttr, we use bool variable to record the
+  // result of HasAttr.
+  if (!enable_cache_runtime_context && HasAttr(kEnableCacheRuntimeContext))
+    enable_cache_runtime_context = true;
+  if (!enable_cache_expected_kernel && HasAttr(kEnableCacheExpectedKernel))
+    enable_cache_expected_kernel = true;
+  if (!all_kernels_must_compute_runtime_shape &&
+      HasAttr(kAllKernelsMustComputeRuntimeShape))
+    all_kernels_must_compute_runtime_shape = true;
+  if (!enable_cache_runtime_context) {
     RuntimeContext ctx(Inputs(), Outputs(), scope);
     RunImpl(scope, place, &ctx);
   } else {
@@ -899,7 +908,7 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
   platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
   auto* dev_ctx = pool.Get(place);
 
-  if (!HasAttr(kEnableCacheExpectedKernel) || !kernel_type_) {
+  if (!enable_cache_expected_kernel || !kernel_type_) {
     ChooseKernel(*runtime_ctx, scope, place);
   }
 
@@ -918,7 +927,7 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
     dev_ctx = pool.Get(kernel_type_->place_);
   }
 
-  if (!HasAttr(kAllKernelsMustComputeRuntimeShape)) {
+  if (!all_kernels_must_compute_runtime_shape) {
     RuntimeInferShapeContext infer_shape_ctx(*this, exec_scope, *runtime_ctx);
     this->InferShape(&infer_shape_ctx);
   }

paddle/fluid/framework/operator.h

@@ -506,6 +506,9 @@ class OperatorWithKernel : public OperatorBase {
   mutable std::unique_ptr<OpKernelFunc> kernel_func_;
   mutable std::unique_ptr<RuntimeContext> runtime_ctx_;
   mutable const Scope* pre_scope_ = nullptr;
+  mutable bool enable_cache_runtime_context = false;
+  mutable bool enable_cache_expected_kernel = false;
+  mutable bool all_kernels_must_compute_runtime_shape = false;
 };
 
 extern bool OpSupportGPU(const std::string& op_type);

paddle/fluid/framework/parallel_executor.cc

@@ -221,7 +221,7 @@ ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
     PADDLE_ENFORCE(!member_->use_cuda_,
                    "gpu mode does not support async_mode_ now!");
     graphs.push_back(graph);
-    for (int i = 1; i < places.size(); ++i) {
+    for (size_t i = 1; i < places.size(); ++i) {
       auto *tmp_graph = new ir::Graph(graph->OriginProgram());
       async_graphs_.emplace_back(tmp_graph);
       graphs.push_back(tmp_graph);
@@ -315,7 +315,7 @@ ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
     graph = build_strategy.Apply(graph, {member_->places_[0]}, loss_var_name,
                                  {member_->local_scopes_[0]}, 1,
                                  member_->use_cuda_, member_->nccl_ctxs_.get());
-    for (int i = 1; i < member_->places_.size(); ++i) {
+    for (size_t i = 1; i < member_->places_.size(); ++i) {
       graphs[i] =
           build_strategy.Apply(graphs[i], {member_->places_[i]}, loss_var_name,
                                {member_->local_scopes_[i]}, 1,

paddle/fluid/framework/trainer_desc.proto

@@ -76,7 +76,7 @@ message PullDenseWorkerParameter {
 
 message TableParameter {
   // dense table only
-  optional int64 table_id = 1;
+  optional uint64 table_id = 1;
   repeated string dense_value_name = 2;
   repeated string dense_grad_name = 3;
   repeated int32 push_dense_wait_times = 5;

paddle/fluid/inference/api/analysis_predictor.cc

@@ -259,6 +259,9 @@ bool AnalysisPredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
       return false;
     }
 
+    PADDLE_ENFORCE_NOT_NULL(input_ptr);
+    PADDLE_ENFORCE_NOT_NULL(inputs[i].data.data());
+
     if (platform::is_cpu_place(place_)) {
       // TODO(panyx0718): Init LoDTensor from existing memcpy to save a copy.
       std::memcpy(static_cast<void *>(input_ptr), inputs[i].data.data(),

paddle/fluid/inference/api/api.cc

@@ -54,6 +54,7 @@ PaddleBuf &PaddleBuf::operator=(const PaddleBuf &other) {
     memory_owned_ = other.memory_owned_;
   } else {
     Resize(other.length());
+    PADDLE_ENFORCE(!(other.length() > 0 && other.data() == nullptr));
     memcpy(data_, other.data(), other.length());
     length_ = other.length();
     memory_owned_ = true;

paddle/fluid/inference/api/api_impl.cc

@@ -169,6 +169,7 @@ std::unique_ptr<PaddlePredictor> NativePaddlePredictor::Clone() {
   std::unique_ptr<PaddlePredictor> cls(new NativePaddlePredictor(config_));
   // Hot fix the bug that result diff in multi-thread.
   // TODO(Superjomn) re-implement a real clone here.
+  PADDLE_ENFORCE_NOT_NULL(dynamic_cast<NativePaddlePredictor *>(cls.get()));
   if (!dynamic_cast<NativePaddlePredictor *>(cls.get())->Init(nullptr)) {
     LOG(ERROR) << "fail to call Init";
     return nullptr;
@@ -210,6 +211,8 @@ bool NativePaddlePredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
       return false;
     }
 
+    PADDLE_ENFORCE_NOT_NULL(input_ptr);
+    PADDLE_ENFORCE_NOT_NULL(inputs[i].data.data());
     if (platform::is_cpu_place(place_)) {
       // TODO(panyx0718): Init LoDTensor from existing memcpy to save a copy.
       std::memcpy(static_cast<void *>(input_ptr), inputs[i].data.data(),
@@ -316,6 +319,8 @@ std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
   }
 
   std::unique_ptr<PaddlePredictor> predictor(new NativePaddlePredictor(config));
+  PADDLE_ENFORCE_NOT_NULL(
+      dynamic_cast<NativePaddlePredictor *>(predictor.get()));
   if (!dynamic_cast<NativePaddlePredictor *>(predictor.get())->Init(nullptr)) {
     return nullptr;
   }

paddle/fluid/inference/api/paddle_pass_builder.cc

@@ -123,8 +123,8 @@ CpuPassStrategy::CpuPassStrategy() : PassStrategy({}) {
       // will enhance this pass later.
       "runtime_context_cache_pass",     //
       "attention_lstm_fuse_pass",       //
-      "seqpool_concat_fuse_pass",       //
       "seqconv_eltadd_relu_fuse_pass",  //
+      // "seqpool_concat_fuse_pass",    //
       // "embedding_fc_lstm_fuse_pass", //
       "fc_lstm_fuse_pass",             //
       "mul_lstm_fuse_pass",            //

paddle/fluid/inference/tests/api/analyzer_seq_conv1_tester.cc

@@ -47,6 +47,7 @@ struct DataRecord {
       num_lines++;
       std::vector<std::string> data;
       split(line, '\t', &data);
+      PADDLE_ENFORCE(data.size() >= 4);
       // load title1 data
       std::vector<int64_t> title1_data;
       split_to_int64(data[0], ' ', &title1_data);

paddle/fluid/inference/tests/api/analyzer_seq_pool1_tester.cc

@@ -150,6 +150,9 @@ void SetConfig(AnalysisConfig *cfg, bool use_mkldnn = false) {
   if (use_mkldnn) {
     cfg->EnableMKLDNN();
   }
+  // Enable seqpool_concat_fuse_pass, disabled by default since it takes much
+  // time
+  cfg->pass_builder()->InsertPass(2, "seqpool_concat_fuse_pass");
 }
 
 void profile(bool use_mkldnn = false) {

paddle/fluid/inference/tests/api/analyzer_transformer_tester.cc

@@ -214,28 +214,23 @@ TEST(Analyzer_Transformer, fuse_statis) {
 }
 
 // Compare result of NativeConfig and AnalysisConfig
-// void compare(bool use_mkldnn = false) {
-//   AnalysisConfig cfg;
-//   SetConfig(&cfg);
-//   if (use_mkldnn) {
-//     cfg.EnableMKLDNN();
-//   }
-//
-//   std::vector<std::vector<PaddleTensor>> input_slots_all;
-//   SetInput(&input_slots_all);
-//   CompareNativeAndAnalysis(
-//       reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
-//       input_slots_all);
-// }
-
-// TODO(yihuaxu):
-//    Disable compare and compare_mkldnn temporary, see
-//    https://github.com/paddlePaddle/Paddle/issues/16316 for details.
-// TEST(Analyzer_Transformer, compare) { compare(); }
-// #ifdef PADDLE_WITH_MKLDNN
-// TEST(Analyzer_Transformer, compare_mkldnn) { compare(true /* use_mkldnn */);
-// }
-// #endif
+void compare(bool use_mkldnn = false) {
+  AnalysisConfig cfg;
+  SetConfig(&cfg);
+  if (use_mkldnn) {
+    cfg.EnableMKLDNN();
+  }
+
+  std::vector<std::vector<PaddleTensor>> input_slots_all;
+  SetInput(&input_slots_all);
+  CompareNativeAndAnalysis(
+      reinterpret_cast<const PaddlePredictor::Config *>(&cfg), input_slots_all);
+}
+
+TEST(Analyzer_Transformer, compare) { compare(); }
+#ifdef PADDLE_WITH_MKLDNN
+TEST(Analyzer_Transformer, compare_mkldnn) { compare(true /* use_mkldnn */); }
+#endif
 
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/op_use_default_grad_op_maker.spec

-abs
-acos
-asin
-atan
 attention_lstm
-brelu
 conv_shift
-cos
 cos_sim
 dequantize
-elu
 fc
 flatten
 fsp
@@ -21,14 +14,8 @@ fusion_seqconv_eltadd_relu
 fusion_seqexpand_concat_fc
 fusion_seqpool_concat
 fusion_squared_mat_sub
-gelu
 gru
-hard_shrink
 hierarchical_sigmoid
-leaky_relu
-log
-logsigmoid
-lookup_table
 lrn
 lstm_unit
 lstmp
@@ -39,10 +26,11 @@ modified_huber_loss
 nce
 pool2d
 pool3d
-pow
 prelu
 quantize
 rank_loss
+reduce_all
+reduce_any
 reduce_max
 reduce_mean
 reduce_min
@@ -51,26 +39,10 @@ reduce_sum
 requantize
 reshape
 rnn_memory_helper
-round
 sequence_softmax
-sin
-softplus
-softshrink
-softsign
-space_to_depth
 spp
-square
-squared_l2_distance
-squared_l2_norm
 squeeze
-stanh
-swish
-tanh_shrink
-teacher_student_sigmoid_loss
 tensor_array_to_tensor
-thresholded_relu
 transpose
-tree_conv
 unpool
 unsqueeze
-warpctc

paddle/fluid/operators/activation_cudnn_op.cu.cc

@@ -12,6 +12,9 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+#include <memory>
+#include <string>
+#include <vector>
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/operators/activation_op.h"
 #include "paddle/fluid/platform/cudnn_desc.h"
@@ -82,6 +85,8 @@ template <typename T>
 struct CudnnReluGradFunctor : public CudnnActivationGradFunctor<T> {
   explicit CudnnReluGradFunctor(const CUDADeviceContext& ctx)
       : CudnnActivationGradFunctor<T>(ctx, 0.0, CUDNN_ACTIVATION_RELU) {}
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepOut; }
 };
 
 template <typename T>
@@ -94,6 +99,8 @@ struct CudnnRelu6GradFunctor : public CudnnActivationGradFunctor<T> {
   explicit CudnnRelu6GradFunctor(const CUDADeviceContext& ctx)
       : CudnnActivationGradFunctor<T>(ctx, 6.0, CUDNN_ACTIVATION_CLIPPED_RELU) {
   }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepOut; }
 };
 
 template <typename T>
@@ -105,6 +112,8 @@ template <typename T>
 struct CudnnSigmoidGradFunctor : public CudnnActivationGradFunctor<T> {
   explicit CudnnSigmoidGradFunctor(const CUDADeviceContext& ctx)
       : CudnnActivationGradFunctor<T>(ctx, 0.0, CUDNN_ACTIVATION_SIGMOID) {}
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepOut; }
 };
 
 template <typename T>
@@ -116,6 +125,8 @@ template <typename T>
 struct CudnnTanhGradFunctor : public CudnnActivationGradFunctor<T> {
   explicit CudnnTanhGradFunctor(const CUDADeviceContext& ctx)
       : CudnnActivationGradFunctor<T>(ctx, 0.0, CUDNN_ACTIVATION_TANH) {}
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepOut; }
 };
 
 template <typename Functor>
@@ -140,10 +151,13 @@ class CudnnActivationGradKernel
  public:
   using T = typename Functor::ELEMENT_TYPE;
   void Compute(const framework::ExecutionContext& context) const override {
+    static_assert(Functor::FwdDeps() == kDepOut, "Forward deps must be Out.");
+
     const framework::Tensor *X, *Out, *dOut;
     X = Out = dOut = nullptr;
     framework::Tensor* dX = nullptr;
-    ExtractActivationGradTensor(context, &X, &Out, &dOut, &dX);
+    ExtractActivationGradTensor<Functor::FwdDeps()>(context, &X, &Out, &dOut,
+                                                    &dX);
     dX->mutable_data<T>(context.GetPlace());
     auto& dev_ctx = context.template device_context<CUDADeviceContext>();
     Functor functor(dev_ctx);

paddle/fluid/operators/activation_op.cc

@@ -15,7 +15,9 @@ limitations under the License. */
 #include "paddle/fluid/operators/activation_op.h"
 #include <memory>
 #include <string>
+#include <type_traits>
 #include <unordered_map>
+#include <vector>
 #include "paddle/fluid/operators/mkldnn/mkldnn_activation_op.h"
 #include "paddle/fluid/platform/port.h"
 #ifdef PADDLE_WITH_CUDA
@@ -27,6 +29,25 @@ namespace operators {
 
 using paddle::framework::Tensor;
 
+template <typename GradFunctor>
+static constexpr bool CanInplaceAct() {
+  return GradFunctor::FwdDeps() == kDepOut || GradFunctor::FwdDeps() == kNoDeps;
+}
+
+std::unique_ptr<std::unordered_set<std::string>> GetInplaceOpSet() {
+  std::unique_ptr<std::unordered_set<std::string>> ret(
+      new std::unordered_set<std::string>());
+#define INSERT_INTO_INPLACE_OP_SET(op_type, __omitted, fwd_functor, \
+                                   bwd_functor)                     \
+  if (CanInplaceAct<bwd_functor<float>>()) {                        \
+    ret->insert(#op_type);                                          \
+  }
+
+  FOR_EACH_ACTIVATION_OP(INSERT_INTO_INPLACE_OP_SET);
+#undef INSERT_INTO_INPLACE_OP_SET
+  return ret;
+}
+
 #define REGISTER_ACTIVATION_OP_MAKER(OP_NAME, OP_COMMENT)                    \
   class OP_NAME##OpMaker                                                     \
       : public ::paddle::framework::OpProtoAndCheckerMaker {                 \
@@ -50,26 +71,32 @@ using paddle::framework::Tensor;
     }                                                                        \
   }
 
-#define REGISTER_ACTIVATION_OP_GRAD_MAKER(OP_NAME, KERNEL_TYPE)              \
-  class OP_NAME##GradMaker                                                   \
-      : public ::paddle::framework::SingleGradOpDescMaker {                  \
-   public:                                                                   \
-    using ::paddle::framework::SingleGradOpDescMaker::SingleGradOpDescMaker; \
-                                                                             \
-   protected:                                                                \
-    std::unique_ptr<::paddle::framework::OpDesc> Apply() const override {    \
-      auto* op = new ::paddle::framework::OpDesc();                          \
-      op->SetType(#KERNEL_TYPE "_grad");                                     \
-      op->SetInput("Out", Output("Out"));                                    \
-      op->SetInput(::paddle::framework::GradVarName("Out"),                  \
-                   OutputGrad("Out"));                                       \
-                                                                             \
-      op->SetAttrMap(Attrs());                                               \
-                                                                             \
-      op->SetOutput(::paddle::framework::GradVarName("X"), InputGrad("X"));  \
-      return std::unique_ptr<::paddle::framework::OpDesc>(op);               \
-    }                                                                        \
+template <ActBwdOpFwdDeps kDepValue>
+class ActivationGradOpDescMaker : 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(ForwardOpType() + "_grad");
+    op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+    op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+    op->SetAttrMap(Attrs());
+
+    if (static_cast<int>(kDepValue) &
+        static_cast<int>(ActBwdOpFwdDeps::kDepX)) {
+      op->SetInput("X", Input("X"));
+    }
+
+    if (static_cast<int>(kDepValue) &
+        static_cast<int>(ActBwdOpFwdDeps::kDepOut)) {
+      op->SetInput("Out", Output("Out"));
+    }
+
+    return op;
   }
+};
 
 framework::OpKernelType GetKernelType(const framework::ExecutionContext& ctx,
                                       const framework::OperatorWithKernel& oper,
@@ -129,14 +156,15 @@ class ActivationOpGrad : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    ctx->ShareDim("Out", framework::GradVarName("X"));
-    ctx->ShareLoD("Out", framework::GradVarName("X"));
+    auto out_grad_name = framework::GradVarName("Out");
+    ctx->ShareDim(out_grad_name, framework::GradVarName("X"));
+    ctx->ShareLoD(out_grad_name, framework::GradVarName("X"));
   }
 
  protected:
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return GetKernelType(ctx, *this, "Out");
+    return GetKernelType(ctx, *this, framework::GradVarName("Out"));
   }
 };
 
@@ -558,79 +586,27 @@ REGISTER_ACTIVATION_OP_MAKER(Log, LogDoc);
 REGISTER_ACTIVATION_OP_MAKER(Square, SquareDoc);
 REGISTER_ACTIVATION_OP_MAKER(Softplus, SoftplusDoc);
 REGISTER_ACTIVATION_OP_MAKER(Softsign, SoftsignDoc);
-
-REGISTER_ACTIVATION_OP_GRAD_MAKER(Sigmoid, sigmoid);
-REGISTER_ACTIVATION_OP_GRAD_MAKER(Relu, relu);
-REGISTER_ACTIVATION_OP_GRAD_MAKER(Gelu, gelu);
-REGISTER_ACTIVATION_OP_GRAD_MAKER(Exp, exp);
-REGISTER_ACTIVATION_OP_GRAD_MAKER(Tanh, tanh);
-REGISTER_ACTIVATION_OP_GRAD_MAKER(Ceil, ceil);
-REGISTER_ACTIVATION_OP_GRAD_MAKER(Floor, floor);
-REGISTER_ACTIVATION_OP_GRAD_MAKER(Sqrt, sqrt);
-REGISTER_ACTIVATION_OP_GRAD_MAKER(SoftRelu, soft_relu);
-REGISTER_ACTIVATION_OP_GRAD_MAKER(Relu6, relu6);
-REGISTER_ACTIVATION_OP_GRAD_MAKER(Reciprocal, reciprocal);
-REGISTER_ACTIVATION_OP_GRAD_MAKER(HardSigmoid, hard_sigmoid);
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
 
-#define FOR_EACH_INPLACE_OP_FUNCTOR(__macro) \
-  __macro(Sigmoid, sigmoid);                 \
-  __macro(Relu, relu);                       \
-  __macro(Exp, exp);                         \
-  __macro(Tanh, tanh);                       \
-  __macro(Ceil, ceil);                       \
-  __macro(Floor, floor);                     \
-  __macro(Sqrt, sqrt);                       \
-  __macro(SoftRelu, soft_relu);              \
-  __macro(Relu6, relu6);                     \
-  __macro(Reciprocal, reciprocal);           \
-  __macro(HardSigmoid, hard_sigmoid);
-
-#define FOR_EACH_OP_FUNCTOR(__macro) \
-  __macro(LogSigmoid, logsigmoid);   \
-  __macro(SoftShrink, softshrink);   \
-  __macro(Abs, abs);                 \
-  __macro(Cos, cos);                 \
-  __macro(Acos, acos);               \
-  __macro(Sin, sin);                 \
-  __macro(Asin, asin);               \
-  __macro(Atan, atan);               \
-  __macro(Round, round);             \
-  __macro(Log, log);                 \
-  __macro(Square, square);           \
-  __macro(Gelu, gelu);               \
-  __macro(BRelu, brelu);             \
-  __macro(Pow, pow);                 \
-  __macro(STanh, stanh);             \
-  __macro(Softplus, softplus);       \
-  __macro(Softsign, softsign);       \
-  __macro(LeakyRelu, leaky_relu);    \
-  __macro(TanhShrink, tanh_shrink);  \
-  __macro(ELU, elu);                 \
-  __macro(HardShrink, hard_shrink);  \
-  __macro(Swish, swish);             \
-  __macro(ThresholdedRelu, thresholded_relu);
-
-#define REGISTER_INPLACE_ACTIVATION_OP(OP_NAME, KERNEL_TYPE)                   \
-  REGISTER_OPERATOR(KERNEL_TYPE, ::paddle::operators::ActivationOp,            \
-                    ::paddle::operators::OP_NAME##OpMaker,                     \
-                    ::paddle::operators::ActivationOpInferVarType,             \
-                    ::paddle::operators::OP_NAME##GradMaker,                   \
-                    ::paddle::framework::SingleOpInplaceInToOut);              \
-  REGISTER_OPERATOR(KERNEL_TYPE##_grad, ::paddle::operators::ActivationOpGrad, \
-                    ::paddle::framework::SingleOpInplaceInToOut)
-
-#define REGISTER_ACTIVATION_OP(OP_NAME, KERNEL_TYPE)                    \
-  REGISTER_OPERATOR(KERNEL_TYPE, ::paddle::operators::ActivationOp,     \
-                    ::paddle::operators::OP_NAME##OpMaker,              \
-                    ::paddle::operators::ActivationOpInferVarType,      \
-                    ::paddle::framework::DefaultGradOpDescMaker<true>); \
-  REGISTER_OPERATOR(KERNEL_TYPE##_grad, ::paddle::operators::ActivationOpGrad)
-
-#define REGISTER_ACTIVATION_CPU_KERNEL(act_type, functor, grad_functor)   \
+#define REGISTER_ACTIVATION_OP(KERNEL_TYPE, OP_NAME, functor, grad_functor) \
+  REGISTER_OPERATOR(                                                        \
+      KERNEL_TYPE, ops::ActivationOp, ops::OP_NAME##OpMaker,                \
+      ops::ActivationOpInferVarType,                                        \
+      ops::ActivationGradOpDescMaker<ops::grad_functor<float>::FwdDeps()>,  \
+      std::conditional<ops::CanInplaceAct<ops::grad_functor<float>>(),      \
+                       ::paddle::framework::SingleOpInplaceInToOut,         \
+                       void>::type);                                        \
+  REGISTER_OPERATOR(                                                        \
+      KERNEL_TYPE##_grad, ops::ActivationOpGrad,                            \
+      std::conditional<ops::CanInplaceAct<ops::grad_functor<float>>(),      \
+                       ::paddle::framework::SingleOpInplaceInToOut,         \
+                       void>::type)
+
+#define REGISTER_ACTIVATION_CPU_KERNEL(act_type, op_name, functor,        \
+                                       grad_functor)                      \
   REGISTER_OP_CPU_KERNEL(                                                 \
       act_type, ops::ActivationKernel<paddle::platform::CPUDeviceContext, \
                                       ops::functor<float>>,               \
@@ -643,6 +619,5 @@ namespace ops = paddle::operators;
       ops::ActivationGradKernel<paddle::platform::CPUDeviceContext,       \
                                 ops::grad_functor<double>>);
 
-FOR_EACH_OP_FUNCTOR(REGISTER_ACTIVATION_OP);
-FOR_EACH_INPLACE_OP_FUNCTOR(REGISTER_INPLACE_ACTIVATION_OP);
-FOR_EACH_KERNEL_FUNCTOR(REGISTER_ACTIVATION_CPU_KERNEL);
+FOR_EACH_ACTIVATION_OP(REGISTER_ACTIVATION_OP);
+FOR_EACH_ACTIVATION_OP(REGISTER_ACTIVATION_CPU_KERNEL);

paddle/fluid/operators/activation_op.cu

@@ -15,7 +15,8 @@ limitations under the License. */
 namespace ops = paddle::operators;
 namespace plat = paddle::platform;
 
-#define REGISTER_ACTIVATION_CUDA_KERNEL(act_type, functor, grad_functor)    \
+#define REGISTER_ACTIVATION_CUDA_KERNEL(act_type, op_name, functor,         \
+                                        grad_functor)                       \
   REGISTER_OP_CUDA_KERNEL(                                                  \
       act_type,                                                             \
       ops::ActivationKernel<plat::CUDADeviceContext, ops::functor<float>>,  \
@@ -30,4 +31,4 @@ namespace plat = paddle::platform;
       ops::ActivationGradKernel<plat::CUDADeviceContext,                    \
                                 ops::grad_functor<plat::float16>>);
 
-FOR_EACH_KERNEL_FUNCTOR(REGISTER_ACTIVATION_CUDA_KERNEL);
+FOR_EACH_ACTIVATION_OP(REGISTER_ACTIVATION_CUDA_KERNEL);

paddle/fluid/operators/activation_op.h

@@ -12,6 +12,7 @@ limitations under the License. */
 #pragma once
 #include <glog/logging.h>
 #include <algorithm>
+#include <memory>
 #include <string>
 #include <unordered_set>
 #include <utility>
@@ -35,21 +36,29 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
-/* Use ugly global variable, for the using in python layer side
-   Please refer to the layer_helper.py and get the details.
- */
-static std::unordered_set<std::string> InplaceOpSet = {
-    "sigmoid", "exp",        "relu",  "tanh",      "sqrt",        "ceil",
-    "floor",   "reciprocal", "relu6", "soft_relu", "hard_sigmoid"};
+enum ActBwdOpFwdDeps {
+  kNoDeps = 0x00,  // Do not need any forward input/output
+  kDepX = 0x01,    // Only need forward input X
+  kDepOut = 0x02,  // Only need forward output Out
+
+  // Never add kDepXOut, because Out can be always calculated
+  // by forward input X in backward part.
+  // FIXME(zjl): but in MKLDNN abs, X and Out are all needed...
+  // Developers should not rely on this enum value!
+  kDepXOut = 0x03
+};
+
+std::unique_ptr<std::unordered_set<std::string>> GetInplaceOpSet();
 
 static bool IsInplace(const std::string& op) {
-  bool inplace = InplaceOpSet.count(op);
+  static auto InplaceOpSet = GetInplaceOpSet();
+  bool inplace = InplaceOpSet->count(op);
   // for op_grad
   const int kGradSuffixLen = 4;
   if (op.size() > kGradSuffixLen &&
       op.compare(op.size() - kGradSuffixLen - 1, kGradSuffixLen, "grad")) {
     inplace =
-        InplaceOpSet.count(op.substr(0, op.size() - (kGradSuffixLen + 1)));
+        InplaceOpSet->count(op.substr(0, op.size() - (kGradSuffixLen + 1)));
   }
   return inplace;
 }
@@ -85,16 +94,21 @@ inline void ExtractActivationTensor(const framework::ExecutionContext& context,
                  context.op().Output("Out"));
 }
 
+template <ActBwdOpFwdDeps kDepValue>
 inline void ExtractActivationGradTensor(
     const framework::ExecutionContext& context, const framework::Tensor** X,
     const framework::Tensor** Out, const framework::Tensor** dOut,
     framework::Tensor** dX) {
-  auto out_var = context.InputVar("Out");
   auto out_grad_var = context.InputVar(framework::GradVarName("Out"));
   auto x_grad_var = context.OutputVar(framework::GradVarName("X"));
-  PADDLE_ENFORCE(out_var != nullptr,
-                 "Cannot get input Variable Out, variable name = %s",
-                 context.op().Input("Out"));
+  const framework::Variable* out_var = nullptr;
+
+  if (static_cast<int>(kDepValue) & static_cast<int>(kDepOut)) {
+    out_var = context.InputVar("Out");
+    PADDLE_ENFORCE(out_var != nullptr,
+                   "Cannot get input Variable Out, variable name = %s",
+                   context.op().Input("Out"));
+  }
   PADDLE_ENFORCE(out_grad_var != nullptr,
                  "Cannot get input Variable %s, variable name = %s",
                  framework::GradVarName("Out"),
@@ -105,23 +119,36 @@ inline void ExtractActivationGradTensor(
                  context.op().Output(framework::GradVarName("X")));
 
   if (CanBeUsedBySelectedRows.count(context.op().Type())) {
-    *Out = paddle::framework::GetLoDTensorOrSelectedRowsValueFromVar(*out_var);
     *dOut = paddle::framework::GetLoDTensorOrSelectedRowsValueFromVar(
         *out_grad_var);
     *dX = paddle::framework::GetMutableLoDTensorOrSelectedRowsValueFromVar(
         x_grad_var);
+
+    if (out_var) {
+      *Out =
+          paddle::framework::GetLoDTensorOrSelectedRowsValueFromVar(*out_var);
+    } else {
+      *Out = *dOut;  // fake out
+    }
+
   } else {
     *Out = context.Input<framework::Tensor>("Out");
     *dOut = context.Input<framework::Tensor>(framework::GradVarName("Out"));
     *dX = context.Output<framework::Tensor>(framework::GradVarName("X"));
+
+    if (out_var) {
+      *Out = &(out_var->Get<framework::LoDTensor>());
+    } else {
+      *Out = *dOut;  // fake out
+    }
   }
+
   PADDLE_ENFORCE(*dX != nullptr,
                  "Cannot get output tensor %s, variable name = %s",
                  framework::GradVarName("X"),
                  context.op().Output(framework::GradVarName("X")));
 
-  bool inplace = IsInplace(context.op().Type());
-  if (!inplace) {
+  if (static_cast<int>(kDepValue) & static_cast<int>(kDepX)) {
     auto x_var = context.InputVar("X");
     PADDLE_ENFORCE(x_var != nullptr,
                    "Cannot get input tensor X, variable name = %s",
@@ -172,7 +199,8 @@ class ActivationGradKernel
     const framework::Tensor *X, *Out, *dOut;
     framework::Tensor* dX = nullptr;
     X = Out = dOut = nullptr;
-    ExtractActivationGradTensor(context, &X, &Out, &dOut, &dX);
+    ExtractActivationGradTensor<Functor::FwdDeps()>(context, &X, &Out, &dOut,
+                                                    &dX);
     dX->mutable_data<T>(context.GetPlace());
     auto dout = framework::EigenVector<T>::Flatten(detail::Ref(dOut));
     auto out = framework::EigenVector<T>::Flatten(detail::Ref(Out));
@@ -222,6 +250,8 @@ struct SigmoidGradFunctor : public BaseActivationFunctor<T> {
   void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
     dx.device(d) = dout * out * (static_cast<T>(1) - out);
   }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepOut; }
 };
 
 // Originally: logsigmoid(x) = -log (1 + exp(-x))
@@ -258,6 +288,8 @@ struct LogSigmoidGradFunctor : public BaseActivationFunctor<T> {
     dx.device(d) =
         dout * ((-x - temp).exp() / ((-temp).exp() + (-x - temp).exp()));
   }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
 };
 
 // exp(x) = e^x
@@ -276,6 +308,8 @@ struct ExpGradFunctor : public BaseActivationFunctor<T> {
   void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
     dx.device(d) = dout * out;
   }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepOut; }
 };
 
 // relu(x) = max(x, 0)
@@ -294,6 +328,8 @@ struct ReluGradFunctor : public BaseActivationFunctor<T> {
   void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
     dx.device(d) = dout * (out > static_cast<T>(0)).template cast<T>();
   }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepOut; }
 };
 
 // gelu(x) = 0.5 * x *  (1 + erf(x / sqrt(2)))
@@ -338,6 +374,8 @@ struct GeluGradFunctor : BaseActivationFunctor<T> {
                   (-static_cast<T>(0.5) * x.square()).exp();
     dx.device(d) = dout * (first + second);
   }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
 };
 
 // tanh(x) = (exp(x) - exp(-x)) / (exp(x) + exp(-x))
@@ -356,6 +394,8 @@ struct TanhGradFunctor : public BaseActivationFunctor<T> {
   void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
     dx.device(d) = dout * (static_cast<T>(1) - out * out);
   }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepOut; }
 };
 
 // tanhshrink(x) = x - tanh(x)
@@ -375,6 +415,8 @@ struct TanhShrinkGradFunctor : public BaseActivationFunctor<T> {
   void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
     dx.device(d) = dout * (x.tanh() * x.tanh());
   }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
 };
 
 // tanhshrink(x) = x - tanh(x)
@@ -409,6 +451,8 @@ struct HardShrinkGradFunctor : public BaseActivationFunctor<T> {
     auto temp2 = (x > static_cast<T>(threshold)).template cast<T>().eval();
     dx.device(d) = dout * (temp1 + temp2).template cast<T>();
   }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
 };
 
 // softshrink(x) = x - lambda, if x > lambda; x + lambda, if x < -lambda; 0
@@ -443,6 +487,8 @@ struct SoftShrinkGradFunctor : public BaseActivationFunctor<T> {
     auto temp2 = (x < -lambdaT).template cast<T>().eval();
     dx.device(d) = dout * (temp1 + temp2).template cast<T>();
   }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
 };
 
 // sqrt(x) = x^(1/2)
@@ -461,6 +507,8 @@ struct SqrtGradFunctor : public BaseActivationFunctor<T> {
   void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
     dx.device(d) = static_cast<T>(0.5) * dout / out;
   }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepOut; }
 };
 
 // ceil(x) = ceiling(x)
@@ -479,6 +527,8 @@ struct ZeroGradFunctor : public BaseActivationFunctor<T> {
   void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
     dx.device(d) = static_cast<T>(0) / out;
   }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kNoDeps; }
 };
 
 // floor(x) = flooring(x)
@@ -522,6 +572,8 @@ struct CosGradFunctor : public BaseActivationFunctor<T> {
   void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
     dx.device(d) = -dout * x.unaryExpr(Sine<T>());
   }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
 };
 
 // cosine(x) = cos(x)
@@ -541,6 +593,8 @@ struct SinGradFunctor : public BaseActivationFunctor<T> {
   void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
     dx.device(d) = dout * x.unaryExpr(Cosine<T>());
   }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
 };
 
 // sine(x) = sin(x)
@@ -582,6 +636,8 @@ struct AcosGradFunctor : public BaseActivationFunctor<T> {
     dx.device(d) =
         -dout * static_cast<T>(1) / (static_cast<T>(1) - x.square()).sqrt();
   }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
 };
 
 template <typename T>
@@ -614,6 +670,8 @@ struct AsinGradFunctor : public BaseActivationFunctor<T> {
     dx.device(d) =
         dout * static_cast<T>(1) / (static_cast<T>(1) - x.square()).sqrt();
   }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
 };
 
 template <typename T>
@@ -645,6 +703,8 @@ struct AtanGradFunctor : public BaseActivationFunctor<T> {
   void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
     dx.device(d) = dout * static_cast<T>(1) / (static_cast<T>(1) + x.square());
   }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
 };
 
 // round(x) = [x]
@@ -672,6 +732,8 @@ struct AbsGradFunctor : public BaseActivationFunctor<T> {
   void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
     dx.device(d) = dout * x.sign();
   }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepXOut; }
 };
 
 // reciprocal(x) = 1 / x
@@ -690,6 +752,8 @@ struct ReciprocalGradFunctor : public BaseActivationFunctor<T> {
   void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
     dx.device(d) = dout * static_cast<T>(-1) * out * out;
   }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepOut; }
 };
 
 // log(x) = natural logarithm of x
@@ -708,6 +772,8 @@ struct LogGradFunctor : public BaseActivationFunctor<T> {
   void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
     dx.device(d) = dout * (static_cast<T>(1) / x);
   }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
 };
 
 // square(x) = x^2
@@ -726,6 +792,8 @@ struct SquareGradFunctor : public BaseActivationFunctor<T> {
   void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
     dx.device(d) = dout * static_cast<T>(2) * x;
   }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
 };
 
 template <typename T>
@@ -760,6 +828,8 @@ struct BReluGradFunctor : public BaseActivationFunctor<T> {
                    ((x > static_cast<T>(t_min)) * (x < static_cast<T>(t_max)))
                        .template cast<T>();
   }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
 };
 
 // relu6(x) = min(max(0, x), 6)
@@ -792,6 +862,8 @@ struct Relu6GradFunctor : public BaseActivationFunctor<T> {
         ((out > static_cast<T>(0)) * (out < static_cast<T>(threshold)))
             .template cast<T>();
   }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepOut; }
 };
 
 // softplus(x) = log(1 + exp(x))
@@ -821,6 +893,8 @@ struct SoftplusGradFunctor : public BaseActivationFunctor<T> {
     dx.device(d) =
         dout * ((x - temp).exp() / ((-temp).exp() + (x - temp).exp()));
   }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
 };
 
 // softsign(x) = x / (1 + |x|)
@@ -842,6 +916,8 @@ struct SoftsignGradFunctor : public BaseActivationFunctor<T> {
     dx.device(d) =
         dout * (static_cast<T>(1) / (static_cast<T>(1) + x.abs()).square());
   }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
 };
 
 template <typename T>
@@ -872,6 +948,8 @@ struct SoftReluGradFunctor : public BaseActivationFunctor<T> {
     auto temp = ((out > -tmp) * (out < tmp)).template cast<T>().eval();
     dx.device(d) = dout * (static_cast<T>(1) - (-out).exp()) * temp;
   }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepOut; }
 };
 
 template <typename T>
@@ -901,6 +979,8 @@ struct LeakyReluGradFunctor : public BaseActivationFunctor<T> {
     auto temp2 = (x >= static_cast<T>(0)).template cast<T>().eval();
     dx.device(d) = dout * (temp1 + temp2).template cast<T>();
   }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
 };
 
 template <typename T>
@@ -928,9 +1008,11 @@ struct ELUGradFunctor : public BaseActivationFunctor<T> {
             typename dX>
   void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
     dx.device(d) = dout * (x > static_cast<T>(0)).template cast<T>() +
-                   dout * (out + static_cast<T>(alpha)) *
+                   dout * static_cast<T>(alpha) * x.exp() *
                        (x < static_cast<T>(0)).template cast<T>();
   }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
 };
 
 // FIXME(qijun) https://github.com/PaddlePaddle/Paddle/issues/5198
@@ -958,6 +1040,8 @@ struct PowGradFunctor : public BaseActivationFunctor<T> {
     dx.device(d) = dout * static_cast<T>(factor) *
                    x.pow(static_cast<T>(factor) - static_cast<T>(1));
   }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
 };
 
 template <typename T>
@@ -991,6 +1075,8 @@ struct STanhGradFunctor : public BaseActivationFunctor<T> {
     auto temp = (a * x).tanh() * (a * x).tanh();
     dx.device(d) = dout * a * b * (static_cast<T>(1) - temp);
   }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
 };
 
 template <typename T>
@@ -1020,6 +1106,8 @@ struct ThresholdedReluGradFunctor : public BaseActivationFunctor<T> {
     auto th = static_cast<T>(threshold);
     dx.device(d) = dout * (x > th).template cast<T>();
   }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
 };
 
 template <typename T>
@@ -1053,6 +1141,8 @@ struct HardSigmoidGradFunctor : public BaseActivationFunctor<T> {
                        .template cast<T>() *
                    static_cast<T>(slope);
   }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepOut; }
 };
 
 template <typename T>
@@ -1077,49 +1167,54 @@ struct SwishGradFunctor : 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 {
+  void operator()(Device d, X x, Out fake_out, dOut dout, dX dx) const {
     auto temp1 = static_cast<T>(1) /
                  (static_cast<T>(1) + (static_cast<T>(-beta) * x).exp());
+    auto out = x * temp1;
     auto temp2 = temp1 * (static_cast<T>(1) - (static_cast<T>(beta) * out));
     dx.device(d) = dout * ((static_cast<T>(beta) * out) + temp2);
   }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
 };
 
 }  // namespace operators
 }  // namespace paddle
 
-#define FOR_EACH_KERNEL_FUNCTOR(__macro)                             \
-  __macro(sigmoid, SigmoidFunctor, SigmoidGradFunctor);              \
-  __macro(logsigmoid, LogSigmoidFunctor, LogSigmoidGradFunctor);     \
-  __macro(exp, ExpFunctor, ExpGradFunctor);                          \
-  __macro(relu, ReluFunctor, ReluGradFunctor);                       \
-  __macro(gelu, GeluFunctor, GeluGradFunctor);                       \
-  __macro(tanh, TanhFunctor, TanhGradFunctor);                       \
-  __macro(atan, AtanFunctor, AtanGradFunctor);                       \
-  __macro(softshrink, SoftShrinkFunctor, SoftShrinkGradFunctor);     \
-  __macro(sqrt, SqrtFunctor, SqrtGradFunctor);                       \
-  __macro(abs, AbsFunctor, AbsGradFunctor);                          \
-  __macro(ceil, CeilFunctor, ZeroGradFunctor);                       \
-  __macro(floor, FloorFunctor, ZeroGradFunctor);                     \
-  __macro(cos, CosFunctor, CosGradFunctor);                          \
-  __macro(acos, AcosFunctor, AcosGradFunctor);                       \
-  __macro(sin, SinFunctor, SinGradFunctor);                          \
-  __macro(asin, AsinFunctor, AsinGradFunctor);                       \
-  __macro(round, RoundFunctor, ZeroGradFunctor);                     \
-  __macro(reciprocal, ReciprocalFunctor, ReciprocalGradFunctor);     \
-  __macro(log, LogFunctor, LogGradFunctor);                          \
-  __macro(square, SquareFunctor, SquareGradFunctor);                 \
-  __macro(brelu, BReluFunctor, BReluGradFunctor);                    \
-  __macro(soft_relu, SoftReluFunctor, SoftReluGradFunctor);          \
-  __macro(pow, PowFunctor, PowGradFunctor);                          \
-  __macro(stanh, STanhFunctor, STanhGradFunctor);                    \
-  __macro(softplus, SoftplusFunctor, SoftplusGradFunctor);           \
-  __macro(softsign, SoftsignFunctor, SoftsignGradFunctor);           \
-  __macro(relu6, Relu6Functor, Relu6GradFunctor);                    \
-  __macro(leaky_relu, LeakyReluFunctor, LeakyReluGradFunctor);       \
-  __macro(tanh_shrink, TanhShrinkFunctor, TanhShrinkGradFunctor);    \
-  __macro(elu, ELUFunctor, ELUGradFunctor);                          \
-  __macro(hard_shrink, HardShrinkFunctor, HardShrinkGradFunctor);    \
-  __macro(hard_sigmoid, HardSigmoidFunctor, HardSigmoidGradFunctor); \
-  __macro(swish, SwishFunctor, SwishGradFunctor);                    \
-  __macro(thresholded_relu, ThresholdedReluFunctor, ThresholdedReluGradFunctor);
+#define FOR_EACH_ACTIVATION_OP(__macro)                                       \
+  __macro(sigmoid, Sigmoid, SigmoidFunctor, SigmoidGradFunctor);              \
+  __macro(logsigmoid, LogSigmoid, LogSigmoidFunctor, LogSigmoidGradFunctor);  \
+  __macro(exp, Exp, ExpFunctor, ExpGradFunctor);                              \
+  __macro(relu, Relu, ReluFunctor, ReluGradFunctor);                          \
+  __macro(gelu, Gelu, GeluFunctor, GeluGradFunctor);                          \
+  __macro(tanh, Tanh, TanhFunctor, TanhGradFunctor);                          \
+  __macro(atan, Atan, AtanFunctor, AtanGradFunctor);                          \
+  __macro(softshrink, SoftShrink, SoftShrinkFunctor, SoftShrinkGradFunctor);  \
+  __macro(sqrt, Sqrt, SqrtFunctor, SqrtGradFunctor);                          \
+  __macro(abs, Abs, AbsFunctor, AbsGradFunctor);                              \
+  __macro(ceil, Ceil, CeilFunctor, ZeroGradFunctor);                          \
+  __macro(floor, Floor, FloorFunctor, ZeroGradFunctor);                       \
+  __macro(cos, Cos, CosFunctor, CosGradFunctor);                              \
+  __macro(acos, Acos, AcosFunctor, AcosGradFunctor);                          \
+  __macro(sin, Sin, SinFunctor, SinGradFunctor);                              \
+  __macro(asin, Asin, AsinFunctor, AsinGradFunctor);                          \
+  __macro(round, Round, RoundFunctor, ZeroGradFunctor);                       \
+  __macro(reciprocal, Reciprocal, ReciprocalFunctor, ReciprocalGradFunctor);  \
+  __macro(log, Log, LogFunctor, LogGradFunctor);                              \
+  __macro(square, Square, SquareFunctor, SquareGradFunctor);                  \
+  __macro(brelu, BRelu, BReluFunctor, BReluGradFunctor);                      \
+  __macro(soft_relu, SoftRelu, SoftReluFunctor, SoftReluGradFunctor);         \
+  __macro(pow, Pow, PowFunctor, PowGradFunctor);                              \
+  __macro(stanh, STanh, STanhFunctor, STanhGradFunctor);                      \
+  __macro(softplus, Softplus, SoftplusFunctor, SoftplusGradFunctor);          \
+  __macro(softsign, Softsign, SoftsignFunctor, SoftsignGradFunctor);          \
+  __macro(relu6, Relu6, Relu6Functor, Relu6GradFunctor);                      \
+  __macro(leaky_relu, LeakyRelu, LeakyReluFunctor, LeakyReluGradFunctor);     \
+  __macro(tanh_shrink, TanhShrink, TanhShrinkFunctor, TanhShrinkGradFunctor); \
+  __macro(elu, ELU, ELUFunctor, ELUGradFunctor);                              \
+  __macro(hard_shrink, HardShrink, HardShrinkFunctor, HardShrinkGradFunctor); \
+  __macro(hard_sigmoid, HardSigmoid, HardSigmoidFunctor,                      \
+          HardSigmoidGradFunctor);                                            \
+  __macro(swish, Swish, SwishFunctor, SwishGradFunctor);                      \
+  __macro(thresholded_relu, ThresholdedRelu, ThresholdedReluFunctor,          \
+          ThresholdedReluGradFunctor);

paddle/fluid/operators/detection/gpc.cc

@@ -24,6 +24,7 @@
  **/
 
 #include "paddle/fluid/operators/detection/gpc.h"
+#include "paddle/fluid/platform/enforce.h"
 
 namespace gpc {
 
@@ -689,6 +690,7 @@ static bbox *create_contour_bboxes(gpc_polygon *p) {
 
   gpc_malloc<bbox>(box, p->num_contours * sizeof(bbox),
                    const_cast<char *>("Bounding box creation"));
+  PADDLE_ENFORCE_NOT_NULL(box);
 
   /* Construct contour bounding boxes */
   for (c = 0; c < p->num_contours; c++) {
@@ -852,6 +854,7 @@ void gpc_add_contour(gpc_polygon *p, gpc_vertex_list *new_contour, int hole) {
   /* Create an extended hole array */
   gpc_malloc<int>(extended_hole, (p->num_contours + 1) * sizeof(int),
                   const_cast<char *>("contour hole addition"));
+  PADDLE_ENFORCE_NOT_NULL(extended_hole);
 
   /* Create an extended contour array */
   gpc_malloc<gpc_vertex_list>(extended_contour,
@@ -969,6 +972,7 @@ void gpc_polygon_clip(gpc_op op, gpc_polygon *subj, gpc_polygon *clip,
   /* Build scanbeam table from scanbeam tree */
   gpc_malloc<double>(sbt, sbt_entries * sizeof(double),
                      const_cast<char *>("sbt creation"));
+  PADDLE_ENFORCE_NOT_NULL(sbt);
   build_sbt(&scanbeam, sbt, sbtree);
   scanbeam = 0;
   free_sbtree(&sbtree);
@@ -1604,6 +1608,7 @@ void gpc_tristrip_clip(gpc_op op, gpc_polygon *subj, gpc_polygon *clip,
   /* Build scanbeam table from scanbeam tree */
   gpc_malloc<double>(sbt, sbt_entries * sizeof(double),
                      const_cast<char *>("sbt creation"));
+  PADDLE_ENFORCE_NOT_NULL(sbt);
   build_sbt(&scanbeam, sbt, sbtree);
   scanbeam = 0;
   free_sbtree(&sbtree);

paddle/fluid/operators/distributed/grpc/grpc_serde.cc

@@ -16,6 +16,7 @@ limitations under the License. */
 #include <nccl.h>
 #endif
 #include <limits>
+#include <memory>
 #include <thread>  // NOLINT
 
 #include "google/protobuf/io/coded_stream.h"
@@ -104,8 +105,10 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
   e.WriteVarlengthBeginning(VarMsg::kSerializedFieldNumber,
                             payload->memory_size());
   if (payload->memory_size() >= std::numeric_limits<int>::max()) {
-    LOG(FATAL) << "AppendZeroCopy varname:" << name
-               << ", vlen:" << payload->memory_size();
+    LOG(FATAL) << "FATAL error: varname:" << name
+               << ", vlen:" << payload->memory_size()
+               << " >= std::numeric_limits<int>::max():"
+               << std::numeric_limits<int>::max() << ", so exit!";
   }
   // steal reference of tensor data
   ::grpc::Slice slices[4];  // metadata, tensor, rows meta, rows

paddle/fluid/operators/interpolate_op.cc

@@ -37,10 +37,19 @@ class InterpolateOp : public framework::OperatorWithKernel {
         "Interpolation method can only be \"bilinear\" or \"nearest\".");
 
     auto dim_x = ctx->GetInputDim("X");  // NCHW format
-    int out_h = ctx->Attrs().Get<int>("out_h");
-    int out_w = ctx->Attrs().Get<int>("out_w");
     PADDLE_ENFORCE_EQ(dim_x.size(), 4, "X's dimension must be 4");
 
+    int out_h, out_w;
+    float scale = ctx->Attrs().Get<float>("scale");
+    if (scale > 0) {
+      // round down
+      out_h = static_cast<int>(dim_x[2] * scale);
+      out_w = static_cast<int>(dim_x[3] * scale);
+    } else {
+      out_h = ctx->Attrs().Get<int>("out_h");
+      out_w = ctx->Attrs().Get<int>("out_w");
+    }
+
     if (ctx->HasInput("OutSize") && ctx->IsRuntime()) {
       auto out_size_dim = ctx->GetInputDim("OutSize");
       PADDLE_ENFORCE_EQ(out_size_dim.size(), 1,
@@ -77,6 +86,7 @@ class InterpolateOpMaker : public framework::OpProtoAndCheckerMaker {
 
     AddAttr<int>("out_h", "output height of interpolate op.");
     AddAttr<int>("out_w", "output width of interpolate op.");
+    AddAttr<float>("scale", "scale factor of interpolate op.").SetDefault(0.);
     AddAttr<std::string>("interp_method",
                          "(string, default \"bilinear\"), interpolation "
                          "method, can be \"bilinear\" for "

paddle/fluid/operators/interpolate_op.cu

@@ -192,9 +192,21 @@ class InterpolateOpCUDAKernel : public framework::OpKernel<T> {
     auto* output = ctx.Output<Tensor>("Out");
     auto* input_data = input->data<T>();
 
+    int n = input->dims()[0];
+    int c = input->dims()[1];
+    int in_h = input->dims()[2];
+    int in_w = input->dims()[3];
+
     auto interp_method = ctx.Attr<std::string>("interp_method");
     int out_h = ctx.Attr<int>("out_h");
     int out_w = ctx.Attr<int>("out_w");
+
+    float scale = ctx.Attr<float>("scale");
+    if (scale > 0) {
+      out_h = in_h * scale;
+      out_w = in_w * scale;
+    }
+
     auto out_size = ctx.Input<Tensor>("OutSize");
     if (out_size != nullptr) {
       Tensor sizes;
@@ -207,11 +219,6 @@ class InterpolateOpCUDAKernel : public framework::OpKernel<T> {
     bool align_corners = ctx.Attr<bool>("align_corners");
     int align_mode = ctx.Attr<int>("align_mode");
 
-    int n = input->dims()[0];
-    int c = input->dims()[1];
-    int in_h = input->dims()[2];
-    int in_w = input->dims()[3];
-
     auto* output_data =
         output->mutable_data<T>({n, c, out_h, out_w}, ctx.GetPlace());
 
@@ -268,14 +275,20 @@ class InterpolateGradOpCUDAKernel : public framework::OpKernel<T> {
     math::SetConstant<platform::CUDADeviceContext, T> zero;
     zero(device_ctx, input_grad, static_cast<T>(0.0));
 
+    int n = input_grad->dims()[0];
+    int c = input_grad->dims()[1];
+    int in_h = input_grad->dims()[2];
+    int in_w = input_grad->dims()[3];
+
     auto interp_method = ctx.Attr<std::string>("interp_method");
     int out_h = ctx.Attr<int>("out_h");
     int out_w = ctx.Attr<int>("out_w");
+    float scale = ctx.Attr<float>("scale");
+    if (scale > 0) {
+      out_h = in_h * scale;
+      out_w - in_w* scale;
+    }
     auto out_size = ctx.Input<Tensor>("OutSize");
-
-    bool align_corners = ctx.Attr<bool>("align_corners");
-    int align_mode = ctx.Attr<int>("align_mode");
-
     if (out_size != nullptr) {
       Tensor sizes;
       framework::TensorCopy(*out_size, platform::CPUPlace(), &sizes);
@@ -284,10 +297,8 @@ class InterpolateGradOpCUDAKernel : public framework::OpKernel<T> {
       out_w = size_data[1];
     }
 
-    int n = input_grad->dims()[0];
-    int c = input_grad->dims()[1];
-    int in_h = input_grad->dims()[2];
-    int in_w = input_grad->dims()[3];
+    bool align_corners = ctx.Attr<bool>("align_corners");
+    int align_mode = ctx.Attr<int>("align_mode");
 
     int in_hw = in_h * in_w;
     int out_hw = out_h * out_w;

paddle/fluid/operators/interpolate_op.h

@@ -163,9 +163,21 @@ class InterpolateKernel : public framework::OpKernel<T> {
     auto* input = ctx.Input<Tensor>("X");
     auto* output = ctx.Output<Tensor>("Out");
 
+    const int n = input->dims()[0];
+    const int c = input->dims()[1];
+    const int in_h = input->dims()[2];
+    const int in_w = input->dims()[3];
+
     std::string interp_method = ctx.Attr<std::string>("interp_method");
     int out_h = ctx.Attr<int>("out_h");
     int out_w = ctx.Attr<int>("out_w");
+
+    float scale = ctx.Attr<float>("scale");
+    if (scale > 0) {
+      out_h = static_cast<int>(in_h * scale);
+      out_w = static_cast<int>(in_w * scale);
+    }
+
     auto out_size = ctx.Input<Tensor>("OutSize");
     if (out_size != nullptr) {
       auto out_size_data = out_size->data<int>();
@@ -175,11 +187,6 @@ class InterpolateKernel : public framework::OpKernel<T> {
     bool align_corners = ctx.Attr<bool>("align_corners");
     int align_mode = ctx.Attr<int>("align_mode");
 
-    const int n = input->dims()[0];
-    const int c = input->dims()[1];
-    const int in_h = input->dims()[2];
-    const int in_w = input->dims()[3];
-
     output->mutable_data<T>({n, c, out_h, out_w}, ctx.GetPlace());
     auto& device_ctx =
         ctx.template device_context<platform::CPUDeviceContext>();
@@ -221,23 +228,31 @@ class InterpolateGradKernel : public framework::OpKernel<T> {
     auto* input_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
     auto* output_grad = ctx.Input<Tensor>(framework::GradVarName("Out"));
 
+    const int n = input->dims()[0];
+    const int c = input->dims()[1];
+    const int in_h = input->dims()[2];
+    const int in_w = input->dims()[3];
+
     std::string interp_method = ctx.Attr<std::string>("interp_method");
     int out_h = ctx.Attr<int>("out_h");
     int out_w = ctx.Attr<int>("out_w");
+
+    float scale = ctx.Attr<float>("scale");
+    if (scale > 0) {
+      out_h = static_cast<int>(in_h * scale);
+      out_w = static_cast<int>(in_w * scale);
+    }
+
     auto out_size = ctx.Input<Tensor>("OutSize");
     if (out_size != nullptr) {
       auto out_size_data = out_size->data<int>();
       out_h = out_size_data[0];
       out_w = out_size_data[1];
     }
+
     bool align_corners = ctx.Attr<bool>("align_corners");
     int align_mode = ctx.Attr<int>("align_mode");
 
-    const int n = input->dims()[0];
-    const int c = input->dims()[1];
-    const int in_h = input->dims()[2];
-    const int in_w = input->dims()[3];
-
     input_grad->mutable_data<T>({n, c, in_h, in_w}, ctx.GetPlace());
     auto& device_ctx =
         ctx.template device_context<platform::CPUDeviceContext>();

paddle/fluid/operators/linspace_op.cc

+/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/fluid/operators/linspace_op.h"
+
+namespace paddle {
+namespace operators {
+
+class LinspaceOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("Start"),
+                   "Input(Start) of LinspaceOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Stop"),
+                   "Input(Stop) of LinspaceOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Num"),
+                   "Input(Num) of LinspaceOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output(OUt) of LinspaceOp should not be null.");
+
+    auto s_dims = ctx->GetInputDim("Start");
+    PADDLE_ENFORCE((s_dims.size() == 1) && (s_dims[0] == 1),
+                   "The shape of Input(Start) should be [1].");
+
+    auto e_dims = ctx->GetInputDim("Stop");
+    PADDLE_ENFORCE((e_dims.size() == 1) && (e_dims[0] == 1),
+                   "The shape of Input(Stop) should be [1].");
+
+    auto step_dims = ctx->GetInputDim("Num");
+    PADDLE_ENFORCE((step_dims.size() == 1) && (step_dims[0] == 1),
+                   "The shape of Input(Num) should be [1].");
+
+    ctx->SetOutputDim("Out", {-1});
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    framework::LibraryType library_{framework::LibraryType::kPlain};
+    framework::DataLayout layout_ = framework::DataLayout::kAnyLayout;
+    return framework::OpKernelType(
+        ctx.Input<framework::Tensor>("Start")->type(), ctx.device_context(),
+        layout_, library_);
+  }
+};
+
+class LinspaceOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("Start",
+             "First entry in the sequence. It is a tensor of shape [1], should "
+             "be of type float32 or float64.");
+    AddInput("Stop",
+             "Last entry in the sequence. It is a tensor of shape [1], should "
+             "be of type float32 or float64.");
+    AddInput("Num",
+             "Number of entry in the sequence. It is a tensor of shape [1], "
+             "should be of type int32.");
+    AddOutput("Out", "A sequence of numbers.");
+    AddComment(R"DOC(
+    Return fixed number of evenly spaced values within a given interval. First entry is start, and last entry is stop. In the case when Num is 1, only Start is returned. Like linspace function of numpy.
+)DOC");
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_WITHOUT_GRADIENT(linspace, ops::LinspaceOp, ops::LinspaceOpMaker);
+REGISTER_OP_CPU_KERNEL(linspace, ops::CPULinspaceKernel<float>,
+                       ops::CPULinspaceKernel<double>);

paddle/fluid/operators/linspace_op.cu

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/linspace_op.h"
+#include "paddle/fluid/platform/cuda_primitives.h"
+
+namespace paddle {
+namespace operators {
+
+#define CUDA_1D_KERNEL_LOOP(i, n)                              \
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \
+       i += blockDim.x * gridDim.x)
+
+template <typename T>
+__global__ void LinspaceKernel(T start, T step, int64_t size, T* out) {
+  CUDA_1D_KERNEL_LOOP(index, size) { out[index] = start + step * index; }
+}
+
+template <typename T>
+__global__ void LinspaceSpecialKernel(T start, T* out) {
+  out[0] = start;
+}
+
+template <typename T>
+class CUDALinspaceKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* start_t = context.Input<framework::Tensor>("Start");
+    auto* stop_t = context.Input<framework::Tensor>("Stop");
+    auto* num_t = context.Input<framework::Tensor>("Num");
+    auto* out = context.Output<framework::Tensor>("Out");
+
+    framework::Tensor n;
+    framework::TensorCopy(*start_t, platform::CPUPlace(), &n);
+    T start = n.data<T>()[0];
+    framework::TensorCopy(*stop_t, platform::CPUPlace(), &n);
+    T stop = n.data<T>()[0];
+    framework::TensorCopy(*num_t, platform::CPUPlace(), &n);
+    int32_t num = n.data<int32_t>()[0];
+
+    PADDLE_ENFORCE(num > 0, "The num of linspace op should be larger than 0.");
+
+    out->Resize(framework::make_ddim({num}));
+    T* out_data = out->mutable_data<T>(context.GetPlace());
+
+    T step = 0;
+    if (num != 1) {
+      step = (stop - start) / (num - 1);
+    }
+
+    auto stream = context.cuda_device_context().stream();
+    int block = 512;
+    int grid = (num + block - 1) / block;
+    LinspaceKernel<T><<<grid, block, 0, stream>>>(start, step, num, out_data);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(linspace, ops::CUDALinspaceKernel<float>,
+                        ops::CUDALinspaceKernel<double>);

paddle/fluid/operators/linspace_op.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+#include <functional>
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/math/math_function.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+class CPULinspaceKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    T start = context.Input<framework::Tensor>("Start")->data<T>()[0];
+    T stop = context.Input<framework::Tensor>("Stop")->data<T>()[0];
+    int32_t num = context.Input<framework::Tensor>("Num")->data<int32_t>()[0];
+    auto* out = context.Output<framework::Tensor>("Out");
+    PADDLE_ENFORCE(num > 0, "The num of linspace op should be larger than 0.");
+
+    out->Resize(framework::make_ddim({num}));
+
+    T* out_data = out->mutable_data<T>(context.GetPlace());
+
+    if (num > 1) {
+      T step = (stop - start) / (num - 1);
+      T value = start;
+      for (int i = 0; i < num; ++i) {
+        out_data[i] = value;
+        value += step;
+      }
+    } else {
+      out_data[0] = start;
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/lookup_table_op.cc

@@ -13,6 +13,10 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/lookup_table_op.h"
+
+#include <memory>
+
+#include "paddle/fluid/framework/no_need_buffer_vars_inference.h"
 #include "paddle/fluid/framework/var_type_inference.h"
 
 namespace paddle {
@@ -119,6 +123,29 @@ or not. And the output only shares the LoD information with input Ids.
   }
 };
 
+DECLARE_NO_NEED_BUFFER_VARS_INFERENCE(LookupTableGradOpNoBuffer, "W");
+
+class LookupTableGradOpDescMaker : 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("lookup_table_grad");
+
+    op->SetInput("W", Input("W"));
+    op->SetInput("Ids", Input("Ids"));
+    op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+
+    op->SetOutput(framework::GradVarName("W"), InputGrad("W"));
+
+    op->SetAttrMap(Attrs());
+    return op;
+  }
+};
+
 class LookupTableOpGrad : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
@@ -131,7 +158,8 @@ class LookupTableOpGrad : public framework::OperatorWithKernel {
  protected:
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
-    auto data_type = framework::GetDataTypeOfVar(ctx.InputVar("Out"));
+    auto data_type = framework::GetDataTypeOfVar(
+        ctx.InputVar(framework::GradVarName("Out")));
     return framework::OpKernelType(data_type, ctx.device_context());
   }
 };
@@ -159,10 +187,11 @@ class LookupTableOpGradVarTypeInference : public framework::VarTypeInference {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OPERATOR(lookup_table, ops::LookupTableOp,
-                  paddle::framework::DefaultGradOpDescMaker<true>,
-                  ops::LookupTableOpMaker);
+REGISTER_OPERATOR(lookup_table, ops::LookupTableOp, ops::LookupTableOpMaker,
+                  ops::LookupTableGradOpDescMaker);
+
 REGISTER_OPERATOR(lookup_table_grad, ops::LookupTableOpGrad,
+                  ops::LookupTableGradOpNoBuffer,
                   ops::LookupTableOpGradVarTypeInference);
 
 REGISTER_OP_CPU_KERNEL(lookup_table, ops::LookupTableKernel<float>,

paddle/fluid/operators/reduce_ops/reduce_all_op.cc

+// 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/reduce_ops/reduce_all_op.h"
+
+REGISTER_REDUCE_OP(reduce_all);
+REGISTER_OP_CPU_KERNEL(reduce_all,
+                       ops::ReduceKernel<paddle::platform::CPUDeviceContext,
+                                         bool, ops::AllFunctor>);

paddle/fluid/operators/reduce_ops/reduce_all_op.cu

+// 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/reduce_ops/reduce_all_op.h"
+
+REGISTER_OP_CUDA_KERNEL(reduce_all,
+                        ops::ReduceKernel<paddle::platform::CUDADeviceContext,
+                                          bool, ops::AllFunctor>);

paddle/fluid/operators/reduce_ops/reduce_all_op.h

+// 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/operators/reduce_ops/reduce_op.h"
+
+namespace paddle {
+namespace operators {
+
+struct AllFunctor {
+  template <typename DeviceContext, typename X, typename Y, typename Dim>
+  void operator()(const DeviceContext& place, X* x, Y* y, const Dim& dim) {
+    y->device(place) = x->all(dim);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/reduce_ops/reduce_any_op.cc

+// Copyright (c) 2018 PaddlePaddle Authors. Any 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/reduce_ops/reduce_any_op.h"
+
+REGISTER_REDUCE_OP(reduce_any);
+REGISTER_OP_CPU_KERNEL(reduce_any,
+                       ops::ReduceKernel<paddle::platform::CPUDeviceContext,
+                                         bool, ops::AnyFunctor>);

paddle/fluid/operators/reduce_ops/reduce_any_op.cu

+// Copyright (c) 2018 PaddlePaddle Authors. Any 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/reduce_ops/reduce_any_op.h"
+
+REGISTER_OP_CUDA_KERNEL(reduce_any,
+                        ops::ReduceKernel<paddle::platform::CUDADeviceContext,
+                                          bool, ops::AnyFunctor>);

paddle/fluid/operators/reduce_ops/reduce_any_op.h

+// 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/operators/reduce_ops/reduce_op.h"
+
+namespace paddle {
+namespace operators {
+
+struct AnyFunctor {
+  template <typename DeviceContext, typename X, typename Y, typename Dim>
+  void operator()(const DeviceContext& place, X* x, Y* y, const Dim& dim) {
+    y->device(place) = x->any(dim);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/space_to_depth_op.cc

@@ -13,12 +13,18 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/space_to_depth_op.h"
+
+#include <memory>
 #include <string>
 #include <vector>
 
+#include "paddle/fluid/framework/no_need_buffer_vars_inference.h"
+
 namespace paddle {
 namespace operators {
 
+using Tensor = framework::Tensor;
+
 class SpaceToDepthOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
@@ -100,6 +106,28 @@ class SpaceToDepthOpMaker : public framework::OpProtoAndCheckerMaker {
   }
 };
 
+DECLARE_NO_NEED_BUFFER_VARS_INFERENCE(SpaceToDepthGradOpNoBuffer, "X");
+
+class SpaceToDepthGradOpDescMaker : 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("space_to_depth_grad");
+
+    op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+    op->SetInput("X", Input("X"));
+
+    op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+
+    op->SetAttrMap(Attrs());
+    return op;
+  }
+};
+
 class SpaceToDepthGradOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
@@ -110,6 +138,14 @@ class SpaceToDepthGradOp : public framework::OperatorWithKernel {
                    "Input(Out@GRAD) shouldn't be null.");
     ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
   }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        ctx.Input<Tensor>(framework::GradVarName("Out"))->type(),
+        ctx.GetPlace());
+  }
 };
 }  // namespace operators
 }  // namespace paddle
@@ -117,8 +153,9 @@ class SpaceToDepthGradOp : public framework::OperatorWithKernel {
 namespace ops = paddle::operators;
 
 REGISTER_OPERATOR(space_to_depth, ops::SpaceToDepthOp, ops::SpaceToDepthOpMaker,
-                  paddle::framework::DefaultGradOpDescMaker<true>);
-REGISTER_OPERATOR(space_to_depth_grad, ops::SpaceToDepthGradOp);
+                  ops::SpaceToDepthGradOpDescMaker);
+REGISTER_OPERATOR(space_to_depth_grad, ops::SpaceToDepthGradOp,
+                  ops::SpaceToDepthGradOpNoBuffer);
 REGISTER_OP_CPU_KERNEL(
     space_to_depth,
     ops::SpaceToDepthKernel<paddle::platform::CPUDeviceContext, float>,

paddle/fluid/operators/squared_l2_distance_op.cc

@@ -14,6 +14,10 @@ limitations under the License. */
 
 #include "paddle/fluid/operators/squared_l2_distance_op.h"
 
+#include <memory>
+
+#include "paddle/fluid/framework/no_need_buffer_vars_inference.h"
+
 namespace paddle {
 namespace operators {
 
@@ -54,6 +58,34 @@ class SquaredL2DistanceOp : public framework::OperatorWithKernel {
   }
 };
 
+DECLARE_NO_NEED_BUFFER_VARS_INFERENCE(SquaredL2DistanceGradOpNoBuffer, "X",
+                                      "Y");
+
+class SquaredL2DistanceGradOpDescMaker
+    : 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("squared_l2_distance_grad");
+
+    op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+    op->SetInput("sub_result", Output("sub_result"));
+    op->SetInput("X", Input("X"));
+    op->SetInput("Y", Input("Y"));
+
+    op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+    op->SetOutput(framework::GradVarName("Y"), InputGrad("Y"));
+
+    op->SetAttrMap(Attrs());
+
+    return op;
+  }
+};
+
 class SquaredL2DistanceOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
@@ -88,6 +120,7 @@ class SquaredL2DistanceGradOp : public framework::OperatorWithKernel {
   void InferShape(framework::InferShapeContext* ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
                    "Gradient of Out should not be null");
+    PADDLE_ENFORCE(ctx->HasInput("sub_result"), "SubResult should not be null");
     auto out_dims = ctx->GetInputDim(framework::GradVarName("Out"));
     auto x_dims = ctx->GetInputDim("X");
     auto y_dims = ctx->GetInputDim("Y");
@@ -102,6 +135,13 @@ class SquaredL2DistanceGradOp : public framework::OperatorWithKernel {
     if (ctx->HasOutput(x_grad_name)) ctx->SetOutputDim(x_grad_name, x_dims);
     if (ctx->HasOutput(y_grad_name)) ctx->SetOutputDim(y_grad_name, y_dims);
   }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(ctx.Input<Tensor>("sub_result")->type(),
+                                   ctx.GetPlace());
+  }
 };
 
 }  // namespace operators
@@ -110,8 +150,9 @@ class SquaredL2DistanceGradOp : public framework::OperatorWithKernel {
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(squared_l2_distance, ops::SquaredL2DistanceOp,
                   ops::SquaredL2DistanceOpMaker,
-                  paddle::framework::DefaultGradOpDescMaker<true>);
-REGISTER_OPERATOR(squared_l2_distance_grad, ops::SquaredL2DistanceGradOp);
+                  ops::SquaredL2DistanceGradOpDescMaker);
+REGISTER_OPERATOR(squared_l2_distance_grad, ops::SquaredL2DistanceGradOp,
+                  ops::SquaredL2DistanceGradOpNoBuffer);
 REGISTER_OP_CPU_KERNEL(
     squared_l2_distance,
     ops::SquaredL2DistanceKernel<paddle::platform::CPUDeviceContext, float>);

paddle/fluid/operators/squared_l2_distance_op.h

@@ -77,6 +77,9 @@ class SquaredL2DistanceGradKernel : public framework::OpKernel<T> {
     auto* x_g = context.Output<Tensor>(framework::GradVarName("X"));
     auto* y_g = context.Output<Tensor>(framework::GradVarName("Y"));
 
+    PADDLE_ENFORCE_NOT_NULL(x_g);
+    PADDLE_ENFORCE_NOT_NULL(y_g);
+
     auto sub_result = EigenMatrix<T>::From(*in0);
     auto out_grad = EigenMatrix<T>::From(*in1);
 
@@ -92,31 +95,28 @@ class SquaredL2DistanceGradKernel : public framework::OpKernel<T> {
     // propagate back to input
     auto& eigen_place =
         *context.template device_context<DeviceContext>().eigen_device();
-    if (x_g) {
-      x_g->mutable_data<T>(context.GetPlace());
-      // eigen matrix
-      auto x_grad =
-          EigenMatrix<T>::From(*x_g, framework::make_ddim({x_dims[0], cols}));
-      // dimensions are same with subResult
-      x_grad.device(eigen_place) = grad_mat;
-    }
 
-    if (y_g) {
-      y_g->mutable_data<T>(context.GetPlace());
-
-      PADDLE_ENFORCE_GE(sub_result.dimensions()[0], y_dims[0],
-                        "First dimension of gradient must be greater or "
-                        "equal than first dimension of target.");
-
-      if (sub_result.dimensions()[0] == y_dims[0]) {
-        auto y_grad =
-            EigenMatrix<T>::From(*y_g, framework::make_ddim({y_dims[0], cols}));
-        y_grad.device(eigen_place) = -1 * grad_mat;
-      } else {
-        auto col_sum_res = -1 * (grad_mat.sum(Eigen::array<int, 1>({{0}})));
-        auto y_grad = EigenVector<T>::Flatten(*y_g);
-        y_grad.device(eigen_place) = col_sum_res;
-      }
+    x_g->mutable_data<T>(context.GetPlace());
+    // eigen matrix
+    auto x_grad =
+        EigenMatrix<T>::From(*x_g, framework::make_ddim({x_dims[0], cols}));
+    // dimensions are same with subResult
+    x_grad.device(eigen_place) = grad_mat;
+
+    y_g->mutable_data<T>(context.GetPlace());
+
+    PADDLE_ENFORCE_GE(sub_result.dimensions()[0], y_dims[0],
+                      "First dimension of gradient must be greater or "
+                      "equal than first dimension of target.");
+
+    if (sub_result.dimensions()[0] == y_dims[0]) {
+      auto y_grad =
+          EigenMatrix<T>::From(*y_g, framework::make_ddim({y_dims[0], cols}));
+      y_grad.device(eigen_place) = -1 * grad_mat;
+    } else {
+      auto col_sum_res = -1 * (grad_mat.sum(Eigen::array<int, 1>({{0}})));
+      auto y_grad = EigenVector<T>::Flatten(*y_g);
+      y_grad.device(eigen_place) = col_sum_res;
     }
   }
 };

paddle/fluid/operators/squared_l2_norm_op.cc

@@ -14,6 +14,8 @@ limitations under the License. */
 
 #include "paddle/fluid/operators/squared_l2_norm_op.h"
 
+#include <memory>
+
 namespace paddle {
 namespace operators {
 
@@ -31,6 +33,26 @@ class SquaredL2NormOp : public framework::OperatorWithKernel {
   }
 };
 
+class SquaredL2NormGradOpDescMaker : 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("squared_l2_norm_grad");
+
+    op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+    op->SetInput("X", Input("X"));
+
+    op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+
+    op->SetAttrMap(Attrs());
+    return op;
+  }
+};
+
 class SquaredL2NormGradOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
@@ -67,8 +89,7 @@ $$Out = \sum_{i} X_{i}^2$$
 
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(squared_l2_norm, ops::SquaredL2NormOp,
-                  ops::SquaredL2NormOpMaker,
-                  paddle::framework::DefaultGradOpDescMaker<true>);
+                  ops::SquaredL2NormOpMaker, ops::SquaredL2NormGradOpDescMaker);
 REGISTER_OPERATOR(squared_l2_norm_grad, ops::SquaredL2NormGradOp);
 REGISTER_OP_CPU_KERNEL(
     squared_l2_norm,

paddle/fluid/operators/teacher_student_sigmoid_loss_op.cc

@@ -13,6 +13,9 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/teacher_student_sigmoid_loss_op.h"
+
+#include <memory>
+
 #include "paddle/fluid/operators/math/math_function.h"
 
 namespace paddle {
@@ -55,6 +58,28 @@ class TeacherStudentSigmoidLossOp : public framework::OperatorWithKernel {
   }
 };
 
+class TeacherStudentSigmoidLossGradOpDescMaker
+    : 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("teacher_student_sigmoid_loss_grad");
+
+    op->SetInput("X", Input("X"));
+    op->SetInput("Label", Input("Label"));
+    op->SetInput(framework::GradVarName("Y"), OutputGrad("Y"));
+
+    op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+
+    op->SetAttrMap(Attrs());
+    return op;
+  }
+};
+
 class TeacherStudentSigmoidLossGradientOp
     : public framework::OperatorWithKernel {
  public:
@@ -148,7 +173,7 @@ namespace ops = paddle::operators;
 REGISTER_OPERATOR(teacher_student_sigmoid_loss,
                   ops::TeacherStudentSigmoidLossOp,
                   ops::TeacherStudentSigmoidLossOpMaker,
-                  paddle::framework::DefaultGradOpDescMaker<true>);
+                  ops::TeacherStudentSigmoidLossGradOpDescMaker);
 
 REGISTER_OPERATOR(teacher_student_sigmoid_loss_grad,
                   ops::TeacherStudentSigmoidLossGradientOp);

paddle/fluid/operators/tree_conv_op.cc

@@ -13,6 +13,8 @@
 // limitations under the License.
 
 #include "paddle/fluid/operators/tree_conv_op.h"
+
+#include <memory>
 #include <string>
 
 namespace paddle {
@@ -86,6 +88,30 @@ class TreeConvOp : public framework::OperatorWithKernel {
   }
 };
 
+class TreeConvGradOpDescMaker : 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("tree_conv_grad");
+
+    op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+    op->SetInput("Filter", Input("Filter"));
+    op->SetInput("EdgeSet", Input("EdgeSet"));
+    op->SetInput("NodesVector", Input("NodesVector"));
+
+    op->SetOutput(framework::GradVarName("NodesVector"),
+                  InputGrad("NodesVector"));
+    op->SetOutput(framework::GradVarName("Filter"), InputGrad("Filter"));
+
+    op->SetAttrMap(Attrs());
+    return op;
+  }
+};
+
 class TreeConvGradOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
@@ -115,7 +141,7 @@ class TreeConvGradOp : public framework::OperatorWithKernel {
 
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(tree_conv, ops::TreeConvOp, ops::TreeConvOpMaker,
-                  paddle::framework::DefaultGradOpDescMaker<true>);
+                  ops::TreeConvGradOpDescMaker);
 
 REGISTER_OPERATOR(tree_conv_grad, ops::TreeConvGradOp);
 

paddle/fluid/operators/warpctc_op.cc

@@ -14,6 +14,8 @@ limitations under the License. */
 
 #include "paddle/fluid/operators/warpctc_op.h"
 
+#include <memory>
+
 #ifdef PADDLE_WITH_CUDA
 #include "paddle/fluid/platform/cudnn_helper.h"
 #endif
@@ -118,6 +120,27 @@ http://machinelearning.wustl.edu/mlpapers/paper_files/icml2006_GravesFGS06.pdf).
   }
 };
 
+class WarpCTCGradOpDescMaker : 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("warpctc_grad");
+
+    op->SetInput("WarpCTCGrad", Output("WarpCTCGrad"));
+    op->SetInput("Logits", Input("Logits"));
+    op->SetInput(framework::GradVarName("Loss"), OutputGrad("Loss"));
+
+    op->SetOutput(framework::GradVarName("Logits"), InputGrad("Logits"));
+
+    op->SetAttrMap(Attrs());
+    return op;
+  }
+};
+
 class WarpCTCGradOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
@@ -145,7 +168,7 @@ class WarpCTCGradOp : public framework::OperatorWithKernel {
 
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(warpctc, ops::WarpCTCOp, ops::WarpCTCOpMaker,
-                  paddle::framework::DefaultGradOpDescMaker<true>);
+                  ops::WarpCTCGradOpDescMaker);
 REGISTER_OPERATOR(warpctc_grad, ops::WarpCTCGradOp);
 REGISTER_OP_CPU_KERNEL(
     warpctc, ops::WarpCTCKernel<paddle::platform::CPUDeviceContext, float>);

paddle/fluid/pybind/pybind.cc

@@ -1366,6 +1366,10 @@ All parameter, weight, gradient are variables in Paddle.
           "cache_runtime_context",
           [](const BuildStrategy &self) { return self.cache_runtime_context_; },
           [](BuildStrategy &self, bool b) { self.cache_runtime_context_ = b; })
+      .def_property(
+          "cache_expected_kernel",
+          [](const BuildStrategy &self) { return self.cache_expected_kernel_; },
+          [](BuildStrategy &self, bool b) { self.cache_expected_kernel_ = b; })
       .def("_finalize_strategy_and_create_passes",
            [](BuildStrategy &self) -> std::shared_ptr<ir::PassBuilder> {
              return self.CreatePassesFromStrategy(true);

paddle/scripts/paddle_build.sh

@@ -202,6 +202,7 @@ function cmake_gen() {
         -DCMAKE_EXPORT_COMPILE_COMMANDS=ON
         -DWITH_CONTRIB=${WITH_CONTRIB:-ON}
         -DWITH_INFERENCE_API_TEST=${WITH_INFERENCE_API_TEST:-ON}
+        -DWITH_HIGH_LEVEL_API_TEST=${WITH_HIGH_LEVEL_API_TEST:-OFF}
         -DINFERENCE_DEMO_INSTALL_DIR=${INFERENCE_DEMO_INSTALL_DIR}
         -DWITH_ANAKIN=${WITH_ANAKIN:-OFF}
         -DANAKIN_BUILD_FAT_BIN=${ANAKIN_BUILD_FAT_BIN:OFF}
@@ -234,6 +235,7 @@ EOF
         -DCMAKE_EXPORT_COMPILE_COMMANDS=ON \
         -DWITH_CONTRIB=${WITH_CONTRIB:-ON} \
         -DWITH_INFERENCE_API_TEST=${WITH_INFERENCE_API_TEST:-ON} \
+        -DWITH_HIGH_LEVEL_API_TEST=${WITH_HIGH_LEVEL_API_TEST:-OFF} \
         -DINFERENCE_DEMO_INSTALL_DIR=${INFERENCE_DEMO_INSTALL_DIR} \
         -DWITH_ANAKIN=${WITH_ANAKIN:-OFF} \
         -DANAKIN_BUILD_FAT_BIN=${ANAKIN_BUILD_FAT_BIN:OFF}\
@@ -291,8 +293,12 @@ function build() {
     Building in /paddle/build ...
     ============================================
 EOF
+    parallel_number=`nproc`
+    if [[ "$1" != "" ]]; then
+      parallel_number=$1
+    fi
     make clean
-    make -j `nproc`
+    make -j ${parallel_number}
     make install -j `nproc`
 }
 
@@ -737,9 +743,13 @@ function gen_fluid_lib() {
     Generating fluid library for train and inference ...
     ========================================
 EOF
+    parallel_number=`nproc`
+    if [[ "$1" != "" ]]; then
+      parallel_number=$1
+    fi
     cmake .. -DWITH_DISTRIBUTE=OFF -DON_INFER=ON
-    make -j `nproc` fluid_lib_dist
-    make -j `nproc` inference_lib_dist
+    make -j ${parallel_number} fluid_lib_dist
+    make -j ${parallel_number} inference_lib_dist
 }
 
 function tar_fluid_lib() {
@@ -770,11 +780,22 @@ EOF
 
 function main() {
     local CMD=$1
+    local parallel_number=$2
     init
     case $CMD in
+      build_only)
+        cmake_gen ${PYTHON_ABI:-""}
+        build ${parallel_number}
+        ;;
+      build_and_check)
+        cmake_gen ${PYTHON_ABI:-""}
+        build ${parallel_number}
+        assert_api_not_changed ${PYTHON_ABI:-""}
+        assert_api_spec_approvals
+        ;;
       build)
         cmake_gen ${PYTHON_ABI:-""}
-        build
+        build ${parallel_number}
         gen_dockerfile ${PYTHON_ABI:-""}
         ;;
       test)
@@ -797,7 +818,7 @@ function main() {
         ;;
       fluid_inference_lib)
         cmake_gen ${PYTHON_ABI:-""}
-        gen_fluid_lib
+        gen_fluid_lib ${parallel_number}
         tar_fluid_lib
         test_fluid_lib
         ;;
@@ -806,16 +827,16 @@ function main() {
         ;;
       cicheck)
         cmake_gen ${PYTHON_ABI:-""}
-        build
+        build ${parallel_number}
         assert_api_not_changed ${PYTHON_ABI:-""}
         run_test
-        gen_fluid_lib
+        gen_fluid_lib ${parallel_number}
         test_fluid_lib
         assert_api_spec_approvals
         ;;
       cicheck_brpc)
         cmake_gen ${PYTHON_ABI:-""}
-        build
+        build ${parallel_number}
         run_brpc_test
         ;;
       assert_api)
@@ -823,7 +844,7 @@ function main() {
         assert_api_spec_approvals
         ;;
       test_inference)
-        gen_fluid_lib
+        gen_fluid_lib ${parallel_number}
         test_fluid_lib
         ;;
       assert_api_approvals)
@@ -840,7 +861,7 @@ function main() {
         ;;
       cicheck_py35)
         cmake_gen ${PYTHON_ABI:-""}
-        build
+        build ${parallel_number}
         run_test
         assert_api_not_changed ${PYTHON_ABI:-""}
         ;;
@@ -848,7 +869,7 @@ function main() {
         cmake_gen ${PYTHON_ABI:-""}
         ;;
       gen_fluid_lib)
-        gen_fluid_lib
+        gen_fluid_lib ${parallel_number}
         ;;
       test_fluid_lib)
         test_fluid_lib

python/paddle/fluid/__init__.py

@@ -66,6 +66,8 @@ from . import compiler
 from .compiler import *
 from paddle.fluid.layers.math_op_patch import monkey_patch_variable
 from . import install_check
+from .dygraph.nn import *
+from .dygraph.layers import *
 
 Tensor = LoDTensor
 

python/paddle/fluid/dataset.py

@@ -136,6 +136,7 @@ class DatasetBase(object):
             slot_var.name = var.name
             if var.lod_level == 0:
                 slot_var.is_dense = True
+                slot_var.shape.extend(var.shape)
             if var.dtype == core.VarDesc.VarType.FP32:
                 slot_var.type = "float"
             elif var.dtype == core.VarDesc.VarType.INT64:

python/paddle/fluid/dygraph/base.py

@@ -22,7 +22,7 @@ __all__ = ['enabled', 'guard', 'to_variable']
 
 
 def enabled():
-    return framework._in_dygraph_mode()
+    return framework.in_dygraph_mode()
 
 
 @signature_safe_contextmanager

python/paddle/fluid/dygraph/checkpoint.py

@@ -97,20 +97,12 @@ def load_persistables(vardict, dirname, filename=None):
 
     Examples:
         .. code-block:: python
-            my_layer = layer(fluid.dygraph.Layer)
+            my_layer = layer(fluid.Layer)
             param_path = "./my_paddle_model"
 
             param_dict = fluid.dygraph.load_persistables(my_layer.parameters(), param_path)
             param_1 = param_dict['PtbModel_0.w_1']
 
-            or:
-            my_layer = layer(fluid.dygraph.Layer)
-            param_path = "./my_paddle_model"
-            filename = "model.file"
-            param_dict = fluid.dygraph.load_persistables(my_layer.state_dict(), param_path,
-                                                                       filename=filename)
-            param_1 = param_dict['PtbModel_0.w_1']
-
         """
     if isinstance(vardict, collections.OrderedDict):
         return _load_var_from_file(vardict, dirname, filename)

python/paddle/fluid/dygraph/layer_object_helper.py

@@ -16,7 +16,7 @@ from __future__ import print_function
 
 import copy
 import six
-from ..framework import Parameter, _in_dygraph_mode
+from ..framework import Parameter, in_dygraph_mode
 from ..param_attr import ParamAttr
 from .. import core
 from six.moves import zip

python/paddle/fluid/dygraph/layers.py

@@ -139,14 +139,14 @@ class Layer(core.Layer):
 
     def clear_gradients(self):
         for p in self.parameters():
-            p._clear_gradient()
+            p.clear_gradient()
 
-    def _build_once(self, *args):
+    def build_once(self, *args):
         pass
 
     def __call__(self, *inputs):
         if not self._built:
-            self._build_once(*inputs)
+            self.build_once(*inputs)
 
         outputs = self.forward(*inputs)
         self._built = True

python/paddle/fluid/dygraph/nn.py

@@ -19,7 +19,7 @@ from six.moves import reduce
 from .. import core
 from ..layers import utils
 from . import layers
-from ..framework import Variable, _in_dygraph_mode, OpProtoHolder, Parameter
+from ..framework import Variable, in_dygraph_mode, OpProtoHolder, Parameter
 from ..param_attr import ParamAttr
 from ..initializer import Normal, Constant, NumpyArrayInitializer
 import numpy as np
@@ -33,6 +33,109 @@ __all__ = [
 
 
 class Conv2D(layers.Layer):
+    """
+    The convolution2D layer calculates the output based on the input, filter
+    and strides, paddings, dilations, groups parameters. Input and
+    Output are in NCHW format, where N is batch size, C is the number of
+    channels, H is the height of the feature, and W is the width of the feature.
+    Filter is in MCHW format, where M is the number of output image channels,
+    C is the number of input image channels, H is the height of the filter,
+    and W is the width of the filter. If the groups is greater than 1,
+    C will equal the number of input image channels divided by the groups.
+    Please refer to UFLDL's `convolution
+    <http://ufldl.stanford.edu/tutorial/supervised/FeatureExtractionUsingConvolution/>`_
+    for more detials.
+    If bias attribution and activation type are provided, bias is added to the
+    output of the convolution, and the corresponding activation function is
+    applied to the final result.
+
+    For each input :math:`X`, the equation is:
+
+    .. math::
+
+        Out = \sigma (W \\ast X + b)
+
+    Where:
+
+    * :math:`X`: Input value, a tensor with NCHW format.
+    * :math:`W`: Filter value, a tensor with MCHW format.
+    * :math:`\\ast`: Convolution operation.
+    * :math:`b`: Bias value, a 2-D tensor with shape [M, 1].
+    * :math:`\\sigma`: Activation function.
+    * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.
+
+    Example:
+
+        - Input:
+
+          Input shape: :math:`(N, C_{in}, H_{in}, W_{in})`
+
+          Filter shape: :math:`(C_{out}, C_{in}, H_f, W_f)`
+
+        - Output:
+
+          Output shape: :math:`(N, C_{out}, H_{out}, W_{out})`
+
+        Where
+
+        .. math::
+
+            H_{out}&= \\frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \\\\
+            W_{out}&= \\frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1
+
+    Args:
+        input (Variable): The input image with [N, C, H, W] format.
+        num_filters(int): The number of filter. It is as same as the output
+            image channel.
+        filter_size (int|tuple|None): The filter size. If filter_size is a tuple,
+            it must contain two integers, (filter_size_H, filter_size_W).
+            Otherwise, the filter will be a square.
+        stride (int|tuple): The stride size. If stride is a tuple, it must
+            contain two integers, (stride_H, stride_W). Otherwise, the
+            stride_H = stride_W = stride. Default: stride = 1.
+        padding (int|tuple): The padding size. If padding is a tuple, it must
+            contain two integers, (padding_H, padding_W). Otherwise, the
+            padding_H = padding_W = padding. Default: padding = 0.
+        dilation (int|tuple): The dilation size. If dilation is a tuple, it must
+            contain two integers, (dilation_H, dilation_W). Otherwise, the
+            dilation_H = dilation_W = dilation. Default: dilation = 1.
+        groups (int): The groups number of the Conv2d Layer. According to grouped
+            convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
+            the first half of the filters is only connected to the first half
+            of the input channels, while the second half of the filters is only
+            connected to the second half of the input channels. Default: groups=1.
+        param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights
+            of conv2d. If it is set to None or one attribute of ParamAttr, conv2d
+            will create ParamAttr as param_attr. If the Initializer of the param_attr
+            is not set, the parameter is initialized with :math:`Normal(0.0, std)`,
+            and the :math:`std` is :math:`(\\frac{2.0 }{filter\_elem\_num})^{0.5}`. Default: None.
+        bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of conv2d.
+            If it is set to False, no bias will be added to the output units.
+            If it is set to None or one attribute of ParamAttr, conv2d
+            will create ParamAttr as bias_attr. If the Initializer of the bias_attr
+            is not set, the bias is initialized zero. Default: None.
+        use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn
+            library is installed. Default: True
+        act (str): Activation type, if it is set to None, activation is not appended.
+            Default: None
+        name (str|None): A name for this layer(optional). If set None, the layer
+            will be named automatically. Default: None
+
+    Returns:
+        Variable: The tensor variable storing the convolution and \
+                  non-linearity activation result.
+
+    Raises:
+        ValueError: If the shapes of input, filter_size, stride, padding and
+                    groups mismatch.
+
+    Examples:
+        .. code-block:: python
+
+          data = fluid.layers.data(name='data', shape=[3, 32, 32], dtype='float32')
+          conv2d = fluid.layers.conv2d(input=data, num_filters=2, filter_size=3, act="relu")
+    """
+
     def __init__(self,
                  name_scope,
                  num_channels,
@@ -265,7 +368,7 @@ class Conv3D(layers.Layer):
         self._param_attr = param_attr
         self._bias_attr = bias_attr
 
-    def _build_once(self, input):
+    def build_once(self, input):
         num_channels = input.shape[1]
         self._dtype = self._helper.input_dtype(input)
 
@@ -332,6 +435,116 @@ class Conv3D(layers.Layer):
 
 
 class Conv3DTranspose(layers.Layer):
+    """
+    **Convlution3D transpose layer**
+
+    The convolution3D transpose layer calculates the output based on the input,
+    filter, and dilations, strides, paddings. Input(Input) and output(Output)
+    are in NCDHW format. Where N is batch size, C is the number of channels,
+    D is the depth of the feature, H is the height of the feature, and W
+    is the width of the feature. Parameters(dilations, strides, paddings) are
+    two elements. These two elements represent height and width, respectively.
+    The details of convolution transpose layer, please refer to the following
+    explanation and references `therein <http://www.matthewzeiler.com/wp-content/uploads/2017/07/cvpr2010.pdf>`_.
+    If bias attribution and activation type are provided, bias is added to
+    the output of the convolution, and the corresponding activation function
+    is applied to the final result.
+
+    For each input :math:`X`, the equation is:
+
+    .. math::
+
+        Out = \sigma (W \\ast X + b)
+
+    In the above equation:
+
+    * :math:`X`: Input value, a tensor with NCDHW format.
+    * :math:`W`: Filter value, a tensor with MCDHW format.
+    * :math:`\\ast`: Convolution operation.
+    * :math:`b`: Bias value, a 2-D tensor with shape [M, 1].
+    * :math:`\\sigma`: Activation function.
+    * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.
+
+    Example:
+
+        - Input:
+
+          Input shape: :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`
+
+          Filter shape: :math:`(C_{in}, C_{out}, D_f, H_f, W_f)`
+
+        - Output:
+
+          Output shape: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})`
+
+        Where
+
+        .. math::
+
+           D_{out} &= (D_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (D_f - 1) + 1 \\\\
+           H_{out} &= (H_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (H_f - 1) + 1 \\\\
+           W_{out} &= (W_{in} - 1) * strides[2] - 2 * paddings[2] + dilations[2] * (W_f - 1) + 1
+
+    Args:
+        input(Variable): The input image with [N, C, D, H, W] format.
+        num_filters(int): The number of the filter. It is as same as the output
+            image channel.
+        output_size(int|tuple|None): The output image size. If output size is a
+            tuple, it must contain three integers, (image_D, image_H, image_W). This
+            parameter only works when filter_size is None.
+        filter_size(int|tuple|None): The filter size. If filter_size is a tuple,
+            it must contain three integers, (filter_size_D, filter_size_H, filter_size_W).
+            Otherwise, the filter will be a square. None if use output size to
+            calculate filter_size.
+        padding(int|tuple): The padding size. If padding is a tuple, it must
+            contain three integers, (padding_D, padding_H, padding_W). Otherwise, the
+            padding_D = padding_H = padding_W = padding. Default: padding = 0.
+        stride(int|tuple): The stride size. If stride is a tuple, it must
+            contain three integers, (stride_D, stride_H, stride_W). Otherwise, the
+            stride_D = stride_H = stride_W = stride. Default: stride = 1.
+        dilation(int|tuple): The dilation size. If dilation is a tuple, it must
+            contain three integers, (dilation_D, dilation_H, dilation_W). Otherwise, the
+            dilation_D = dilation_H = dilation_W = dilation. Default: dilation = 1.
+        groups(int): The groups number of the Conv3d transpose layer. Inspired by
+            grouped convolution in Alex Krizhevsky's Deep CNN paper, in which
+            when group=2, the first half of the filters is only connected to the
+            first half of the input channels, while the second half of the
+            filters is only connected to the second half of the input channels.
+            Default: groups=1
+        param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights
+            of conv3d_transpose. If it is set to None or one attribute of ParamAttr, conv3d_transpose
+            will create ParamAttr as param_attr. If the Initializer of the param_attr
+            is not set, the parameter is initialized with Xavier. Default: None.
+        bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of conv3d_transpose.
+            If it is set to False, no bias will be added to the output units.
+            If it is set to None or one attribute of ParamAttr, conv3d_transpose
+            will create ParamAttr as bias_attr. If the Initializer of the bias_attr
+            is not set, the bias is initialized zero. Default: None.
+        use_cudnn(bool): Use cudnn kernel or not, it is valid only when the cudnn
+            library is installed. Default: True
+        act (str): Activation type, if it is set to None, activation is not appended.
+            Default: None.
+        name(str|None): A name for this layer(optional). If set None, the layer
+            will be named automatically.
+
+    Returns:
+        Variable: The tensor variable storing the convolution transpose result.
+
+    Raises:
+        ValueError: If the shapes of input, filter_size, stride, padding and
+                    groups mismatch.
+
+    Examples:
+       .. code-block:: python
+
+          conv3d_transpose = nn.Conv3DTranspose(
+                'Conv3DTranspose',
+                num_filters=12,
+                filter_size=12,
+                use_cudnn=False)
+          transpose_res = conv3d_transpose(base.to_variable(input_array))
+    """
+
     def __init__(self,
                  name_scope,
                  num_filters,
@@ -362,7 +575,7 @@ class Conv3DTranspose(layers.Layer):
         self._bias_attr = bias_attr
         self._act = act
 
-    def _build_once(self, input):
+    def build_once(self, input):
         self._dtype = self._helper.input_dtype(input)
         self._input_channel = input.shape[1]
 
@@ -436,6 +649,54 @@ class Conv3DTranspose(layers.Layer):
 
 
 class Pool2D(layers.Layer):
+    """
+    ${comment}
+
+    Args:
+        input (Variable): The input tensor of pooling operator. The format of
+                          input tensor is NCHW, where N is batch size, C is
+                          the number of channels, H is the height of the
+                          feature, and W is the width of the feature.
+        pool_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
+            it must contain two integers, (pool_size_Height, pool_size_Width).
+            Otherwise, the pool kernel size will be a square of an int.
+        pool_type: ${pooling_type_comment}
+        pool_stride (int|list|tuple): The pool stride size. If pool stride size is a tuple or list,
+            it must contain two integers, (pool_stride_Height, pool_stride_Width).
+            Otherwise, the pool stride size will be a square of an int.
+        pool_padding (int|list|tuple): The pool padding size. If pool padding size is a tuple,
+            it must contain two integers, (pool_padding_on_Height, pool_padding_on_Width).
+            Otherwise, the pool padding size will be a square of an int.
+        global_pooling (bool): ${global_pooling_comment}
+        use_cudnn (bool): ${use_cudnn_comment}
+        ceil_mode (bool): ${ceil_mode_comment}
+        name (str|None): A name for this layer(optional). If set None, the
+                        layer will be named automatically.
+        exclusive (bool): Whether to exclude padding points in average pooling
+                          mode, default is true
+
+    Returns:
+        Variable: The pooling result.
+
+    Raises:
+        ValueError: If 'pool_type' is not "max" nor "avg"
+        ValueError: If 'global_pooling' is False and 'pool_size' is -1
+        ValueError: If 'use_cudnn' is not a bool value.
+
+    Examples:
+
+        .. code-block:: python
+
+          data = fluid.layers.data(
+              name='data', shape=[3, 32, 32], dtype='float32')
+          pool2d = fluid.Pool2D("pool2d",pool_size=2,
+                            pool_type='max',
+                            pool_stride=1,
+                            global_pooling=False)
+
+          pool2d_res = pool2d(data)
+    """
+
     def __init__(self,
                  name_scope,
                  pool_size=-1,
@@ -495,6 +756,102 @@ class Pool2D(layers.Layer):
 
 
 class FC(layers.Layer):
+    """
+    **Fully Connected Layer**
+
+    This function creates a fully connected layer in the network. It can take
+    one or multiple tensors as its inputs(input can be a list of Variable, see
+    Args in detail). It creates a variable called weights for each input tensor,
+    which represents a fully connected weight matrix from each input unit to
+    each output unit. The fully connected layer multiplies each input tensor
+    with its corresponding weight to produce an output Tensor with shape [M, `size`],
+    where M is batch size. If multiple input tensors are given, the results of
+    multiple output tensors with shape [M, `size`] will be summed up. If bias_attr
+    is not None, a bias variable will be created and added to the output.
+    Finally, if activation is not None, it will be applied to the output as well.
+
+    When the input is single tensor:
+
+    .. math::
+
+        Out = Act({XW + b})
+
+    When the input are multiple tensors:
+
+    .. math::
+
+        Out = Act({\sum_{i=0}^{N-1}X_iW_i + b})
+
+    In the above equation:
+
+    * :math:`N`: Number of the input. N equals to len(input) if input is list of Variable.
+    * :math:`X_i`: The i-th input tensor.
+    * :math:`W_i`: The i-th weights matrix corresponding i-th input tensor.
+    * :math:`b`: The bias parameter created by this layer (if needed).
+    * :math:`Act`: The activation function.
+    * :math:`Out`: The output tensor.
+
+    See below for an example.
+
+    .. code-block:: text
+
+        Given:
+            data_1.data = [[[0.1, 0.2],
+                           [0.3, 0.4]]]
+            data_1.shape = (1, 2, 2) # 1 is batch_size
+
+            data_2 = [[[0.1, 0.2, 0.3]]]
+            data_2.shape = (1, 1, 3)
+
+            out = fluid.layers.fc(input=[data_1, data_2], size=2)
+
+        Then:
+            out.data = [[0.18669507, 0.1893476]]
+            out.shape = (1, 2)
+
+    Args:
+        input (Variable|list of Variable): The input tensor(s) of this layer, and the dimension of
+            the input tensor(s) is at least 2.
+        size(int): The number of output units in this layer.
+        num_flatten_dims (int, default 1): The fc layer can accept an input tensor with more than
+            two dimensions. If this happens, the multidimensional tensor will first be flattened
+            into a 2-dimensional matrix. The parameter `num_flatten_dims` determines how the input
+            tensor is flattened: the first `num_flatten_dims` (inclusive, index starts from 1)
+            dimensions will be flatten to form the first dimension of the final matrix (height of
+            the matrix), and the rest `rank(X) - num_flatten_dims` dimensions are flattened to
+            form the second dimension of the final matrix (width of the matrix). For example, suppose
+            `X` is a 5-dimensional tensor with a shape [2, 3, 4, 5, 6], and `num_flatten_dims` = 3.
+            Then, the flattened matrix will have a shape [2 x 3 x 4, 5 x 6] = [24, 30].
+        param_attr (ParamAttr|list of ParamAttr, default None): The parameter attribute for learnable
+            parameters/weights of this layer.
+        bias_attr (ParamAttr|list of ParamAttr, default None): The parameter attribute for the bias
+            of this layer. If it is set to False, no bias will be added to the output units.
+            If it is set to None, the bias is initialized zero. Default: None.
+        act (str, default None): Activation to be applied to the output of this layer.
+        is_test(bool): A flag indicating whether execution is in test phase.
+        name (str, default None): The name of this layer.
+
+    Returns:
+        Variable: The transformation result.
+
+    Raises:
+        ValueError: If rank of the input tensor is less than 2.
+
+    Examples:
+        .. code-block:: python
+
+          # when input is single tensor
+          data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
+          fc = fluid.FC("fc", size=1000, act="tanh")
+          fc_res = fc(data)
+
+          # when input are multiple tensors
+          data_1 = fluid.layers.data(name="data_1", shape=[32, 32], dtype="float32")
+          data_2 = fluid.layers.data(name="data_2", shape=[24, 36], dtype="float32")
+          fc = fluid.FC("fc", size=1000, act="tanh")
+          fc_res = fc([data_1, data_2])
+    """
+
     def __init__(self,
                  name_scope,
                  size,
@@ -522,7 +879,7 @@ class FC(layers.Layer):
         assert isinstance(value, Parameter)
         self.__w[i] = value
 
-    def _build_once(self, input):
+    def build_once(self, input):
         i = 0
         for inp, param in self._helper.iter_inputs_and_params(input,
                                                               self._param_attr):
@@ -591,6 +948,91 @@ class FC(layers.Layer):
 
 
 class BatchNorm(layers.Layer):
+    """
+    **Batch Normalization Layer**
+
+    Can be used as a normalizer function for conv2d and fully_connected operations.
+    The required data format for this layer is one of the following:
+
+    1. NHWC `[batch, in_height, in_width, in_channels]`
+
+    2. NCHW `[batch, in_channels, in_height, in_width]`
+
+    Refer to `Batch Normalization: Accelerating Deep Network Training by Reducing
+    Internal Covariate Shift <https://arxiv.org/pdf/1502.03167.pdf>`_
+    for more details.
+
+    :math:`input` is the input features over a mini-batch.
+
+    ..  math::
+
+        \\mu_{\\beta} &\\gets \\frac{1}{m} \\sum_{i=1}^{m} x_i \\qquad &//\\
+        \ mini-batch\ mean \\\\
+        \\sigma_{\\beta}^{2} &\\gets \\frac{1}{m} \\sum_{i=1}^{m}(x_i - \\
+        \\mu_{\\beta})^2 \\qquad &//\ mini-batch\ variance \\\\
+        \\hat{x_i} &\\gets \\frac{x_i - \\mu_\\beta} {\\sqrt{\\
+        \\sigma_{\\beta}^{2} + \\epsilon}} \\qquad &//\ normalize \\\\
+        y_i &\\gets \\gamma \\hat{x_i} + \\beta \\qquad &//\ scale\ and\ shift
+
+
+    When use_global_stats = True, the :math:`\\mu_{\\beta}`
+    and :math:`\\sigma_{\\beta}^{2}` are not the statistics of one mini-batch.
+    They are global (or running) statistics. (It usually got from the
+    pre-trained model.)
+    The training and testing (or inference) have the same behavior:
+
+    ..  math::
+
+        \\hat{x_i} &\\gets \\frac{x_i - \\mu_\\beta} {\\sqrt{\\
+        \\sigma_{\\beta}^{2} + \\epsilon}}  \\\\
+        y_i &\\gets \\gamma \\hat{x_i} + \\beta
+
+    Args:
+        input(variable): The rank of input variable can be 2, 3, 4, 5.
+        act(string, Default None): Activation type, linear|relu|prelu|...
+        is_test (bool, Default False): A flag indicating whether it is in
+            test phrase or not.
+        momentum(float, Default 0.9): The value used for the moving_mean and
+            moving_var computation. The updated formula is:
+            :math:`moving\_mean = moving\_mean * momentum + new\_mean * (1. - momentum)`
+            :math:`moving\_var = moving\_var * momentum + new\_var * (1. - momentum)`
+            Default is 0.9.
+        epsilon(float, Default 1e-05): A value added to the denominator for
+            numerical stability. Default is 1e-5.
+        param_attr(ParamAttr|None): The parameter attribute for Parameter `scale`
+             of batch_norm. If it is set to None or one attribute of ParamAttr, batch_norm
+             will create ParamAttr as param_attr. If the Initializer of the param_attr
+             is not set, the parameter is initialized with Xavier. Default: None.
+        bias_attr(ParamAttr|None): The parameter attribute for the bias of batch_norm.
+             If it is set to None or one attribute of ParamAttr, batch_norm
+             will create ParamAttr as bias_attr. If the Initializer of the bias_attr
+             is not set, the bias is initialized zero. Default: None.
+        data_layout(string, default NCHW): NCHW|NHWC
+        in_place(bool, Default False): Make the input and output of batch norm reuse memory.
+        name(string, Default None): A name for this layer(optional). If set None, the layer
+            will be named automatically.
+        moving_mean_name(string, Default None): The name of moving_mean which store the global Mean.
+        moving_variance_name(string, Default None): The name of the moving_variance which store the global Variance.
+        do_model_average_for_mean_and_var(bool, Default False): Do model average for mean and variance or not.
+        fuse_with_relu (bool): if True, this OP performs relu after batch norm.
+        use_global_stats(bool, Default False): Whether to use global mean and
+            variance. In inference or test mode, set use_global_stats to true
+            or is_test to true, and the behavior is equivalent.
+            In train mode, when setting use_global_stats True, the global mean
+            and variance are also used during train period.
+
+    Returns:
+        Variable: A tensor variable which is the result after applying batch normalization on the input.
+
+    Examples:
+
+        .. code-block:: python
+            fc = fluid.FC('fc', size=200, param_attr='fc1.w')
+            hidden1 = fc(x)
+            batch_norm = fluid.BatchNorm("batch_norm", 10)
+            hidden2 = batch_norm(hidden1)
+    """
+
     def __init__(self,
                  name_scope,
                  num_channels,
@@ -629,7 +1071,7 @@ class BatchNorm(layers.Layer):
             dtype=self._dtype,
             default_initializer=Constant(1.0))
         if use_global_stats and self._param_attr.learning_rate == 0.:
-            self._scale._stop_gradient = True
+            self._scale.stop_gradient = True
 
         self._bias = self.create_parameter(
             attr=self._param_attr,
@@ -637,7 +1079,7 @@ class BatchNorm(layers.Layer):
             dtype=self._dtype,
             is_bias=True)
         if use_global_stats and self._param_attr.learning_rate == 0.:
-            self._bias._stop_gradient = True
+            self._bias.stop_gradient = True
 
         self._mean = self.create_parameter(
             attr=ParamAttr(
@@ -647,7 +1089,7 @@ class BatchNorm(layers.Layer):
                 do_model_average=do_model_average_for_mean_and_var),
             shape=param_shape,
             dtype=self._dtype)
-        self._mean._stop_gradient = True
+        self._mean.stop_gradient = True
 
         self._variance = self.create_parameter(
             attr=ParamAttr(
@@ -657,7 +1099,7 @@ class BatchNorm(layers.Layer):
                 do_model_average=do_model_average_for_mean_and_var),
             shape=param_shape,
             dtype=self._dtype)
-        self._variance._stop_gradient = True
+        self._variance.stop_gradient = True
 
         self._in_place = in_place
         self._momentum = momentum
@@ -666,7 +1108,7 @@ class BatchNorm(layers.Layer):
         self._fuse_with_relu = fuse_with_relu
         self._use_global_stats = use_global_stats
 
-    def _build_once(self, input):
+    def build_once(self, input):
         pass
 
     def forward(self, input):
@@ -747,7 +1189,7 @@ class Embedding(layers.Layer):
 
           dict_size = len(dataset.ids)
           input = fluid.layers.data(name='ids', shape=[32, 32], dtype='float32')
-          embedding = fluid.dygraph.Embedding(size=[dict_size, 16])
+          embedding = fluid.Embedding(size=[dict_size, 16])
           fc = embedding(input)
     """
 
@@ -797,70 +1239,70 @@ class Embedding(layers.Layer):
 
 
 class LayerNorm(layers.Layer):
-    def __init__(self,
-                 name_scope,
-                 scale=True,
-                 shift=True,
-                 begin_norm_axis=1,
-                 epsilon=1e-05,
-                 param_attr=None,
-                 bias_attr=None,
-                 act=None):
-        """
-        ${comment}
+    """
+    ${comment}
 
-        The formula is as follows:
+    The formula is as follows:
 
-        ..  math::
+    ..  math::
 
-            \\mu & = \\frac{1}{H}\\sum_{i=1}^{H} a_i
+        \\mu & = \\frac{1}{H}\\sum_{i=1}^{H} a_i
 
-            \\sigma & = \\sqrt{\\frac{1}{H}\sum_{i=1}^{H}(a_i - \\mu)^2}
+        \\sigma & = \\sqrt{\\frac{1}{H}\sum_{i=1}^{H}(a_i - \\mu)^2}
 
-            h & = f(\\frac{g}{\\sigma}(a - \\mu) + b)
+        h & = f(\\frac{g}{\\sigma}(a - \\mu) + b)
 
-        * :math:`a`: the vector representation of the summed inputs to the neurons
-        in that layer.
+    * :math:`a`: the vector representation of the summed inputs to the neurons
+    in that layer.
 
-        * :math:`H`: the number of hidden units in a layers
+    * :math:`H`: the number of hidden units in a layers
 
-        * :math:`g`: the trainable scale parameter.
+    * :math:`g`: the trainable scale parameter.
 
-        * :math:`b`: the trainable bias parameter.
+    * :math:`b`: the trainable bias parameter.
 
-        Args:
-            input(Variable): The input tensor variable.
-            scale(bool): Whether to learn the adaptive gain :math:`g` after
-                normalization. Default True.
-            shift(bool): Whether to learn the adaptive bias :math:`b` after
-                normalization. Default True.
-            begin_norm_axis(int): The normalization will be performed along
-                dimensions from :attr:`begin_norm_axis` to :attr:`rank(input)`.
-                Default 1.
-            epsilon(float): The small value added to the variance to prevent
-                division by zero. Default 1e-05.
-            param_attr(ParamAttr|None): The parameter attribute for the learnable
-                gain :math:`g`. If :attr:`scale` is False, :attr:`param_attr` is
-                omitted. If :attr:`scale` is True and :attr:`param_attr` is None,
-                a default :code:`ParamAttr` would be added as scale. The
-                :attr:`param_attr` is initialized as 1 if it is added. Default None.
-            bias_attr(ParamAttr|None): The parameter attribute for the learnable
-                bias :math:`b`. If :attr:`shift` is False, :attr:`bias_attr` is
-                omitted. If :attr:`shift` is True and :attr:`param_attr` is None,
-                a default :code:`ParamAttr` would be added as bias. The
-                :attr:`bias_attr` is initialized as 0 if it is added. Default None.
-            act(str): Activation to be applied to the output of layer normalizaiton.
-                      Default None.
-        Returns:
-            ${y_comment}
+    Args:
+        input(Variable): The input tensor variable.
+        scale(bool): Whether to learn the adaptive gain :math:`g` after
+            normalization. Default True.
+        shift(bool): Whether to learn the adaptive bias :math:`b` after
+            normalization. Default True.
+        begin_norm_axis(int): The normalization will be performed along
+            dimensions from :attr:`begin_norm_axis` to :attr:`rank(input)`.
+            Default 1.
+        epsilon(float): The small value added to the variance to prevent
+            division by zero. Default 1e-05.
+        param_attr(ParamAttr|None): The parameter attribute for the learnable
+            gain :math:`g`. If :attr:`scale` is False, :attr:`param_attr` is
+            omitted. If :attr:`scale` is True and :attr:`param_attr` is None,
+            a default :code:`ParamAttr` would be added as scale. The
+            :attr:`param_attr` is initialized as 1 if it is added. Default None.
+        bias_attr(ParamAttr|None): The parameter attribute for the learnable
+            bias :math:`b`. If :attr:`shift` is False, :attr:`bias_attr` is
+            omitted. If :attr:`shift` is True and :attr:`param_attr` is None,
+            a default :code:`ParamAttr` would be added as bias. The
+            :attr:`bias_attr` is initialized as 0 if it is added. Default None.
+        act(str): Activation to be applied to the output of layer normalizaiton.
+                  Default None.
+    Returns:
+        ${y_comment}
 
-        Examples:
+    Examples:
 
-            >>> data = fluid.layers.data(name='data', shape=[3, 32, 32],
-            >>>                          dtype='float32')
-            >>> x = fluid.layers.layer_norm(input=data, begin_norm_axis=1)
-        """
+        >>> data = fluid.layers.data(name='data', shape=[3, 32, 32],
+        >>>                          dtype='float32')
+        >>> x = fluid.layers.layer_norm(input=data, begin_norm_axis=1)
+    """
 
+    def __init__(self,
+                 name_scope,
+                 scale=True,
+                 shift=True,
+                 begin_norm_axis=1,
+                 epsilon=1e-05,
+                 param_attr=None,
+                 bias_attr=None,
+                 act=None):
         super(LayerNorm, self).__init__(name_scope)
         self._scale = scale
         self._shift = shift
@@ -870,7 +1312,7 @@ class LayerNorm(layers.Layer):
         self._bias_attr = bias_attr
         self._act = act
 
-    def _build_once(self, input):
+    def build_once(self, input):
         self._dtype = self._helper.input_dtype(input)
         input_shape = input.shape
         param_shape = [
@@ -1232,7 +1674,7 @@ class NCE(layers.Layer):
             'remote_prefetch': remote_prefetch
         }
 
-    def _build_once(self, input, label, sample_weight=None):
+    def build_once(self, input, label, sample_weight=None):
         assert isinstance(input, Variable)
         assert isinstance(label, Variable)
 
@@ -1318,7 +1760,7 @@ class PRelu(layers.Layer):
             raise ValueError('mode should be one of all, channel, element.')
         self._alpha_shape = [1]
 
-    def _build_once(self, input):
+    def build_once(self, input):
         if self._mode == 'channel':
             self._alpha_shape = [1, input.shape[1], 1, 1]
         elif self._mode == 'element':
@@ -1396,7 +1838,7 @@ class BilinearTensorProduct(layers.Layer):
         self._name = name
         self._inputs = dict()
 
-    def _build_once(self, x, y):
+    def build_once(self, x, y):
         self._dtype = self._helper.input_dtype(x)
 
         param_shape = [self._size, x.shape[1], y.shape[1]]
@@ -1572,7 +2014,7 @@ class Conv2DTranspose(layers.Layer):
         self._output_size = output_size
         self._op_type = 'conv2d_transpose'
 
-    def _build_once(self, input):
+    def build_once(self, input):
         input_channel = input.shape[1]
         if (input_channel == self._groups and
                 self._num_filters == input_channel and not self._use_cudnn):
@@ -1686,7 +2128,7 @@ class SequenceConv(layers.Layer):
                  bias_attr=None,
                  param_attr=None,
                  act=None):
-        assert not _in_dygraph_mode(
+        assert not in_dygraph_mode(
         ), "SequenceConv is not supported by dynamic graph mode yet!"
         super(SequenceConv, self).__init__(name_scope)
         self._num_filters = num_filters
@@ -1696,7 +2138,7 @@ class SequenceConv(layers.Layer):
         self._bias_attr = bias_attr
         self._param_attr = param_attr
 
-    def _build_once(self, input):
+    def build_once(self, input):
         self._dtype = self._helper.input_dtype(input)
         filter_shape = [self._filter_size * input.shape[1], self._num_filters]
         self._filter_param = self.create_parameter(
@@ -1726,14 +2168,14 @@ class RowConv(layers.Layer):
                  future_context_size,
                  param_attr=None,
                  act=None):
-        assert not _in_dygraph_mode(
+        assert not in_dygraph_mode(
         ), "RowConv is not supported by dynamic graph mode yet!"
         super(RowConv, self).__init__(name_scope)
         self._act = act
         self._param_attr = param_attr
         self._future_context_size = future_context_size
 
-    def _build_once(self, input):
+    def build_once(self, input):
         self._dtype = self._helper.input_dtype(input)
         filter_shape = [self._future_context_size + 1, input.shape[1]]
         self._filter_param = self.create_parameter(
@@ -1796,7 +2238,7 @@ class GroupNorm(layers.Layer):
         if data_layout != 'NCHW':
             raise ValueError("unsupported data layout:" + data_layout)
 
-    def _build_once(self, input):
+    def build_once(self, input):
         self._dtype = self._helper.input_dtype(input)
         param_shape = [input.shape[1]]
         if self._bias_attr:
@@ -1849,7 +2291,7 @@ class SpectralNorm(layers.Layer):
         self._eps = eps
         self._dim = dim
 
-    def _build_once(self, weight):
+    def build_once(self, weight):
         self._dtype = self._helper.input_dtype(weight)
         input_shape = weight.shape
         h = input_shape[self._dim]
@@ -1904,7 +2346,7 @@ class TreeConv(layers.Layer):
         self._bias_attr = bias_attr
         self._param_attr = param_attr
 
-    def _build_once(self, nodes_vector, edge_set):
+    def build_once(self, nodes_vector, edge_set):
         assert isinstance(nodes_vector, Variable)
         assert isinstance(edge_set, Variable)
         self._dtype = self._helper.input_dtype(nodes_vector)

python/paddle/fluid/executor.py

@@ -712,10 +712,6 @@ class Executor(object):
         if dataset == None:
             raise RuntimeError("dataset is needed and should be initialized")
 
-        if not isinstance(self.place, core.CPUPlace):
-            raise RuntimeError("infer_from_dataset is verified on CPUPlace"
-                               "We will open CUDAPlace in the future")
-
         scope, trainer = self._prepare_trainer(
             program=program,
             dataset=dataset,
@@ -796,10 +792,6 @@ class Executor(object):
         if dataset == None:
             raise RuntimeError("dataset is need and should be initialized")
 
-        if not isinstance(self.place, core.CPUPlace):
-            raise RuntimeError("train_from_dataset is verified on CPUPlace"
-                               "We will open CUDAPlace in the future")
-
         scope, trainer = self._prepare_trainer(
             program=program,
             dataset=dataset,

python/paddle/fluid/framework.py

@@ -67,6 +67,7 @@ __all__ = [
     'cuda_places',
     'cpu_places',
     'cuda_pinned_places',
+    'in_dygraph_mode',
 ]
 
 EMPTY_VAR_NAME = core.kEmptyVarName()
@@ -79,7 +80,10 @@ _dygraph_tracer_ = None
 _dygraph_current_expected_place_ = None
 
 
-def _in_dygraph_mode():
+def in_dygraph_mode():
+    '''
+    Returns(bool): True if the program is running in dynamic graph mode
+    '''
     return _dygraph_tracer_ is not None
 
 
@@ -396,7 +400,7 @@ class Variable(object):
             if not isinstance(dtype, core.VarDesc.VarType):
                 dtype = convert_np_dtype_to_dtype_(dtype)
 
-        if _in_dygraph_mode():
+        if in_dygraph_mode():
             # record vars in tracer rather than blocks
             self._ivar = kwargs.get("ivar", None)
             if not self._ivar:
@@ -482,21 +486,21 @@ class Variable(object):
 
             self.block.vars[name] = self
             self.op = None
-            self.stop_gradient = stop_gradient
+            self._stop_gradient = stop_gradient
             self.is_data = is_data
 
-    def _numpy(self):
+    def numpy(self):
         new_ivar = self._ivar._copy_to(core.CPUPlace(), True)
         return np.array(new_ivar.value().get_tensor())
 
-    def _backward(self):
+    def backward(self):
         self._ivar._run_backward()
 
-    def _gradient(self):
+    def gradient(self):
         new_ivar = self._ivar._grad_ivar()._copy_to(core.CPUPlace(), True)
         return np.array(new_ivar.value().get_tensor())
 
-    def _clear_gradient(self):
+    def clear_gradient(self):
         self._ivar._clear_gradient()
 
     def __str__(self):
@@ -516,7 +520,7 @@ class Variable(object):
         Returns:
             str: The debug string.
         """
-        if _in_dygraph_mode():
+        if in_dygraph_mode():
             # TODO(panyx0718): add more dygraph debug info.
             return 'name %s, dtype: %s shape: %s' % (self.name, self.dtype,
                                                      self.shape)
@@ -535,7 +539,7 @@ class Variable(object):
 
     __repr__ = __str__
 
-    def _set_desc(self, input):
+    def set_desc(self, input):
         """
         Set the variable description.
 
@@ -548,43 +552,43 @@ class Variable(object):
         self.desc = input
 
     @property
-    def _stop_gradient(self):
-        if _in_dygraph_mode():
+    def stop_gradient(self):
+        if in_dygraph_mode():
             return self._ivar.stop_gradient
         else:
-            return self.stop_gradient
+            return self._stop_gradient
 
-    @_stop_gradient.setter
-    def _stop_gradient(self, s):
-        if _in_dygraph_mode():
+    @stop_gradient.setter
+    def stop_gradient(self, s):
+        if in_dygraph_mode():
             self._ivar.stop_gradient = s
         else:
-            self.stop_gradient = s
+            self._stop_gradient = s
 
     @property
     def persistable(self):
-        if _in_dygraph_mode():
+        if in_dygraph_mode():
             return self._ivar.persistable
         else:
             return self.desc.persistable()
 
     @persistable.setter
     def persistable(self, p):
-        if _in_dygraph_mode():
+        if in_dygraph_mode():
             return self._ivar.persistable
         else:
             self.desc.set_persistable(p)
 
     @property
     def name(self):
-        if _in_dygraph_mode():
+        if in_dygraph_mode():
             return self._ivar.name
         else:
             return cpt.to_text(self.desc.name())
 
     @name.setter
     def name(self, new_name):
-        if _in_dygraph_mode():
+        if in_dygraph_mode():
             self._ivar.name = new_name
         else:
             self.desc.set_name(new_name)
@@ -592,14 +596,14 @@ class Variable(object):
     @property
     def shape(self):
         # convert to tuple, make it as same as numpy API.
-        if _in_dygraph_mode():
+        if in_dygraph_mode():
             return self._ivar.shape
         else:
             return tuple(self.desc.shape())
 
     @property
     def dtype(self):
-        if _in_dygraph_mode():
+        if in_dygraph_mode():
             return self._ivar.dtype
         else:
             return self.desc.dtype()
@@ -611,7 +615,7 @@ class Variable(object):
 
     @property
     def type(self):
-        if _in_dygraph_mode():
+        if in_dygraph_mode():
             return self._ivar.dtype
         else:
             return self.desc.type()
@@ -721,7 +725,7 @@ class Variable(object):
                 name=unique_name.generate(".".join(self.name)),
                 dtype=self.dtype,
                 persistable=self.persistable,
-                stop_gradient=self._stop_gradient, )
+                stop_gradient=self.stop_gradient, )
         else:
             return self
 
@@ -930,7 +934,7 @@ class Operator(object):
                  inputs=None,
                  outputs=None,
                  attrs=None):
-        if _in_dygraph_mode():
+        if in_dygraph_mode():
             if type is None:
                 raise ValueError(
                     "`type` to initialized an Operator can not be None.")
@@ -1049,7 +1053,7 @@ class Operator(object):
                     for arg in out_args:
                         out_arg_names.append(cpt.to_text(arg.name))
                         # TODO(minqiyang): could we remove variable's op in static mode?
-                        if not _in_dygraph_mode():
+                        if not in_dygraph_mode():
                             arg.op = self
                     self.desc.set_output(out_proto.name, out_arg_names)
 
@@ -1095,7 +1099,7 @@ class Operator(object):
 
     @property
     def type(self):
-        if _in_dygraph_mode():
+        if in_dygraph_mode():
             return self.iop.type
         else:
             return self.desc.type()
@@ -1638,7 +1642,7 @@ class Block(object):
         Returns:
             Operator: the append Operator.
         """
-        if _in_dygraph_mode():
+        if in_dygraph_mode():
             op = Operator(
                 block=self,
                 desc=None,
@@ -1710,7 +1714,7 @@ class Block(object):
         return self.ops[start:end]
 
     def _prepend_op(self, *args, **kwargs):
-        if _in_dygraph_mode():
+        if in_dygraph_mode():
             op = Operator(
                 self,
                 None,

python/paddle/fluid/initializer.py

@@ -165,7 +165,7 @@ class ConstantInitializer(Initializer):
                 'force_cpu': self._force_cpu or force_init_on_cpu()
             },
             stop_gradient=True)
-        if not framework._in_dygraph_mode():
+        if not framework.in_dygraph_mode():
             var.op = op
         return op
 
@@ -245,7 +245,7 @@ class UniformInitializer(Initializer):
                 attrs={"in_dtype": out_var.dtype,
                        "out_dtype": var.dtype})
 
-        if not framework._in_dygraph_mode():
+        if not framework.in_dygraph_mode():
             var.op = op
         return op
 
@@ -324,7 +324,7 @@ class NormalInitializer(Initializer):
                 outputs={"Out": var},
                 attrs={"in_dtype": out_var.dtype,
                        "out_dtype": var.dtype})
-        if not framework._in_dygraph_mode():
+        if not framework.in_dygraph_mode():
             var.op = op
         return op
 
@@ -403,7 +403,7 @@ class TruncatedNormalInitializer(Initializer):
                 outputs={"Out": var},
                 attrs={"in_dtype": out_var.dtype,
                        "out_dtype": var.dtype})
-        if not framework._in_dygraph_mode():
+        if not framework.in_dygraph_mode():
             var.op = op
         return op
 
@@ -509,7 +509,7 @@ class XavierInitializer(Initializer):
                     "seed": self._seed
                 },
                 stop_gradient=True)
-        if not framework._in_dygraph_mode():
+        if not framework.in_dygraph_mode():
             var.op = op
         return op
 
@@ -610,7 +610,7 @@ class MSRAInitializer(Initializer):
                     "seed": self._seed
                 },
                 stop_gradient=True)
-        if not framework._in_dygraph_mode():
+        if not framework.in_dygraph_mode():
             var.op = op
         return op
 
@@ -709,7 +709,7 @@ class BilinearInitializer(Initializer):
                 'shape': list(shape),
                 value_name: values
             })
-        if not framework._in_dygraph_mode():
+        if not framework.in_dygraph_mode():
             var.op = op
         return op
 
@@ -768,7 +768,7 @@ class NumpyArrayInitializer(Initializer):
                 value_name: values
             },
             stop_gradient=True)
-        if not framework._in_dygraph_mode():
+        if not framework.in_dygraph_mode():
             var.op = op
         return op
 

python/paddle/fluid/layer_helper.py

@@ -17,7 +17,7 @@ from __future__ import print_function
 import copy
 import six
 
-from .framework import Parameter, dtype_is_floating, _in_dygraph_mode
+from .framework import Parameter, dtype_is_floating, in_dygraph_mode
 from . import unique_name
 from paddle.fluid.initializer import Constant, Xavier
 from .param_attr import ParamAttr

python/paddle/fluid/layer_helper_base.py

@@ -17,7 +17,7 @@ from __future__ import print_function
 import copy
 import numpy as np
 
-from .framework import Variable, default_main_program, default_startup_program, _in_dygraph_mode, _current_expected_place
+from .framework import Variable, default_main_program, default_startup_program, in_dygraph_mode, _current_expected_place
 from . import unique_name
 from .param_attr import ParamAttr, WeightNormParamAttr
 from . import core
@@ -54,7 +54,7 @@ class LayerHelperBase(object):
         Return Variable construct from value
         """
         if isinstance(value, np.ndarray):
-            assert _in_dygraph_mode(
+            assert in_dygraph_mode(
             ), "to_variable could only be called in dygraph mode"
 
             if not block:
@@ -302,7 +302,7 @@ class LayerHelperBase(object):
             param = self._create_weight_normalize(attr, shape, dtype)
             WeightNormParamAttr.params_with_weight_norm.append(param)
             return param
-        if _in_dygraph_mode():
+        if in_dygraph_mode():
             # In dygraph mode, we want the returned parameter to be
             # initialized so that it can be used imperatively.
             return self.main_program.global_block().create_parameter(
@@ -370,7 +370,7 @@ class LayerHelperBase(object):
                initializer: initializer to use
         """
         assert isinstance(var, Variable)
-        if _in_dygraph_mode():
+        if in_dygraph_mode():
             initializer(var, var.block)
         else:
             self.startup_program.global_block().create_var(

python/paddle/fluid/layers/learning_rate_scheduler.py

@@ -35,8 +35,8 @@ from ..dygraph import learning_rate_scheduler as imperate_lr
 
 __all__ = [
     'exponential_decay', 'natural_exp_decay', 'inverse_time_decay',
-    'polynomial_decay', 'piecewise_decay', 'noam_decay', 'append_LARS',
-    'cosine_decay', 'linear_lr_warmup'
+    'polynomial_decay', 'piecewise_decay', 'noam_decay', 'cosine_decay',
+    'linear_lr_warmup'
 ]
 
 
@@ -349,24 +349,26 @@ def cosine_decay(learning_rate, step_each_epoch, epochs):
     training progresses. By using this function, the learning rate will be decayed by
     following cosine decay strategy.
 
-    decayed_lr = learning_rate * 0.5 * (math.cos(epoch * math.pi / epochs) + 1)
+    .. math::
+
+	decayed\_lr = learning\_rate * 0.5 * (math.cos * (epoch * \\frac{math.pi}{epochs} ) + 1)
     
     Args:
         learning_rate(Variable|float): The initial learning rate.
         step_each_epoch(int): the number of steps in an epoch.
         epochs(int): the number of epochs.
 
-     Returns:
-        Variable: The decayed learning rate.
-
-     Examples:
+    Returns:
+	Variable: The decayed learning rate.
 
-    ..code-block:: python
+    Examples:
+	.. code-block:: python
 
-  	base_lr = 0.1
-	lr = fluid.layers.cosine_decay(
-	learning_rate = base_lr, step_each_epoch=10000, epochs=120)
+  	    base_lr = 0.1
+	    lr = fluid.layers.cosine_decay(
+	    learning_rate = base_lr, step_each_epoch=10000, epochs=120)
     """
+
     with default_main_program()._lr_schedule_guard():
         if imperative_base.enabled():
             decay = imperate_lr.CosineDecay(learning_rate, step_each_epoch,
@@ -381,50 +383,6 @@ def cosine_decay(learning_rate, step_each_epoch, epochs):
             return decayed_lr
 
 
-def append_LARS(params_grads, learning_rate, weight_decay):
-    """
-    Applies LARS (LAYER-WISE ADAPTIVE RATE SCALING) to learning rate for
-    each layer.
-
-    Args:
-        learning_rate: A learning rate Variable. This
-          is the global learning rate for LARS.
-        weight_decay: A Python `float` number.
-
-    Returns:
-        The decayed learning rate
-    Examples:
-        .. code-block:: python
-
-            learning_rate *= local_gw_ratio * sqrt(sumsq(param))
-                        / (sqrt(sumsq(gradient))+ weight_decay * sqrt(sumsq(param)))
-    """
-
-    assert not imperative_base.enabled(
-    ), "append_LARS is NOT supported in dygraph mode now"
-
-    def _balanced_weight(param_norm, grad_norm):
-        if weight_decay == 1.0:
-            return grad_norm + param_norm
-        else:
-            return grad_norm + weight_decay * param_norm
-
-    for param, grad in params_grads:
-        with param.block.program.optimized_guard(
-            [param, grad]), name_scope("optimizer"):
-            param_lr = param.optimize_attr['learning_rate']
-            param_norm = ops.sqrt(nn.reduce_sum(input=ops.square(param)))
-            grad_norm = ops.sqrt(nn.reduce_sum(input=ops.square(grad)))
-            if type(param_lr) == float and param_lr == 1.0:
-                decayed_lr = learning_rate * param_norm \
-                    / _balanced_weight(param_norm, grad_norm)
-            else:
-                decayed_lr = learning_rate * param_lr * param_norm \
-                    / _balanced_weight(param_norm, grad_norm)
-            # set back param local learning rate
-            param.optimize_attr['learning_rate'] = decayed_lr
-
-
 def linear_lr_warmup(learning_rate, warmup_steps, start_lr, end_lr):
     """
     Applies linear learning rate warmup before the normal learning rate

python/paddle/fluid/layers/nn.py

@@ -23,7 +23,7 @@ import os
 import inspect
 from ..layer_helper import LayerHelper
 from ..initializer import Normal, Constant, NumpyArrayInitializer
-from ..framework import Variable, OpProtoHolder, _in_dygraph_mode
+from ..framework import Variable, OpProtoHolder, in_dygraph_mode
 from ..dygraph import base
 from ..param_attr import ParamAttr
 from .layer_function_generator import autodoc, templatedoc, _generate_doc_string_
@@ -73,6 +73,8 @@ __all__ = [
     'reduce_max',
     'reduce_min',
     'reduce_prod',
+    'reduce_all',
+    'reduce_any',
     'sequence_first_step',
     'sequence_last_step',
     'sequence_slice',
@@ -159,6 +161,7 @@ __all__ = [
     'sum',
     'slice',
     'shape',
+    'rank',
     'logical_and',
     'logical_or',
     'logical_xor',
@@ -482,7 +485,7 @@ def dynamic_lstm(input,
             forward, _ = fluid.layers.dynamic_lstm(
                 input=forward_proj, size=hidden_dim * 4, use_peepholes=False)
     """
-    assert _in_dygraph_mode(
+    assert in_dygraph_mode(
     ) is not True, "please use lstm instead of dynamic_lstm in dygraph mode!"
     assert bias_attr is not False, "bias_attr should not be False in dynamic_lstmp."
     helper = LayerHelper('lstm', **locals())
@@ -868,7 +871,7 @@ def dynamic_lstmp(input,
                                                      proj_activation="tanh")
     """
 
-    assert _in_dygraph_mode(
+    assert in_dygraph_mode(
     ) is not True, "please use lstm instead of dynamic_lstmp in dygraph mode!"
 
     assert bias_attr is not False, "bias_attr should not be False in dynamic_lstmp."
@@ -1042,7 +1045,7 @@ def dynamic_gru(input,
             hidden = fluid.layers.dynamic_gru(input=x, size=hidden_dim)
     """
 
-    assert _in_dygraph_mode(
+    assert in_dygraph_mode(
     ) is not True, "please use gru instead of dynamic_gru in dygraph mode!"
 
     helper = LayerHelper('gru', **locals())
@@ -1761,7 +1764,7 @@ def sequence_conv(input,
         Variable: output of sequence_conv
     """
 
-    assert not _in_dygraph_mode(), (
+    assert not in_dygraph_mode(), (
         "sequence layer is not supported in dygraph mode yet.")
     helper = LayerHelper('sequence_conv', **locals())
     dtype = helper.input_dtype()
@@ -1822,7 +1825,7 @@ def sequence_softmax(input, use_cudnn=False, name=None):
                               dtype='float32', lod_level=1)
              x_sequence_softmax = fluid.layers.sequence_softmax(input=x)
     """
-    assert not _in_dygraph_mode(), (
+    assert not in_dygraph_mode(), (
         "sequence layer is not supported in dygraph mode yet.")
     helper = LayerHelper('sequence_softmax', **locals())
     dtype = helper.input_dtype()
@@ -2316,7 +2319,7 @@ def sequence_pool(input, pool_type, is_test=False):
              last_x = fluid.layers.sequence_pool(input=x, pool_type='last')
              first_x = fluid.layers.sequence_pool(input=x, pool_type='first')
     """
-    assert not _in_dygraph_mode(), (
+    assert not in_dygraph_mode(), (
         "sequence layer is not supported in dygraph mode yet.")
     helper = LayerHelper('sequence_pool', **locals())
     dtype = helper.input_dtype()
@@ -2357,7 +2360,7 @@ def sequence_concat(input, name=None):
 
            out = fluid.layers.sequence_concat(input=[seq1, seq2, seq3])
     """
-    assert not _in_dygraph_mode(), (
+    assert not in_dygraph_mode(), (
         "sequence layer is not supported in dygraph mode yet.")
     helper = LayerHelper('sequence_concat', **locals())
     out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
@@ -2486,7 +2489,7 @@ def sequence_slice(input, offset, length, name=None):
              subseqs = fluid.layers.sequence_slice(input=seqs, offset=offset,
                                                    length=length)
     """
-    assert not _in_dygraph_mode(), (
+    assert not in_dygraph_mode(), (
         "sequence layer is not supported in dygraph mode yet.")
     helper = LayerHelper("sequence_slice", **locals())
     dtype = helper.input_dtype()
@@ -3308,7 +3311,7 @@ def layer_norm(input,
         >>>                          dtype='float32')
         >>> x = fluid.layers.layer_norm(input=data, begin_norm_axis=1)
     """
-    assert _in_dygraph_mode(
+    assert in_dygraph_mode(
     ) is not True, "please use FC instead of fc in dygraph mode!"
     helper = LayerHelper('layer_norm', **locals())
     dtype = helper.input_dtype()
@@ -3947,7 +3950,7 @@ def sequence_expand(x, y, ref_level=-1, name=None):
                              dtype='float32', lod_level=1)
             out = layers.sequence_expand(x=x, y=y, ref_level=0)
     """
-    assert not _in_dygraph_mode(), (
+    assert not in_dygraph_mode(), (
         "sequence layer is not supported in dygraph mode yet.")
     helper = LayerHelper('sequence_expand', input=x, **locals())
     dtype = helper.input_dtype()
@@ -4015,7 +4018,7 @@ def sequence_expand_as(x, y, name=None):
                              dtype='float32', lod_level=1)
             out = layers.sequence_expand_as(x=x, y=y)
     """
-    assert not _in_dygraph_mode(), (
+    assert not in_dygraph_mode(), (
         "sequence layer is not supported in dygraph mode yet.")
     helper = LayerHelper('sequence_expand_as', input=x, **locals())
     dtype = helper.input_dtype()
@@ -4063,7 +4066,7 @@ def sequence_pad(x, pad_value, maxlen=None, name=None):
             out = fluid.layers.sequence_pad(x=x, pad_value=pad_value)
     """
 
-    assert not _in_dygraph_mode(), (
+    assert not in_dygraph_mode(), (
         "sequence layer is not supported in dygraph mode yet.")
     helper = LayerHelper('sequence_pad', input=x, **locals())
     dtype = helper.input_dtype()
@@ -4131,7 +4134,7 @@ def sequence_unpad(x, length, name=None):
             out = fluid.layers.sequence_unpad(x=x, length=len)
     """
 
-    assert not _in_dygraph_mode(), (
+    assert not in_dygraph_mode(), (
         "sequence layer is not supported in dygraph mode yet.")
     helper = LayerHelper('sequence_unpad', input=x, **locals())
     dtype = helper.input_dtype()
@@ -4739,6 +4742,106 @@ def reduce_prod(input, dim=None, keep_dim=False, name=None):
     return out
 
 
+def reduce_all(input, dim=None, keep_dim=False, name=None):
+    """
+    Computes the ``logical and`` of tensor elements over the given dimension.
+
+    Args:
+        input (Variable): The input variable which is a Tensor or LoDTensor.
+        dim (list|int|None): The dimension along which the logical and is computed.
+            If :attr:`None`, compute the logical and over all elements of
+            :attr:`input` and return a Tensor variable with a single element,
+            otherwise must be in the range :math:`[-rank(input), rank(input))`.
+            If :math:`dim[i] < 0`, the dimension to reduce is :math:`rank + dim[i]`.
+        keep_dim (bool): Whether to reserve the reduced dimension in the
+            output Tensor. The result tensor will have one fewer dimension
+            than the :attr:`input` unless :attr:`keep_dim` is true.
+        name(str|None): A name for this layer(optional). If set None, the layer
+                       will be named automatically.
+
+    Returns:
+        Variable: The reduced Tensor variable.
+
+    Examples:
+        .. code-block:: python
+        
+            # x is a bool Tensor variable with following elements:
+            #    [[True, False]
+            #     [True, True]]
+            # Each example is followed by the correspending output tensor.
+            fluid.layers.reduce_all(x)  # False 
+            fluid.layers.reduce_all(x, dim=0)  # [True, False]
+            fluid.layers.reduce_all(x, dim=-1)  # [False, True]
+            fluid.layers.reduce_all(x, dim=1,
+                                     keep_dim=True)  # [[False], [True]]
+
+    """
+    helper = LayerHelper('reduce_all', **locals())
+    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
+    if dim is not None and not isinstance(dim, list):
+        dim = [dim]
+    helper.append_op(
+        type='reduce_all',
+        inputs={'X': input},
+        outputs={'Out': out},
+        attrs={
+            'dim': dim if dim != None else [0],
+            'keep_dim': keep_dim,
+            'reduce_all': True if dim == None else False
+        })
+    return out
+
+
+def reduce_any(input, dim=None, keep_dim=False, name=None):
+    """
+    Computes the ``logical or`` of tensor elements over the given dimension.
+
+    Args:
+        input (Variable): The input variable which is a Tensor or LoDTensor.
+        dim (list|int|None): The dimension along which the logical or is computed.
+            If :attr:`None`, compute the logical or over all elements of
+            :attr:`input` and return a Tensor variable with a single element,
+            otherwise must be in the range :math:`[-rank(input), rank(input))`.
+            If :math:`dim[i] < 0`, the dimension to reduce is :math:`rank + dim[i]`.
+        keep_dim (bool): Whether to reserve the reduced dimension in the
+            output Tensor. The result tensor will have one fewer dimension
+            than the :attr:`input` unless :attr:`keep_dim` is true.
+        name(str|None): A name for this layer(optional). If set None, the layer
+                       will be named automatically.
+
+    Returns:
+        Variable: The reduced Tensor variable.
+
+    Examples:
+        .. code-block:: python
+
+            # x is a bool Tensor variable with following elements:
+            #    [[True, False]
+            #     [False, False]]
+            # Each example is followed by the correspending output tensor.
+            fluid.layers.reduce_any(x)  # True
+            fluid.layers.reduce_any(x, dim=0)  # [True, False]
+            fluid.layers.reduce_any(x, dim=-1)  # [True, False]
+            fluid.layers.reduce_any(x, dim=1,
+                                     keep_dim=True)  # [[True], [False]]
+
+    """
+    helper = LayerHelper('reduce_any', **locals())
+    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
+    if dim is not None and not isinstance(dim, list):
+        dim = [dim]
+    helper.append_op(
+        type='reduce_any',
+        inputs={'X': input},
+        outputs={'Out': out},
+        attrs={
+            'dim': dim if dim != None else [0],
+            'keep_dim': keep_dim,
+            'reduce_all': True if dim == None else False
+        })
+    return out
+
+
 def split(input, num_or_sections, dim=-1, name=None):
     """
     Split the input tensor into multiple sub-tensors.
@@ -4820,7 +4923,7 @@ def l2_normalize(x, axis, epsilon=1e-12, name=None):
             the dimension to normalization is rank(X) + axis. -1 is the
             last dimension.
         epsilon(float): The epsilon value is used to avoid division by zero, \
-            the defalut value is 1e-10.
+            the defalut value is 1e-12.
         name(str|None): A name for this layer(optional). If set None, the layer \
             will be named automatically.
 
@@ -5306,7 +5409,7 @@ def sequence_reshape(input, new_dim):
             x = fluid.layers.data(shape=[5, 20], dtype='float32', lod_level=1)
             x_reshaped = fluid.layers.sequence_reshape(input=x, new_dim=10)
     """
-    assert not _in_dygraph_mode(), (
+    assert not in_dygraph_mode(), (
         "sequence layer is not supported in dygraph mode yet.")
     helper = LayerHelper('sequence_reshape', **locals())
     out = helper.create_variable_for_type_inference(helper.input_dtype())
@@ -5842,7 +5945,7 @@ def im2sequence(input,
                 input=layer, stride=[1, 1], filter_size=[2, 2])
 
     """
-    assert not _in_dygraph_mode(), (
+    assert not in_dygraph_mode(), (
         "sequence layer is not supported in dygraph mode yet.")
 
     if isinstance(filter_size, int):
@@ -6486,7 +6589,7 @@ def squeeze(input, axes, name=None):
             x = layers.data(name='x', shape=[5, 1, 10])
             y = layers.sequeeze(input=x, axes=[1])
     """
-    assert not _in_dygraph_mode(), (
+    assert not in_dygraph_mode(), (
         "squeeze layer is not supported in dygraph mode yet.")
     helper = LayerHelper("squeeze", **locals())
     out = helper.create_variable_for_type_inference(dtype=input.dtype)
@@ -7139,10 +7242,10 @@ def image_resize(input,
         out_shape(list|tuple|Variable|None): Output shape of image resize
                                     layer, the shape is (out_h, out_w).
                                     Default: None
-        scale(float|None): The multiplier for the input height or width.
-                         At least one of out_shape or scale must be set.
-                         And out_shape has a higher priority than scale.
-                         Default: None
+        scale(float|None): The multiplier for the input height or width. At
+             least one of :attr:`out_shape` or :attr:`scale` must be set. 
+             And :attr:`out_shape` has a higher priority than :attr:`scale`. 
+             Default: None.
         name(str|None): A name for this layer(optional). If set None, the layer
                         will be named automatically.
         resample(str): The resample method. It supports 'BILINEAR' and 'NEAREST'
@@ -7180,6 +7283,7 @@ def image_resize(input,
                     or 'NEAREST' currently.
         ValueError: One of out_shape and scale must not be None.
         ValueError: out_shape length should be 2.
+        ValueError: scale should be greater than zero.
         TypeError: align_corners shoule be a bool value
         ValueError: align_mode can only be '0' or '1'
 
@@ -7211,26 +7315,36 @@ def image_resize(input,
     def _is_list_or_turple_(data):
         return (isinstance(data, list) or isinstance(data, tuple))
 
-    out_h = 0
-    out_w = 0
     inputs = {"X": input}
+    attrs = {
+        "out_h": 0,
+        "out_w": 0,
+        "interp_method": resample_type,
+        "align_corners": align_corners,
+        "align_mode": align_mode
+    }
+
     if out_shape is not None:
         if isinstance(out_shape, Variable):
             warnings.warn("out_shape as Variable type is deprecated, \
                     it is recommended to use actual_shape instead of \
                     out_shape to specify output shape dynamically.")
             inputs['OutSize'] = out_shape
-        elif not (_is_list_or_turple_(out_shape)):
-            raise TypeError("out_shape should be a list or tuple or Variable.")
-        elif len(out_shape) != 2:
-            raise ValueError("out_shape length should be 2.")
-
-        out_shape = list(map(int, out_shape))
-        out_h = out_shape[0]
-        out_w = out_shape[1]
+        else:
+            if not (_is_list_or_turple_(out_shape)):
+                raise TypeError(
+                    "out_shape should be a list or tuple or Variable.")
+            if len(out_shape) != 2:
+                raise ValueError("out_shape length should be 2.")
+
+            out_shape = list(map(int, out_shape))
+            attrs['out_h'] = out_shape[0]
+            attrs['out_w'] = out_shape[1]
+
     else:
-        out_h = int(input.shape[2] * scale)
-        out_w = int(input.shape[3] * scale)
+        if scale <= 0:
+            raise ValueError("scale should be greater than zero.")
+        attrs['scale'] = float(scale)
 
     if isinstance(actual_shape, Variable):
         inputs["OutSize"] = actual_shape
@@ -7242,13 +7356,7 @@ def image_resize(input,
         type='{}_interp'.format(resample_type),
         inputs=inputs,
         outputs={"Out": out},
-        attrs={
-            "out_h": out_h,
-            "out_w": out_w,
-            "interp_method": resample_type,
-            "align_corners": align_corners,
-            "align_mode": align_mode
-        })
+        attrs=attrs)
     return out
 
 
@@ -7316,11 +7424,14 @@ def resize_bilinear(input,
     Args:
         input(${x_type}): ${x_comment}.
 
-        out_shape(${out_size_type}): ${out_size_comment}.
+        out_shape(list|tuple|Variable|None): Output shape of resize bilinear
+                                    layer, the shape is (out_h, out_w).
+                                    Default: None
 
         scale(float|None): The multiplier for the input height or width. At
-             least one of out_shape or scale must be set. And out_shape has
-             a higher priority than scale. Default: None.
+             least one of :attr:`out_shape` or :attr:`scale` must be set. 
+             And :attr:`out_shape` has a higher priority than :attr:`scale`. 
+             Default: None.
 
         name(str|None): The output variable name.
         actual_shape(Variable): An optional input to specify output shape
@@ -7407,11 +7518,14 @@ def resize_nearest(input,
     Args:
         input(${x_type}): ${x_comment}.
 
-        out_shape(${out_size_type}): ${out_size_comment}.
+        out_shape(list|tuple|Variable|None): Output shape of resize nearest
+                                    layer, the shape is (out_h, out_w).
+                                    Default: None
 
         scale(float|None): The multiplier for the input height or width. At
-             least one of out_shape or scale must be set. And out_shape has
-             a higher priority than scale. Default: None.
+             least one of :attr:`out_shape` or :attr:`scale` must be set. 
+             And :attr:`out_shape` has a higher priority than :attr:`scale`. 
+             Default: None.
 
         name(str|None): The output variable name.
         actual_shape(Variable): An optional input to specify output shape
@@ -7621,7 +7735,7 @@ def sequence_scatter(input, index, updates, name=None):
             output = fluid.layers.sequence_scatter(input, index, updates)
 
     """
-    assert not _in_dygraph_mode(), (
+    assert not in_dygraph_mode(), (
         "sequence layer is not supported in dygraph mode yet.")
     helper = LayerHelper('sequence_scatter', **locals())
     dtype = helper.input_dtype()
@@ -8711,7 +8825,7 @@ def sequence_enumerate(input, win_size, pad_value=0, name=None):
             x = fluid.layers.data(shape[30, 1], dtype='int32', lod_level=1)
             out = fluid.layers.sequence_enumerate(input=x, win_size=3, pad_value=0)
     """
-    assert not _in_dygraph_mode(), (
+    assert not in_dygraph_mode(), (
         "sequence layer is not supported in dygraph mode yet.")
     helper = LayerHelper('sequence_enumerate', **locals())
     out = helper.create_variable_for_type_inference(
@@ -8752,7 +8866,7 @@ def sequence_mask(x, maxlen=None, dtype='int64', name=None):
         Variable: The output sequence mask.
 
     """
-    assert not _in_dygraph_mode(), (
+    assert not in_dygraph_mode(), (
         "sequence layer is not supported in dygraph mode yet.")
 
     helper = LayerHelper('sequence_mask', **locals())
@@ -9227,11 +9341,37 @@ def shape(input):
     return out
 
 
+def rank(input):
+    """
+    **Rank Layer**
+
+    Returns the number of dimensions for a tensor, which is a 0-D int32 Tensor.
+
+    Args:
+        input (Variable): The input variable.
+
+    Returns:
+        Variable: The rank of the input variable.
+
+    Examples:
+        .. code-block:: python
+
+            input = layers.data(
+                name="input", shape=[3, 100, 100], dtype="float32")
+            rank = layers.rank(input) # 4
+    """
+
+    ndims = len(input.shape)
+    out = assign(np.array(ndims, 'int32'))
+
+    return out
+
+
 def _elementwise_op(helper):
     op_type = helper.layer_type
     x = helper.kwargs.get('x', None)
     y = helper.kwargs.get('y', None)
-    if _in_dygraph_mode():
+    if in_dygraph_mode():
         x = base.to_variable(x)
         y = base.to_variable(y)
 
@@ -9804,7 +9944,7 @@ def sequence_reverse(x, name=None):
     Returns:
         out(${y_type}): ${y_comment}
     """
-    assert not _in_dygraph_mode(), (
+    assert not in_dygraph_mode(), (
         "sequence layer is not supported in dygraph mode yet.")
     helper = LayerHelper("sequence_reverse", **locals())
     if name is None:
@@ -10992,7 +11132,7 @@ def pixel_shuffle(x, upscale_factor):
 
     Returns:
 
-        Out(Variable): the pixel shuffle result is a tensor variable with the same shape and the same type as the input.
+        Out(Variable): Reshaped tensor according to the new dimension.
 
     Raises:
 

python/paddle/fluid/layers/tensor.py

@@ -24,26 +24,11 @@ from .layer_function_generator import templatedoc
 import numpy
 
 __all__ = [
-    'create_tensor',
-    'create_parameter',
-    'create_global_var',
-    'cast',
-    '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',
+    'create_tensor', 'create_parameter', 'create_global_var', 'cast',
+    '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'
 ]
 
 
@@ -826,3 +811,45 @@ def range(start, end, step, dtype):
                 'Step': step},
         outputs={'Out': [out]})
     return out
+
+
+def linspace(start, stop, num, dtype):
+    """
+    Return fixed number of evenly spaced values within a given interval.
+
+    First entry is start, and last entry is stop. In the case when Num is 1, only Start is returned. Like linspace function of numpy.
+
+    Args:
+        start(float|Variable): First entry in the sequence. It is a float scalar, or a tensor of shape [1] with type 'float32'|'float64'.
+        stop(float|Variable): Last entry in the sequence. It is a float scalar, or a tensor of shape [1] with type 'float32'|'float64'.
+        num(int|Variable): Number of entry in the sequence. It is an int scalar, or a tensor of shape [1] with type int32.
+        dtype(string): 'float32'|'float64', the data type of the output tensor.
+
+    Returns:
+        Variable: The tensor variable storing a 1-D tensor. 
+
+    Examples:
+        .. code-block:: python
+
+             data = fluid.layers.linspace(0, 10, 5, 'float32') # [0.0,  2.5,  5.0,  7.5, 10.0]
+             data = fluid.layers.linspace(0, 10, 1, 'float32') # [0.0]
+
+    """
+    helper = LayerHelper("linspace", **locals())
+
+    if not isinstance(start, Variable):
+        start = fill_constant([1], dtype, start)
+    if not isinstance(stop, Variable):
+        stop = fill_constant([1], dtype, stop)
+    if not isinstance(num, Variable):
+        num = fill_constant([1], 'int32', num)
+
+    out = helper.create_variable_for_type_inference(dtype=start.dtype)
+
+    helper.append_op(
+        type='linspace',
+        inputs={'Start': start,
+                'Stop': stop,
+                'Num': num},
+        outputs={'Out': [out]})
+    return out

python/paddle/fluid/optimizer.py

@@ -55,7 +55,7 @@ class Optimizer(object):
     """
 
     def __init__(self, learning_rate, regularization=None, name=None):
-        if framework._in_dygraph_mode():
+        if framework.in_dygraph_mode():
             if not isinstance(learning_rate, float) and \
                     not isinstance(learning_rate, LearningRateDecay):
                 raise TypeError(
@@ -205,7 +205,7 @@ class Optimizer(object):
             name = self._name + "_" + name
         if (name in self._accumulators and
                 param.name in self._accumulators[name]):
-            if framework._in_dygraph_mode():
+            if framework.in_dygraph_mode():
                 return self._accumulators[name][param.name]
             raise Exception("Accumulator {} already exists for parameter {}".
                             format(name, param.name))
@@ -363,7 +363,7 @@ class Optimizer(object):
             See examples in `apply_gradients`.
         """
         self._dtype = loss.dtype
-        if framework._in_dygraph_mode():
+        if framework.in_dygraph_mode():
             if parameter_list is not None:
                 parameters = parameter_list
             else:
@@ -448,7 +448,7 @@ class Optimizer(object):
         Returns:
             list: A list of operators appended to the current program.
         """
-        if framework._in_dygraph_mode():
+        if framework.in_dygraph_mode():
             with program_guard(framework.default_main_program(),
                                framework.default_startup_program()):
                 optimize_ops = self._create_optimization_pass(params_grads)
@@ -628,16 +628,16 @@ class DGCMomentumOptimizer(MomentumOptimizer):
 
     Original paper is https://arxiv.org/abs/1712.01887
 
-    DGC reduce the communication bandwidth by sending only the important gradients (sparse update):\
+    DGC reduces the communication bandwidth by sending only the important gradients (sparse update):\
         only gradients larger than a threshold are transmitted.
 
-    To avoid losing information, DGC accumulate the rest of the gradients locally.
+    To avoid losing information, DGC accumulates the rest of the gradients locally.
 
     Eventually, these gradients become large enough to be transmitted.
 
-    Thus, DGC send the large gradients immediately but eventually send all of the gradients over time.
+    Thus, DGC sends the large gradients immediately but eventually send all of the gradients over time.
 
-    To ensure no loss of accuracy, DGC employs momentum correc-tionandlocal gradient clipping on top of the gradient sparsification to maintain model performance.
+    To ensure no loss of accuracy, DGC employs momentum correction and local gradient clipping on top of the gradient sparsification to maintain model performance.
 
     DGC also uses momentum factor masking and warmup training to overcome the staleness problem caused by reduced communication.
 
@@ -652,7 +652,7 @@ class DGCMomentumOptimizer(MomentumOptimizer):
         learning_rate (float|Variable): the learning rate used to update parameters. \
             Can be a float value or a Variable with one float value as data element.
         momentum (float): Momentum factor.
-        rampup_begin_step (int): The begining step from which gradient compression is implemented.
+        rampup_begin_step (int): The beginning step from which gradient compression is implemented.
         rampup_step (int): How long it use the sparsity periods. Default is 1.
             for example: If the sparsity is [0.75, 0.9375, 0.984375, 0.996, 0.999], and the rampup_step is 5, \
                 it will use 0.75 at 0 step, and 0.9375 at 1 step, and so on. And when reach sparsity array ends, \
@@ -660,9 +660,9 @@ class DGCMomentumOptimizer(MomentumOptimizer):
         sparsity (list[float]): Get top important element from gradient tensor, the ratio is (1 - current sparsity).
         use_nesterov (bool): Enables Nesterov momentum. True means use nesterov.
         local_grad_clip_norm (float): Clip norm value if needed.
-        num_trainers: The number of training node.
+        num_trainers: The number of training nodes.
         regularization: A Regularizer, such as fluid.regularizer.L2DecayRegularizer.
-        name: A optional name prefix.
+        name: An optional name prefix.
 
     Examples:
         .. code-block:: python

python/paddle/fluid/tests/book/CMakeLists.txt

@@ -6,4 +6,6 @@ foreach(src ${TEST_OPS})
     py_test(${src} SRCS ${src}.py)
 endforeach()
 
-add_subdirectory(high-level-api)
+if(WITH_HIGH_LEVEL_API_TEST)
+  add_subdirectory(high-level-api)
+endif()

python/paddle/fluid/tests/book/high-level-api/CMakeLists.txt

-file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
+file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*_new_api.py")
 string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
 
-# default test
-foreach(src ${TEST_OPS})
-    py_test(${src} SRCS ${src}.py)
-endforeach()
+# This test is buggy
+# py_test(test_understand_sentiment_dynamic_rnn SRCS
+# 	test_understand_sentiment_dynamic_rnn.py SERIAL)
+LIST(REMOVE_ITEM TEST_OPS test_understand_sentiment_dynamic_rnn_new_api)
 
-add_subdirectory(fit_a_line)
-add_subdirectory(recognize_digits)
-add_subdirectory(image_classification)
-add_subdirectory(understand_sentiment)
-add_subdirectory(label_semantic_roles)
-add_subdirectory(word2vec)
-add_subdirectory(recommender_system)
-add_subdirectory(machine_translation)
+if(NOT APPLE)
+    # default test
+    foreach(src ${TEST_OPS})
+        py_test(${src} SRCS ${src}.py)
+    endforeach()
+else()
+    foreach(src ${TEST_OPS})
+        if(${src} STREQUAL "test_image_classification_vgg_new_api")
+            message(WARNING "These tests has been disabled in OSX for random fail: \n" ${src})
+        elseif(${src} STREQUAL "test_image_classification_resnet_new_api")
+            message(WARNING "These tests has been disabled in OSX for random fail: \n" ${src})
+        elseif(${src} STREQUAL "test_recognize_digits_conv_new_api")
+            message(WARNING "These tests has been disabled in OSX for random fail: \n" ${src})
+        elseif(${src} STREQUAL "test_recognize_digits_mlp_new_api")
+            message(WARNING "These tests has been disabled in OSX for random fail: \n" ${src})
+        elseif()
+            py_test(${src} SRCS ${src}.py)
+        endif()
+    endforeach()
+endif()

python/paddle/fluid/tests/book/high-level-api/fit_a_line/CMakeLists.txt

-file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
-string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
-
-# default test
-foreach(src ${TEST_OPS})
-    py_test(${src} SRCS ${src}.py)
-endforeach()

python/paddle/fluid/tests/book/high-level-api/image_classification/CMakeLists.txt

-file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
-string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
-
-if(NOT APPLE)
-    # default test
-    foreach(src ${TEST_OPS})
-        py_test(${src} SRCS ${src}.py)
-    endforeach()
-else()
-    foreach(src ${TEST_OPS})
-        if(${src} STREQUAL "test_image_classification_vgg")
-            message(WARNING "These tests has been disabled in OSX for random fail: \n" ${src})
-        elseif(${src} STREQUAL "test_image_classification_resnet")
-            message(WARNING "These tests has been disabled in OSX for random fail: \n" ${src})
-        elseif()
-            py_test(${src} SRCS ${src}.py)
-        endif()
-    endforeach()
-endif()

python/paddle/fluid/tests/book/high-level-api/label_semantic_roles/CMakeLists.txt

-file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
-string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
-
-# default test
-foreach(src ${TEST_OPS})
-    py_test(${src} SRCS ${src}.py)
-endforeach()

python/paddle/fluid/tests/book/high-level-api/machine_translation/CMakeLists.txt

-file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
-string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
-
-# default test
-foreach(src ${TEST_OPS})
-    py_test(${src} SRCS ${src}.py)
-endforeach()

python/paddle/fluid/tests/book/high-level-api/recognize_digits/CMakeLists.txt

-file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
-string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
-
-# default test
-if(NOT APPLE)
-    foreach(src ${TEST_OPS})
-        py_test(${src} SRCS ${src}.py)
-    endforeach()
-else()
-    foreach(src ${TEST_OPS})
-        if(${src} STREQUAL "test_recognize_digits_conv")
-            message(WARNING "These tests has been disabled in OSX for random fail: \n" ${src})
-        elseif(${src} STREQUAL "test_recognize_digits_mlp")
-            message(WARNING "These tests has been disabled in OSX for random fail: \n" ${src})
-        else()
-            py_test(${src} SRCS ${src}.py)
-        endif()
-    endforeach()
-endif()

python/paddle/fluid/tests/book/high-level-api/recommender_system/CMakeLists.txt

-file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
-string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
-
-# default test
-foreach(src ${TEST_OPS})
-    py_test(${src} SRCS ${src}.py)
-endforeach()

python/paddle/fluid/tests/book/high-level-api/understand_sentiment/CMakeLists.txt

-file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
-string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
-
-# This test is buggy
-# py_test(test_understand_sentiment_dynamic_rnn SRCS
-# 	test_understand_sentiment_dynamic_rnn.py SERIAL)
-LIST(REMOVE_ITEM TEST_OPS test_understand_sentiment_dynamic_rnn)
-
-# default test
-foreach(src ${TEST_OPS})
-    py_test(${src} SRCS ${src}.py)
-endforeach()

python/paddle/fluid/tests/book/high-level-api/word2vec/CMakeLists.txt

-file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
-string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
-
-# default test
-foreach(src ${TEST_OPS})
-    py_test(${src} SRCS ${src}.py)
-endforeach()

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -74,7 +74,6 @@ list(REMOVE_ITEM TEST_OPS test_dgc_op)
 list(REMOVE_ITEM TEST_OPS test_dist_se_resnext_nccl)
 list(REMOVE_ITEM TEST_OPS test_dist_transformer)
 list(REMOVE_ITEM TEST_OPS test_parallel_executor_transformer)
-list(REMOVE_ITEM TEST_OPS test_image_classification_resnet)
 list(REMOVE_ITEM TEST_OPS test_bilinear_interp_op)
 list(REMOVE_ITEM TEST_OPS test_nearest_interp_op)
 list(REMOVE_ITEM TEST_OPS test_imperative_resnet)
@@ -125,10 +124,6 @@ if(NOT WIN32)
     py_test_modules(test_ir_memory_optimize_transformer MODULES test_ir_memory_optimize_transformer SERIAL)
 endif()
 
-if(NOT APPLE)
-    py_test_modules(test_image_classification_resnet MODULES test_image_classification_resnet SERIAL)
-endif()
-
 if(CMAKE_BUILD_TYPE STREQUAL "Debug")
     # change the timeout from 600 to 2200, because in debug mode, this test need more time.
     set_tests_properties(test_parallel_executor_seresnext PROPERTIES TIMEOUT 2200)

python/paddle/fluid/tests/unittests/fake_reader.py

+# 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
+import six
+
+
+def fake_imdb_reader(word_dict_size,
+                     sample_num,
+                     lower_seq_len=100,
+                     upper_seq_len=200,
+                     class_dim=2):
+    def __reader__():
+        for _ in six.moves.range(sample_num):
+            length = np.random.random_integers(
+                low=lower_seq_len, high=upper_seq_len, size=[1])[0]
+            ids = np.random.random_integers(
+                low=0, high=word_dict_size - 1, size=[length]).astype('int64')
+            label = np.random.random_integers(
+                low=0, high=class_dim - 1, size=[1]).astype('int64')[0]
+            yield ids, label
+
+    return __reader__

python/paddle/fluid/tests/unittests/parallel_executor_test_base.py

@@ -29,7 +29,8 @@ __all__ = ['TestParallelExecutorBase']
 
 
 class TestParallelExecutorBase(unittest.TestCase):
-    def check_network_convergence(self,
+    @classmethod
+    def check_network_convergence(cls,
                                   method,
                                   use_cuda=True,
                                   memory_opt=True,

python/paddle/fluid/tests/unittests/test_base_layer.py

@@ -18,7 +18,7 @@ import numpy as np
 import paddle.fluid as fluid
 
 
-class L1(fluid.dygraph.Layer):
+class L1(fluid.Layer):
     def __init__(self, prefix):
         super(L1, self).__init__(prefix)
         self._param_attr = fluid.ParamAttr(
@@ -32,7 +32,7 @@ class L1(fluid.dygraph.Layer):
         return self.w1 + self.w2
 
 
-class L2(fluid.dygraph.Layer):
+class L2(fluid.Layer):
     def __init__(self, prefix):
         super(L2, self).__init__(prefix)
         self.layer1 = L1(self.full_name())
@@ -42,7 +42,7 @@ class L2(fluid.dygraph.Layer):
         return self.layer1() + self.layer2()
 
 
-class L3(fluid.dygraph.Layer):
+class L3(fluid.Layer):
     def __init__(self, prefix):
         super(L3, self).__init__(prefix)
         self.layer1 = L2(self.full_name())
@@ -59,7 +59,7 @@ class TestBaseLayer(unittest.TestCase):
             ret = l()
             self.assertEqual(l.w1.name, "test_one_level/L1_0.w_0")
             self.assertEqual(l.w2.name, "test_one_level/L1_0.w_1")
-            self.assertTrue(np.allclose(ret._numpy(), 0.2 * np.ones([2, 2])))
+            self.assertTrue(np.allclose(ret.numpy(), 0.2 * np.ones([2, 2])))
 
     def test_three_level(self):
         with fluid.dygraph.guard():
@@ -72,7 +72,7 @@ class TestBaseLayer(unittest.TestCase):
             self.assertEqual(names[3], "test_three_level/L3_0/L2_0/L1_1.w_1")
             self.assertEqual(names[4], "test_three_level/L3_0/L2_1/L1_0.w_0")
             self.assertEqual(names[5], "test_three_level/L3_0/L2_1/L1_0.w_1")
-            self.assertTrue(np.allclose(ret._numpy(), 0.8 * np.ones([2, 2])))
+            self.assertTrue(np.allclose(ret.numpy(), 0.8 * np.ones([2, 2])))
 
 
 if __name__ == '__main__':

python/paddle/fluid/tests/unittests/test_bilinear_interp_op.py

@@ -91,17 +91,26 @@ class TestBilinearInterpOp(OpTest):
         self.op_type = "bilinear_interp"
         input_np = np.random.random(self.input_shape).astype("float32")
 
-        output_np = bilinear_interp_np(input_np, self.out_h, self.out_w,
-                                       self.out_size, self.actual_shape,
-                                       self.align_corners, self.align_mode)
+        if self.scale > 0:
+            out_h = int(self.input_shape[2] * self.scale)
+            out_w = int(self.input_shape[3] * self.scale)
+        else:
+            out_h = self.out_h
+            out_w = self.out_w
+
+        output_np = bilinear_interp_np(input_np, out_h, out_w, self.out_size,
+                                       self.actual_shape, self.align_corners,
+                                       self.align_mode)
         self.inputs = {'X': input_np}
         if self.out_size is not None:
             self.inputs['OutSize'] = self.out_size
         if self.actual_shape is not None:
             self.inputs['OutSize'] = self.actual_shape
+
         self.attrs = {
             'out_h': self.out_h,
             'out_w': self.out_w,
+            'scale': self.scale,
             'interp_method': self.interp_method,
             'align_corners': self.align_corners,
             'align_mode': self.align_mode
@@ -119,6 +128,7 @@ class TestBilinearInterpOp(OpTest):
         self.input_shape = [2, 3, 4, 4]
         self.out_h = 2
         self.out_w = 2
+        self.scale = 0.
         self.out_size = np.array([3, 3]).astype("int32")
         self.align_corners = True
         self.align_mode = 1
@@ -130,6 +140,7 @@ class TestBilinearInterpCase1(TestBilinearInterpOp):
         self.input_shape = [4, 1, 7, 8]
         self.out_h = 1
         self.out_w = 1
+        self.scale = 0.
         self.align_corners = True
         self.align_mode = 1
 
@@ -140,6 +151,7 @@ class TestBilinearInterpCase2(TestBilinearInterpOp):
         self.input_shape = [3, 3, 9, 6]
         self.out_h = 12
         self.out_w = 12
+        self.scale = 0.
         self.align_corners = True
         self.align_mode = 1
 
@@ -150,6 +162,7 @@ class TestBilinearInterpCase3(TestBilinearInterpOp):
         self.input_shape = [1, 1, 128, 64]
         self.out_h = 64
         self.out_w = 128
+        self.scale = 0.
         self.align_corners = True
         self.align_mode = 1
 
@@ -160,6 +173,7 @@ class TestBilinearInterpCase4(TestBilinearInterpOp):
         self.input_shape = [4, 1, 7, 8]
         self.out_h = 1
         self.out_w = 1
+        self.scale = 0.
         self.out_size = np.array([2, 2]).astype("int32")
         self.align_corners = True
         self.align_mode = 1
@@ -171,6 +185,7 @@ class TestBilinearInterpCase5(TestBilinearInterpOp):
         self.input_shape = [3, 3, 9, 6]
         self.out_h = 12
         self.out_w = 12
+        self.scale = 0.
         self.out_size = np.array([11, 11]).astype("int32")
         self.align_corners = True
         self.align_mode = 1
@@ -182,6 +197,7 @@ class TestBilinearInterpCase6(TestBilinearInterpOp):
         self.input_shape = [1, 1, 128, 64]
         self.out_h = 64
         self.out_w = 128
+        self.scale = 0.
         self.out_size = np.array([65, 129]).astype("int32")
         self.align_corners = True
         self.align_mode = 1
@@ -193,6 +209,7 @@ class TestBilinearInterpActualShape(TestBilinearInterpOp):
         self.input_shape = [3, 2, 32, 16]
         self.out_h = 64
         self.out_w = 32
+        self.scale = 0.
         self.out_size = np.array([66, 40]).astype("int32")
         self.align_corners = True
         self.align_mode = 1
@@ -206,15 +223,25 @@ class TestBilinearInterpOpUint8(OpTest):
         self.op_type = "bilinear_interp"
         input_np = np.random.randint(
             low=0, high=256, size=self.input_shape).astype("uint8")
-        output_np = bilinear_interp_np(input_np, self.out_h, self.out_w,
-                                       self.out_size, self.actual_shape,
-                                       self.align_corners, self.align_mode)
+
+        if self.scale > 0:
+            out_h = int(self.input_shape[2] * self.scale)
+            out_w = int(self.input_shape[3] * self.scale)
+        else:
+            out_h = self.out_h
+            out_w = self.out_w
+
+        output_np = bilinear_interp_np(input_np, out_h, out_w, self.out_size,
+                                       self.actual_shape, self.align_corners,
+                                       self.align_mode)
         self.inputs = {'X': input_np}
         if self.out_size is not None:
             self.inputs['OutSize'] = self.out_size
+
         self.attrs = {
             'out_h': self.out_h,
             'out_w': self.out_w,
+            'scale': self.scale,
             'interp_method': self.interp_method,
             'align_corners': self.align_corners,
             'align_mode': self.align_mode
@@ -229,6 +256,7 @@ class TestBilinearInterpOpUint8(OpTest):
         self.input_shape = [1, 3, 9, 6]
         self.out_h = 10
         self.out_w = 9
+        self.scale = 0.
         self.align_corners = True
         self.align_mode = 1
 
@@ -239,6 +267,7 @@ class TestBilinearInterpCase1Uint8(TestBilinearInterpOpUint8):
         self.input_shape = [2, 3, 128, 64]
         self.out_h = 120
         self.out_w = 50
+        self.scale = 0.
         self.align_corners = True
         self.align_mode = 1
 
@@ -249,6 +278,7 @@ class TestBilinearInterpCase2Uint8(TestBilinearInterpOpUint8):
         self.input_shape = [4, 1, 7, 8]
         self.out_h = 5
         self.out_w = 13
+        self.scale = 0.
         self.out_size = np.array([6, 15]).astype("int32")
         self.align_corners = True
         self.align_mode = 1
@@ -272,5 +302,38 @@ class TestBilinearInterpWithMethod3(TestBilinearInterpOp):
         self.align_mode = 0
 
 
+class TestBilinearInterpScale1(TestBilinearInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [2, 3, 16, 32]
+        self.out_h = 60
+        self.out_w = 25
+        self.scale = 2.
+        self.align_corners = True
+        self.align_mode = 1
+
+
+class TestBilinearInterpScale2(TestBilinearInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [2, 3, 16, 32]
+        self.out_h = 60
+        self.out_w = 25
+        self.scale = 1.
+        self.align_corners = True
+        self.align_mode = 1
+
+
+class TestBilinearInterpScale3(TestBilinearInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [2, 3, 16, 32]
+        self.out_h = 60
+        self.out_w = 25
+        self.scale = 1.5
+        self.align_corners = True
+        self.align_mode = 1
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/test_decoupled_py_reader.py

@@ -19,7 +19,7 @@ import time
 import six
 import unittest
 
-EPOCH_NUM = 60
+EPOCH_NUM = 20
 BATCH_SIZE = 32
 CLASS_NUM = 10
 

python/paddle/fluid/tests/unittests/test_eager_deletion_dynamic_rnn_base.py

@@ -22,6 +22,8 @@ import paddle
 import paddle.fluid.core as core
 import paddle.fluid as fluid
 from paddle.fluid import compiler
+import numpy as np
+from fake_reader import fake_imdb_reader
 
 
 def train(network, use_cuda, use_parallel_executor, batch_size=32, pass_num=2):
@@ -35,16 +37,16 @@ def train(network, use_cuda, use_parallel_executor, batch_size=32, pass_num=2):
         )
         return
 
-    word_dict = paddle.dataset.imdb.word_dict()
-    train_reader = paddle.batch(
-        paddle.dataset.imdb.train(word_dict), batch_size=batch_size)
+    word_dict_size = 5147
+    reader = fake_imdb_reader(word_dict_size, batch_size * 40)
+    train_reader = paddle.batch(reader, batch_size=batch_size)
 
     data = fluid.layers.data(
         name="words", shape=[1], dtype="int64", lod_level=1)
 
     label = fluid.layers.data(name="label", shape=[1], dtype="int64")
 
-    cost = network(data, label, len(word_dict))
+    cost = network(data, label, word_dict_size)
     cost.persistable = True
     optimizer = fluid.optimizer.Adagrad(learning_rate=0.2)
     optimizer.minimize(cost)

python/paddle/fluid/tests/unittests/test_eager_deletion_no_need_buffer_vars_inference.py

@@ -18,20 +18,21 @@ import importlib
 
 fluid.core._set_eager_deletion_mode(0.0, 1.0, True)
 
+from test_bilinear_interp_op import *
+from test_concat_op import *
 from test_elementwise_add_op import *
 from test_elementwise_sub_op import *
-from test_concat_op import *
+from test_fill_constant_batch_size_like_op import *
+from test_fill_zeros_like2_op import *
 from test_gather_op import *
 from test_gaussian_random_batch_size_like_op import *
-from test_uniform_random_batch_size_like_op import *
-from test_fill_constant_batch_size_like_op import *
+from test_linear_chain_crf_op import *
 from test_lod_reset_op import *
-from test_scatter_op import *
+from test_lookup_table_op import *
 from test_mean_op import *
-from test_slice_op import *
-from test_linear_chain_crf_op import *
-from test_bilinear_interp_op import *
 from test_nearest_interp_op import *
+from test_pad2d_op import *
+from test_scatter_op import *
 from test_sequence_concat import *
 from test_seq_conv import *
 from test_seq_pool import *
@@ -41,8 +42,10 @@ from test_sequence_pad_op import *
 from test_sequence_unpad_op import *
 from test_sequence_scatter_op import *
 from test_sequence_slice_op import *
-from test_pad2d_op import *
-from test_fill_zeros_like2_op import *
+from test_slice_op import *
+from test_space_to_depth_op import *
+from test_squared_l2_distance_op import *
+from test_uniform_random_batch_size_like_op import *
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/test_gradient_clip.py

@@ -19,6 +19,8 @@ import numpy as np
 import paddle
 import paddle.fluid.core as core
 import paddle.fluid as fluid
+import six
+from fake_reader import fake_imdb_reader
 
 
 def bow_net(data,
@@ -48,11 +50,10 @@ def bow_net(data,
 
 class TestGradientClip(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 get_places(self):
         places = [core.CPUPlace()]
@@ -131,7 +132,7 @@ class TestGradientClip(unittest.TestCase):
             data = fluid.layers.data(
                 name="words", shape=[1], dtype="int64", lod_level=1)
             label = fluid.layers.data(name="label", shape=[1], dtype="int64")
-            cost = bow_net(data, label, len(self.word_dict))
+            cost = bow_net(data, label, self.word_dict_len)
 
             fluid.clip.set_gradient_clip(
                 clip=fluid.clip.GradientClipByGlobalNorm(clip_norm=5.0))

python/paddle/fluid/tests/unittests/test_imperative_basic.py

@@ -18,11 +18,11 @@ import numpy as np
 
 import paddle.fluid as fluid
 from paddle.fluid import core
-from paddle.fluid.dygraph.nn import FC
+from paddle.fluid import FC
 from test_imperative_base import new_program_scope
 
 
-class MyLayer(fluid.dygraph.Layer):
+class MyLayer(fluid.Layer):
     def __init__(self, name_scope):
         super(MyLayer, self).__init__(name_scope)
 
@@ -34,7 +34,7 @@ class MyLayer(fluid.dygraph.Layer):
         return [x]
 
 
-class MyPyLayer(fluid.dygraph.PyLayer):
+class MyPyLayer(fluid.PyLayer):
     def __init__(self):
         super(MyPyLayer, self).__init__()
 
@@ -48,7 +48,7 @@ class MyPyLayer(fluid.dygraph.PyLayer):
         return np.array(dout) * (1 - np.square(np.array(out)))
 
 
-class MLP(fluid.dygraph.Layer):
+class MLP(fluid.Layer):
     def __init__(self, name_scope):
         super(MLP, self).__init__(name_scope)
         self._fc1 = FC(self.full_name(),
@@ -71,7 +71,7 @@ class MLP(fluid.dygraph.Layer):
         return x
 
 
-class SimpleRNNCell(fluid.dygraph.Layer):
+class SimpleRNNCell(fluid.Layer):
     def __init__(self, name_scope, step_input_size, hidden_size, output_size,
                  param_attr):
         super(SimpleRNNCell, self).__init__(name_scope)
@@ -81,7 +81,7 @@ class SimpleRNNCell(fluid.dygraph.Layer):
         self._dtype = core.VarDesc.VarType.FP32
         self.param_attr = param_attr
 
-    def _build_once(self, inputs, pre_hidden):
+    def build_once(self, inputs, pre_hidden):
         i2h_param_shape = [self.step_input_size, self.hidden_size]
         h2h_param_shape = [self.hidden_size, self.hidden_size]
         h2o_param_shape = [self.output_size, self.hidden_size]
@@ -159,7 +159,7 @@ class SimpleRNNCell(fluid.dygraph.Layer):
         return reduce_out, hidden
 
 
-class SimpleRNN(fluid.dygraph.Layer):
+class SimpleRNN(fluid.Layer):
     def __init__(self, name_scope):
         super(SimpleRNN, self).__init__(name_scope)
         self.seq_len = 4
@@ -200,22 +200,22 @@ class TestImperative(unittest.TestCase):
                 inputs.append(fluid.dygraph.base.to_variable(x))
             ret = fluid.layers.sums(inputs)
             loss = fluid.layers.reduce_sum(ret)
-            loss._backward()
-            self.assertTrue(np.allclose(ret._numpy(), x * 10))
-            self.assertTrue(np.allclose(inputs[0]._gradient(), x))
+            loss.backward()
+            self.assertTrue(np.allclose(ret.numpy(), x * 10))
+            self.assertTrue(np.allclose(inputs[0].gradient(), x))
 
     def test_layer(self):
         with fluid.dygraph.guard():
             cl = core.Layer()
             cl.forward([])
-            l = fluid.dygraph.Layer("l")
+            l = fluid.Layer("l")
             self.assertRaises(NotImplementedError, l.forward, [])
 
     def test_pylayer_func_id(self):
 
         with fluid.dygraph.guard():
 
-            class PyLayer1(fluid.dygraph.PyLayer):
+            class PyLayer1(fluid.PyLayer):
                 def __init__(self):
                     super(PyLayer1, self).__init__()
 
@@ -227,7 +227,7 @@ class TestImperative(unittest.TestCase):
                 def backward(input):
                     return input
 
-            class PyLayer2(fluid.dygraph.PyLayer):
+            class PyLayer2(fluid.PyLayer):
                 def __init__(self):
                     super(PyLayer2, self).__init__()
 
@@ -257,9 +257,9 @@ class TestImperative(unittest.TestCase):
             my_py_layer = MyPyLayer()
             var_inp = fluid.dygraph.base.to_variable(np_inp)
             outs = my_py_layer(var_inp)
-            dy_out = np.sum(outs[0]._numpy())
-            outs[0]._backward()
-            dy_grad = var_inp._gradient()
+            dy_out = np.sum(outs[0].numpy())
+            outs[0].backward()
+            dy_grad = var_inp.gradient()
 
         with new_program_scope():
             inp = fluid.layers.data(
@@ -287,9 +287,9 @@ class TestImperative(unittest.TestCase):
             l = MyLayer("my_layer")
             x = l(var_inp)[0]
             self.assertIsNotNone(x)
-            dy_out = x._numpy()
-            x._backward()
-            dy_grad = l._x_for_debug._gradient()
+            dy_out = x.numpy()
+            x.backward()
+            dy_grad = l._x_for_debug.gradient()
 
         with new_program_scope():
             inp = fluid.layers.data(
@@ -314,9 +314,9 @@ class TestImperative(unittest.TestCase):
             var_inp = fluid.dygraph.base.to_variable(np_inp)
             mlp = MLP("mlp")
             out = mlp(var_inp)
-            dy_out = out._numpy()
-            out._backward()
-            dy_grad = mlp._fc1._w._gradient()
+            dy_out = out.numpy()
+            out.backward()
+            dy_grad = mlp._fc1._w.gradient()
 
         with new_program_scope():
             inp = fluid.layers.data(
@@ -358,7 +358,7 @@ class TestImperative(unittest.TestCase):
                 x = fluid.layers.elementwise_add(inp1, inp2)
             else:
                 x = fluid.layers.elementwise_sub(inp1, inp2)
-            dygraph_result = x._numpy()
+            dygraph_result = x.numpy()
 
         # static graph
         with new_program_scope():
@@ -407,11 +407,11 @@ class TestImperative(unittest.TestCase):
             var_inp = fluid.layers.reshape(var_inp, shape=[1, 4, 3])
             simple_rnn = SimpleRNN("simple_rnn")
             outs, pre_hiddens = simple_rnn.forward(var_inp)
-            dy_out = outs[3]._numpy()
-            outs[3]._backward()
-            dy_grad_h2o = simple_rnn._cell._h2o_w._gradient()
-            dy_grad_h2h = simple_rnn._cell._h2h_w._gradient()
-            dy_grad_i2h = simple_rnn._cell._i2h_w._gradient()
+            dy_out = outs[3].numpy()
+            outs[3].backward()
+            dy_grad_h2o = simple_rnn._cell._h2o_w.gradient()
+            dy_grad_h2h = simple_rnn._cell._h2h_w.gradient()
+            dy_grad_i2h = simple_rnn._cell._i2h_w.gradient()
 
         with new_program_scope():
             inp = fluid.layers.data(

python/paddle/fluid/tests/unittests/test_imperative_checkpoint.py

@@ -18,11 +18,11 @@ import numpy as np
 import paddle
 import paddle.fluid as fluid
 from paddle.fluid.optimizer import SGDOptimizer
-from paddle.fluid.dygraph.nn import Conv2D, Pool2D, FC
+from paddle.fluid import Conv2D, Pool2D, FC
 from paddle.fluid.dygraph.base import to_variable
 
 
-class SimpleImgConvPool(fluid.dygraph.Layer):
+class SimpleImgConvPool(fluid.Layer):
     def __init__(self,
                  name_scope,
                  num_channels,
@@ -71,7 +71,7 @@ class SimpleImgConvPool(fluid.dygraph.Layer):
         return x
 
 
-class MNIST(fluid.dygraph.Layer):
+class MNIST(fluid.Layer):
     def __init__(self, name_scope):
         super(MNIST, self).__init__(name_scope)
 
@@ -125,21 +125,21 @@ class TestDygraphCheckpoint(unittest.TestCase):
 
                     img = to_variable(dy_x_data)
                     label = to_variable(y_data)
-                    label._stop_gradient = True
+                    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()
+                    dy_out = avg_loss.numpy()
 
-                    avg_loss._backward()
+                    avg_loss.backward()
                     sgd.minimize(avg_loss)
                     fluid.dygraph.save_persistables(mnist, "save_dir")
                     mnist.clear_gradients()
 
                     for param in mnist.parameters():
-                        dy_param_init_value[param.name] = param._numpy()
+                        dy_param_init_value[param.name] = param.numpy()
 
                     mnist.load_dict(
                         fluid.dygraph.load_persistables(mnist, "save_dir"))

python/paddle/fluid/tests/unittests/test_imperative_deepcf.py

@@ -32,11 +32,11 @@ NUM_BATCHES = int(os.environ.get('NUM_BATCHES', 5))
 NUM_EPOCHES = int(os.environ.get('NUM_EPOCHES', 1))
 
 
-class DMF(fluid.dygraph.Layer):
+class DMF(fluid.Layer):
     def __init__(self, name_scope):
         super(DMF, self).__init__(name_scope)
-        self._user_latent = fluid.dygraph.FC(self.full_name(), 256)
-        self._item_latent = fluid.dygraph.FC(self.full_name(), 256)
+        self._user_latent = fluid.FC(self.full_name(), 256)
+        self._item_latent = fluid.FC(self.full_name(), 256)
 
         self._user_layers = []
         self._item_layers = []
@@ -45,13 +45,11 @@ class DMF(fluid.dygraph.Layer):
             self._user_layers.append(
                 self.add_sublayer(
                     'user_layer_%d' % i,
-                    fluid.dygraph.FC(
-                        self.full_name(), self._hid_sizes[i], act='relu')))
+                    fluid.FC(self.full_name(), self._hid_sizes[i], act='relu')))
             self._item_layers.append(
                 self.add_sublayer(
                     'item_layer_%d' % i,
-                    fluid.dygraph.FC(
-                        self.full_name(), self._hid_sizes[i], act='relu')))
+                    fluid.FC(self.full_name(), self._hid_sizes[i], act='relu')))
 
     def forward(self, users, items):
         users = self._user_latent(users)
@@ -63,19 +61,18 @@ class DMF(fluid.dygraph.Layer):
         return fluid.layers.elementwise_mul(users, items)
 
 
-class MLP(fluid.dygraph.Layer):
+class MLP(fluid.Layer):
     def __init__(self, name_scope):
         super(MLP, self).__init__(name_scope)
-        self._user_latent = fluid.dygraph.FC(self.full_name(), 256)
-        self._item_latent = fluid.dygraph.FC(self.full_name(), 256)
+        self._user_latent = fluid.FC(self.full_name(), 256)
+        self._item_latent = fluid.FC(self.full_name(), 256)
         self._match_layers = []
         self._hid_sizes = [128, 64]
         for i in range(len(self._hid_sizes)):
             self._match_layers.append(
                 self.add_sublayer(
                     'match_layer_%d' % i,
-                    fluid.dygraph.FC(
-                        self.full_name(), self._hid_sizes[i], act='relu')))
+                    fluid.FC(self.full_name(), self._hid_sizes[i], act='relu')))
         self._mat
 
     def forward(self, users, items):
@@ -88,7 +85,7 @@ class MLP(fluid.dygraph.Layer):
         return match_vec
 
 
-class DeepCF(fluid.dygraph.Layer):
+class DeepCF(fluid.Layer):
     def __init__(self, name_scope, num_users, num_items, matrix):
         super(DeepCF, self).__init__(name_scope)
         self._num_users = num_users
@@ -99,11 +96,11 @@ class DeepCF(fluid.dygraph.Layer):
             matrix.dtype,
             is_bias=False,
             default_initializer=fluid.initializer.NumpyArrayInitializer(matrix))
-        self._rating_matrix._stop_gradient = True
+        self._rating_matrix.stop_gradient = True
 
         self._mlp = MLP(self.full_name())
         self._dmf = DMF(self.full_name())
-        self._match_fc = fluid.dygraph.FC(self.full_name(), 1, act='sigmoid')
+        self._match_fc = fluid.FC(self.full_name(), 1, act='sigmoid')
 
     def forward(self, users, items):
         # users_emb = self._user_emb(users)
@@ -255,10 +252,10 @@ class TestDygraphDeepCF(unittest.TestCase):
                         fluid.layers.log_loss(prediction,
                                               to_variable(labels_np[
                                                   slice:slice + BATCH_SIZE])))
-                    loss._backward()
+                    loss.backward()
                     adam.minimize(loss)
                     deepcf.clear_gradients()
-                    dy_loss = loss._numpy()
+                    dy_loss = loss.numpy()
                     sys.stderr.write('dynamic loss: %s %s\n' % (slice, dy_loss))
 
         self.assertEqual(static_loss, dy_loss)

python/paddle/fluid/tests/unittests/test_imperative_gan.py

@@ -22,12 +22,12 @@ import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 from paddle.fluid.optimizer import SGDOptimizer
-from paddle.fluid.dygraph.nn import Conv2D, Pool2D, FC
+from paddle.fluid import Conv2D, Pool2D, FC
 from test_imperative_base import new_program_scope
 from paddle.fluid.dygraph.base import to_variable
 
 
-class Discriminator(fluid.dygraph.Layer):
+class Discriminator(fluid.Layer):
     def __init__(self, name_scope):
         super(Discriminator, self).__init__(name_scope)
         self._fc1 = FC(self.full_name(), size=32, act='elu')
@@ -38,7 +38,7 @@ class Discriminator(fluid.dygraph.Layer):
         return self._fc2(x)
 
 
-class Generator(fluid.dygraph.Layer):
+class Generator(fluid.Layer):
     def __init__(self, name_scope):
         super(Generator, self).__init__(name_scope)
         self._fc1 = FC(self.full_name(), size=64, act='elu')
@@ -150,7 +150,7 @@ class TestDygraphGAN(unittest.TestCase):
                     x=d_fake, label=to_variable(np.zeros([2, 1], np.float32))))
 
             d_loss = d_loss_real + d_loss_fake
-            d_loss._backward()
+            d_loss.backward()
             sgd.minimize(d_loss)
             discriminator.clear_gradients()
             generator.clear_gradients()
@@ -160,15 +160,15 @@ class TestDygraphGAN(unittest.TestCase):
             g_loss = fluid.layers.reduce_mean(
                 fluid.layers.sigmoid_cross_entropy_with_logits(
                     x=d_fake, label=to_variable(np.ones([2, 1], np.float32))))
-            g_loss._backward()
+            g_loss.backward()
             sgd.minimize(g_loss)
             for p in discriminator.parameters():
-                dy_params[p.name] = p._numpy()
+                dy_params[p.name] = p.numpy()
             for p in generator.parameters():
-                dy_params[p.name] = p._numpy()
+                dy_params[p.name] = p.numpy()
 
-            dy_g_loss = g_loss._numpy()
-            dy_d_loss = d_loss._numpy()
+            dy_g_loss = g_loss.numpy()
+            dy_d_loss = d_loss.numpy()
 
         self.assertEqual(dy_g_loss, static_g_loss)
         self.assertEqual(dy_d_loss, static_d_loss)

python/paddle/fluid/tests/unittests/test_imperative_gnn.py

@@ -15,14 +15,12 @@
 import contextlib
 import unittest
 import numpy as np
-import six
 import sys
 
 import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 from paddle.fluid.optimizer import AdamOptimizer
-from paddle.fluid.dygraph.nn import Conv2D, Pool2D, FC
 from test_imperative_base import new_program_scope
 from paddle.fluid.dygraph.base import to_variable
 
@@ -31,7 +29,7 @@ def gen_data():
     pass
 
 
-class GraphConv(fluid.dygraph.Layer):
+class GraphConv(fluid.Layer):
     def __init__(self, name_scope, in_features, out_features):
         super(GraphConv, self).__init__(name_scope)
 
@@ -50,7 +48,7 @@ class GraphConv(fluid.dygraph.Layer):
         return fluid.layers.matmul(adj, support) + self.bias
 
 
-class GCN(fluid.dygraph.Layer):
+class GCN(fluid.Layer):
     def __init__(self, name_scope, num_hidden):
         super(GCN, self).__init__(name_scope)
         self.gc = GraphConv(self.full_name(), num_hidden, 32)
@@ -134,10 +132,9 @@ class TestDygraphGNN(unittest.TestCase):
             loss = fluid.layers.reduce_sum(loss)
             adam = AdamOptimizer(learning_rate=1e-3)
             adam.minimize(loss)
-            self.assertEqual(static_loss, loss._numpy())
-            self.assertTrue(
-                np.allclose(static_weight, model.gc.weight._numpy()))
-            sys.stderr.write('%s %s\n' % (static_loss, loss._numpy()))
+            self.assertEqual(static_loss, loss.numpy())
+            self.assertTrue(np.allclose(static_weight, model.gc.weight.numpy()))
+            sys.stderr.write('%s %s\n' % (static_loss, loss.numpy()))
 
 
 if __name__ == '__main__':

python/paddle/fluid/tests/unittests/test_imperative_mnist.py

@@ -128,25 +128,25 @@ class TestImperativeMnist(unittest.TestCase):
 
                     img = to_variable(dy_x_data)
                     label = to_variable(y_data)
-                    label._stop_gradient = True
+                    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()
+                    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()
+                            dy_param_init_value[param.name] = param.numpy()
 
-                    avg_loss._backward()
+                    avg_loss.backward()
                     sgd.minimize(avg_loss)
                     mnist.clear_gradients()
 
                     dy_param_value = {}
                     for param in mnist.parameters():
-                        dy_param_value[param.name] = param._numpy()
+                        dy_param_value[param.name] = param.numpy()
 
         with new_program_scope():
             fluid.default_startup_program().random_seed = seed

python/paddle/fluid/tests/unittests/test_imperative_optimizer.py

@@ -28,7 +28,7 @@ from paddle.fluid.dygraph.base import to_variable
 from test_imperative_base import new_program_scope
 
 
-class MLP(fluid.dygraph.Layer):
+class MLP(fluid.Layer):
     def __init__(self, name_scope, param_attr=None, bias_attr=None):
         super(MLP, self).__init__(name_scope)
 
@@ -75,18 +75,18 @@ class TestImperativeOptimizerBase(unittest.TestCase):
 
                 cost = mlp(img)
                 avg_loss = fluid.layers.reduce_mean(cost)
-                dy_out = avg_loss._numpy()
+                dy_out = avg_loss.numpy()
 
                 if batch_id == 0:
                     for param in mlp.parameters():
-                        dy_param_init_value[param.name] = param._numpy()
+                        dy_param_init_value[param.name] = param.numpy()
 
-                avg_loss._backward()
+                avg_loss.backward()
                 optimizer.minimize(avg_loss)
                 mlp.clear_gradients()
                 dy_param_value = {}
                 for param in mlp.parameters():
-                    dy_param_value[param.name] = param._numpy()
+                    dy_param_value[param.name] = param.numpy()
 
         with new_program_scope():
             fluid.default_startup_program().random_seed = seed

python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py

@@ -24,10 +24,9 @@ from paddle.fluid.dygraph.base import to_variable
 from test_imperative_base import new_program_scope
 import numpy as np
 import six
-from paddle.fluid.backward import append_backward
 
 
-class SimpleLSTMRNN(fluid.dygraph.Layer):
+class SimpleLSTMRNN(fluid.Layer):
     def __init__(self,
                  name_scope,
                  hidden_size,
@@ -45,7 +44,7 @@ class SimpleLSTMRNN(fluid.dygraph.Layer):
         self.cell_array = []
         self.hidden_array = []
 
-    def _build_once(self, input_embedding, init_hidden=None, init_cell=None):
+    def build_once(self, input_embedding, init_hidden=None, init_cell=None):
         self.weight_1_arr = []
         self.weight_2_arr = []
         self.bias_arr = []
@@ -132,7 +131,7 @@ class SimpleLSTMRNN(fluid.dygraph.Layer):
         return real_res, last_hidden, last_cell
 
 
-class PtbModel(fluid.dygraph.Layer):
+class PtbModel(fluid.Layer):
     def __init__(self,
                  name_scope,
                  hidden_size,
@@ -177,7 +176,7 @@ class PtbModel(fluid.dygraph.Layer):
             default_initializer=fluid.initializer.UniformInitializer(
                 low=-self.init_scale, high=self.init_scale))
 
-    def _build_once(self, input, label, init_hidden, init_cell):
+    def build_once(self, input, label, init_hidden, init_cell):
         pass
 
     def forward(self, input, label, init_hidden, init_cell):
@@ -260,13 +259,13 @@ class TestDygraphPtbRnn(unittest.TestCase):
                                                             init_cell)
                 if i == 0:
                     for param in ptb_model.parameters():
-                        dy_param_init[param.name] = param._numpy()
-                dy_loss._backward()
+                        dy_param_init[param.name] = param.numpy()
+                dy_loss.backward()
                 sgd.minimize(dy_loss)
                 ptb_model.clear_gradients()
                 if i == batch_num - 1:
                     for param in ptb_model.parameters():
-                        dy_param_updated[param.name] = param._numpy()
+                        dy_param_updated[param.name] = param.numpy()
 
         with new_program_scope():
             fluid.default_startup_program().random_seed = seed
@@ -334,11 +333,11 @@ class TestDygraphPtbRnn(unittest.TestCase):
                         static_param_updated[static_param_name_list[k -
                                                                     3]] = out[k]
 
-        self.assertTrue(np.array_equal(static_loss_value, dy_loss._numpy()))
+        self.assertTrue(np.array_equal(static_loss_value, dy_loss.numpy()))
         self.assertTrue(
-            np.array_equal(static_last_cell_value, last_cell._numpy()))
+            np.array_equal(static_last_cell_value, last_cell.numpy()))
         self.assertTrue(
-            np.array_equal(static_last_hidden_value, last_hidden._numpy()))
+            np.array_equal(static_last_hidden_value, last_hidden.numpy()))
         for key, value in six.iteritems(static_param_init):
             self.assertTrue(np.array_equal(value, dy_param_init[key]))
         for key, value in six.iteritems(static_param_updated):

python/paddle/fluid/tests/unittests/test_imperative_resnet.py

@@ -21,7 +21,7 @@ import paddle
 import paddle.fluid as fluid
 from paddle.fluid import core
 from paddle.fluid.layer_helper import LayerHelper
-from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, FC
+from paddle.fluid import Conv2D, Pool2D, BatchNorm, FC
 from paddle.fluid.dygraph.base import to_variable
 from test_imperative_base import new_program_scope
 
@@ -68,7 +68,7 @@ def optimizer_setting(params):
     return optimizer
 
 
-class ConvBNLayer(fluid.dygraph.Layer):
+class ConvBNLayer(fluid.Layer):
     def __init__(self,
                  name_scope,
                  num_channels,
@@ -99,7 +99,7 @@ class ConvBNLayer(fluid.dygraph.Layer):
         return y
 
 
-class BottleneckBlock(fluid.dygraph.Layer):
+class BottleneckBlock(fluid.Layer):
     def __init__(self,
                  name_scope,
                  num_channels,
@@ -156,7 +156,7 @@ class BottleneckBlock(fluid.dygraph.Layer):
         return layer_helper.append_activation(y)
 
 
-class ResNet(fluid.dygraph.Layer):
+class ResNet(fluid.Layer):
     def __init__(self, name_scope, layers=50, class_dim=102):
         super(ResNet, self).__init__(name_scope)
 
@@ -247,7 +247,7 @@ class TestDygraphResnet(unittest.TestCase):
 
             dy_param_init_value = {}
             for param in resnet.parameters():
-                dy_param_init_value[param.name] = param._numpy()
+                dy_param_init_value[param.name] = param.numpy()
 
             for batch_id, data in enumerate(train_reader()):
                 if batch_id >= batch_num:
@@ -260,20 +260,20 @@ class TestDygraphResnet(unittest.TestCase):
 
                 img = to_variable(dy_x_data)
                 label = to_variable(y_data)
-                label._stop_gradient = True
+                label.stop_gradient = True
 
                 out = resnet(img)
                 loss = fluid.layers.cross_entropy(input=out, label=label)
                 avg_loss = fluid.layers.mean(x=loss)
 
-                dy_out = avg_loss._numpy()
+                dy_out = avg_loss.numpy()
 
                 if batch_id == 0:
                     for param in resnet.parameters():
                         if param.name not in dy_param_init_value:
-                            dy_param_init_value[param.name] = param._numpy()
+                            dy_param_init_value[param.name] = param.numpy()
 
-                avg_loss._backward()
+                avg_loss.backward()
 
                 dy_grad_value = {}
                 for param in resnet.parameters():
@@ -288,7 +288,7 @@ class TestDygraphResnet(unittest.TestCase):
 
                 dy_param_value = {}
                 for param in resnet.parameters():
-                    dy_param_value[param.name] = param._numpy()
+                    dy_param_value[param.name] = param.numpy()
 
         with new_program_scope():
             fluid.default_startup_program().random_seed = seed

python/paddle/fluid/tests/unittests/test_imperative_se_resnext.py

@@ -333,7 +333,7 @@ class TestImperativeResneXt(unittest.TestCase):
 
             dy_param_init_value = {}
             for param in se_resnext.parameters():
-                dy_param_init_value[param.name] = param._numpy()
+                dy_param_init_value[param.name] = param.numpy()
             for epoch_id in range(epoch_num):
                 for batch_id, data in enumerate(train_reader()):
 
@@ -349,19 +349,19 @@ class TestImperativeResneXt(unittest.TestCase):
 
                     img = to_variable(dy_x_data)
                     label = to_variable(y_data)
-                    label._stop_gradient = True
+                    label.stop_gradient = True
 
                     out = se_resnext(img)
                     loss = fluid.layers.cross_entropy(input=out, label=label)
                     avg_loss = fluid.layers.mean(x=loss)
 
-                    dy_out = avg_loss._numpy()
+                    dy_out = avg_loss.numpy()
 
                     if batch_id == 0:
                         for param in se_resnext.parameters():
                             if param.name not in dy_param_init_value:
-                                dy_param_init_value[param.name] = param._numpy()
-                    avg_loss._backward()
+                                dy_param_init_value[param.name] = param.numpy()
+                    avg_loss.backward()
 
                     #dy_grad_value = {}
                     #for param in se_resnext.parameters():
@@ -375,7 +375,7 @@ class TestImperativeResneXt(unittest.TestCase):
 
                     dy_param_value = {}
                     for param in se_resnext.parameters():
-                        dy_param_value[param.name] = param._numpy()
+                        dy_param_value[param.name] = param.numpy()
 
         with new_program_scope():
             fluid.default_startup_program().random_seed = seed

python/paddle/fluid/tests/unittests/test_imperative_transformer.py

@@ -16,7 +16,8 @@ from __future__ import print_function
 
 import unittest
 import paddle.fluid as fluid
-from paddle.fluid.dygraph import Embedding, LayerNorm, FC, to_variable, Layer, guard
+from paddle.fluid import Embedding, LayerNorm, FC, Layer
+from paddle.fluid.dygraph import to_variable, guard
 from test_imperative_base import new_program_scope
 from paddle.fluid import core
 import numpy as np
@@ -116,7 +117,7 @@ class ModelHyperParams(object):
     # to process after each sub-layer
     postprocess_cmd = "da"  # dropout + residual connection
     # random seed used in dropout for CE.
-    dropout_seed = 1
+    dropout_seed = None
     # the flag indicating whether to share embedding and softmax weights.
     # vocabularies in source and target should be same for weight sharing.
     weight_sharing = True
@@ -166,15 +167,21 @@ def create_data(is_static=False):
         ]
     else:
         enc_inputs = [
-            to_variable(src_word_np), to_variable(src_pos_np),
-            to_variable(src_slf_attn_bias_np)
+            to_variable(
+                src_word_np, name='src_word'), to_variable(
+                    src_pos_np, name='src_pos'), to_variable(
+                        src_slf_attn_bias_np, name='src_slf_attn_bias')
         ]
         dec_inputs = [
-            to_variable(trg_word_np), to_variable(trg_pos_np),
-            to_variable(trg_slf_attn_bias_np), to_variable(trg_src_attn_bias_np)
+            to_variable(
+                trg_word_np, name='trg_word'), to_variable(
+                    trg_pos_np, name='trg_pos'), to_variable(
+                        trg_slf_attn_bias_np, name='trg_slf_attn_bias'),
+            to_variable(
+                trg_src_attn_bias_np, name='trg_src_attn_bias')
         ]
-        label = to_variable(lbl_word_np)
-        weight = to_variable(lbl_weight_np)
+        label = to_variable(lbl_word_np, name='lbl_word')
+        weight = to_variable(lbl_weight_np, name='lbl_weight')
         return enc_inputs, dec_inputs, label, weight
 
 
@@ -211,7 +218,7 @@ def make_all_inputs(input_fields):
 # The placeholder for batch_size in compile time. Must be -1 currently to be
 # consistent with some ops' infer-shape output in compile time, such as the
 # sequence_expand op used in beamsearch decoder.
-batch_size = 32
+batch_size = -1
 # The placeholder for squence length in compile time.
 seq_len = ModelHyperParams.max_length
 # Here list the data shapes and data types of all inputs.
@@ -305,54 +312,40 @@ sync = False
 # how many batches we use
 batch_num = 5
 
-np.random.seed = 1
+np.random.seed = 90
 src_word_np = np.random.randint(
     1,
     ModelHyperParams.src_vocab_size - 1,
-    size=(batch_size, seq_len, 1),
+    size=(TrainTaskConfig.batch_size, seq_len, 1),
     dtype='int64')
 src_pos_np = np.random.randint(
-    1, seq_len, size=(batch_size, seq_len, 1), dtype='int64')
-src_slf_attn_bias_np = np.random.randn(batch_size, ModelHyperParams.n_head,
-                                       seq_len, seq_len).astype('float32')
+    1, seq_len, size=(TrainTaskConfig.batch_size, seq_len, 1), dtype='int64')
+src_slf_attn_bias_np = np.random.randn(TrainTaskConfig.batch_size,
+                                       ModelHyperParams.n_head, seq_len,
+                                       seq_len).astype('float32')
 
 trg_word_np = np.random.randint(
     1,
     ModelHyperParams.src_vocab_size - 1,
-    size=(batch_size, seq_len, 1),
+    size=(TrainTaskConfig.batch_size, seq_len, 1),
     dtype='int64')
 trg_pos_np = np.random.randint(
-    1, seq_len, size=(batch_size, seq_len, 1), dtype='int64')
-trg_slf_attn_bias_np = np.random.randn(batch_size, ModelHyperParams.n_head,
-                                       seq_len, seq_len).astype('float32')
-trg_src_attn_bias_np = np.random.randn(batch_size, ModelHyperParams.n_head,
-                                       seq_len, seq_len).astype('float32')
+    1, seq_len, size=(TrainTaskConfig.batch_size, seq_len, 1), dtype='int64')
+trg_slf_attn_bias_np = np.random.randn(TrainTaskConfig.batch_size,
+                                       ModelHyperParams.n_head, seq_len,
+                                       seq_len).astype('float32')
+trg_src_attn_bias_np = np.random.randn(TrainTaskConfig.batch_size,
+                                       ModelHyperParams.n_head, seq_len,
+                                       seq_len).astype('float32')
 
 lbl_word_np = np.random.randint(
     1,
     ModelHyperParams.src_vocab_size - 1,
-    size=(batch_size * seq_len, 1),
+    size=(TrainTaskConfig.batch_size * seq_len, 1),
     dtype='int64')
-lbl_weight_np = np.random.randn(batch_size * seq_len, 1).astype('float32')
-
-# np.random.seed = 1
-# src_word_np = np.arange(0, 10).reshape([batch_size, seq_len, 1]).astype('int64')
-# src_pos_np = np.random.randint(
-#     1, seq_len, size=(batch_size, seq_len, 1), dtype='int64')
-# src_slf_attn_bias_np = np.random.randn(batch_size, ModelHyperParams.n_head,
-#                                        seq_len, seq_len).astype('float32')
-#
-# trg_word_np =  np.arange(0, 10).reshape([batch_size, seq_len, 1]).astype('int64')
-# trg_pos_np = np.random.randint(
-#     1, seq_len, size=(batch_size, seq_len, 1), dtype='int64')
-# trg_slf_attn_bias_np = np.random.randn(batch_size, ModelHyperParams.n_head,
-#                                        seq_len, seq_len).astype('float32')
-# trg_src_attn_bias_np = np.random.randn(batch_size, ModelHyperParams.n_head,
-#                                        seq_len, seq_len).astype('float32')
-#
-# lbl_word_np =  np.arange(0, 10).reshape([batch_size * seq_len, 1]).astype('int64')
-# lbl_weight_np = np.random.randn(batch_size * seq_len, 1).astype('float32')
-#
+lbl_weight_np = np.random.randn(TrainTaskConfig.batch_size * seq_len,
+                                1).astype('float32')
+
 pos_inp1 = position_encoding_init(ModelHyperParams.max_length,
                                   ModelHyperParams.d_model)
 pos_inp2 = position_encoding_init(ModelHyperParams.max_length,
@@ -466,7 +459,7 @@ class MultiHeadAttentionLayer(Layer):
             x=v, shape=[0, 0, self._n_head, self._d_value], inplace=False)
         transpose_v = fluid.layers.transpose(x=reshaped_v, perm=[0, 2, 1, 3])
 
-        #scale dot product attention
+        # scale dot product attention
         product = fluid.layers.matmul(
             x=transpose_q,
             y=transpose_k,
@@ -739,7 +732,7 @@ class DecoderSubLayer(Layer):
         enc_attn_output_pp = self._multihead_attention_layer2(
             pre_process_rlt2, enc_output, enc_output, dec_enc_attn_bias)
         enc_attn_output = self._post_process_layer2(
-            slf_attn_output, enc_attn_output_pp, self._postprocess_cmd,
+            slf_attn_output_pp, enc_attn_output_pp, self._postprocess_cmd,
             self._prepostprcess_dropout)
         pre_process_rlt3 = self._pre_process_layer3(None, enc_attn_output,
                                                     self._preprocess_cmd,
@@ -990,16 +983,18 @@ class TestDygraphTransformer(unittest.TestCase):
                 enc_inputs, dec_inputs, label, weights = create_data()
                 dy_sum_cost, dy_avg_cost, dy_predict, dy_token_num = transformer(
                     enc_inputs, dec_inputs, label, weights)
+
                 if i == 0:
                     for param in transformer.parameters():
-                        dy_param_init[param.name] = param._numpy()
+                        dy_param_init[param.name] = param.numpy()
 
-                dy_avg_cost._backward()
+                dy_avg_cost.backward()
                 optimizer.minimize(dy_avg_cost)
                 transformer.clear_gradients()
+
                 if i == batch_num - 1:
                     for param in transformer.parameters():
-                        dy_param_updated[param.name] = param._numpy()
+                        dy_param_updated[param.name] = param.numpy()
 
         with new_program_scope():
             fluid.default_startup_program().random_seed = seed
@@ -1043,7 +1038,6 @@ class TestDygraphTransformer(unittest.TestCase):
             static_param_name_list = list()
             static_sum_cost, static_avg_cost, static_predict, static_token_num = transformer(
                 enc_inputs, dec_inputs, label, weights)
-
             optimizer.minimize(static_avg_cost)
             for param in transformer.parameters():
                 static_param_name_list.append(param.name)
@@ -1061,8 +1055,8 @@ class TestDygraphTransformer(unittest.TestCase):
                     static_sum_cost, static_avg_cost, static_predict,
                     static_token_num
                 ]
-                fetch_list.extend(static_param_name_list)
 
+                fetch_list.extend(static_param_name_list)
                 out = exe.run(fluid.default_main_program(),
                               feed=feed_dict,
                               fetch_list=fetch_list)
@@ -1076,13 +1070,14 @@ class TestDygraphTransformer(unittest.TestCase):
                                                                     4]] = out[k]
 
         self.assertTrue(
-            np.array_equal(static_avg_cost_value, dy_avg_cost._numpy()))
+            np.array_equal(static_avg_cost_value, dy_avg_cost.numpy()))
         self.assertTrue(
-            np.array_equal(static_sum_cost_value, dy_sum_cost._numpy()))
+            np.array_equal(static_sum_cost_value, dy_sum_cost.numpy()))
         self.assertTrue(
-            np.array_equal(static_predict_value, dy_predict._numpy()))
+            np.array_equal(static_predict_value, dy_predict.numpy()))
         self.assertTrue(
-            np.array_equal(static_token_num_value, dy_token_num._numpy()))
+            np.array_equal(static_token_num_value, dy_token_num.numpy()))
+
         for key, value in six.iteritems(static_param_init):
             self.assertTrue(np.array_equal(value, dy_param_init[key]))
         for key, value in six.iteritems(static_param_updated):

python/paddle/fluid/tests/unittests/test_layers.py

@@ -114,7 +114,7 @@ class TestLayer(LayerTest):
             dy_ret = fc2(ret)
 
         self.assertTrue(np.array_equal(static_ret, static_ret2))
-        self.assertTrue(np.array_equal(static_ret, dy_ret._numpy()))
+        self.assertTrue(np.array_equal(static_ret, dy_ret.numpy()))
 
     def test_layer_norm(self):
         inp = np.ones([3, 32, 32], dtype='float32')
@@ -142,7 +142,7 @@ class TestLayer(LayerTest):
             dy_ret = lm(base.to_variable(inp))
 
         self.assertTrue(np.allclose(static_ret, static_ret2))
-        self.assertTrue(np.allclose(dy_ret._numpy(), static_ret2))
+        self.assertTrue(np.allclose(dy_ret.numpy(), static_ret2))
 
     def test_relu(self):
         with self.static_graph():
@@ -156,7 +156,7 @@ class TestLayer(LayerTest):
             t = np.ones([3, 3], dtype='float32')
             dy_ret = layers.relu(base.to_variable(t))
 
-        self.assertTrue(np.allclose(static_ret, dy_ret._numpy()))
+        self.assertTrue(np.allclose(static_ret, dy_ret.numpy()))
 
     def test_matmul(self):
         with self.static_graph():
@@ -177,7 +177,7 @@ class TestLayer(LayerTest):
             t2 = np.ones([3, 3], dtype='float32')
             dy_ret = layers.matmul(base.to_variable(t), base.to_variable(t2))
 
-        self.assertTrue(np.allclose(static_ret, dy_ret._numpy()))
+        self.assertTrue(np.allclose(static_ret, dy_ret.numpy()))
 
     def test_conv2d(self):
         with self.static_graph():
@@ -204,7 +204,7 @@ class TestLayer(LayerTest):
                 'conv2d', num_channels=3, num_filters=3, filter_size=[2, 2])
             dy_ret = conv2d(base.to_variable(images))
 
-        self.assertTrue(np.allclose(static_ret, dy_ret._numpy()))
+        self.assertTrue(np.allclose(static_ret, dy_ret.numpy()))
         self.assertTrue(np.allclose(static_ret, static_ret2))
 
     def test_gru_unit(self):
@@ -246,7 +246,7 @@ class TestLayer(LayerTest):
 
         for i in range(len(static_ret)):
             self.assertTrue(np.allclose(static_ret[i], static_ret2[i]))
-            self.assertTrue(np.allclose(static_ret[i], dy_ret[i]._numpy()))
+            self.assertTrue(np.allclose(static_ret[i], dy_ret[i].numpy()))
 
     def test_elementwise_math(self):
         n = np.ones([3, 3], dtype='float32')
@@ -288,8 +288,8 @@ class TestLayer(LayerTest):
             ret = layers.elementwise_sub(ret, n5)
             dy_ret = layers.elementwise_mul(ret, n6)
         self.assertTrue(
-            np.allclose(static_ret, dy_ret._numpy()),
-            '%s vs %s' % (static_ret, dy_ret._numpy()))
+            np.allclose(static_ret, dy_ret.numpy()),
+            '%s vs %s' % (static_ret, dy_ret.numpy()))
 
     def test_elementwise_minmax(self):
         n = np.ones([3, 3], dtype='float32')
@@ -299,8 +299,8 @@ class TestLayer(LayerTest):
             min_ret = layers.elementwise_min(n, n2)
             max_ret = layers.elementwise_max(n, n2)
 
-        self.assertTrue(np.allclose(n, min_ret._numpy()))
-        self.assertTrue(np.allclose(n2, max_ret._numpy()))
+        self.assertTrue(np.allclose(n, min_ret.numpy()))
+        self.assertTrue(np.allclose(n2, max_ret.numpy()))
 
     def test_sequence_conv(self):
         inp_np = np.arange(12).reshape([3, 4]).astype('float32')
@@ -367,7 +367,7 @@ class TestLayer(LayerTest):
                 'conv2d_transpose', num_filters=10, output_size=28)
             dy_rlt = conv2d_transpose(base.to_variable(inp_np))
         self.assertTrue(np.allclose(static_rlt2, static_rlt))
-        self.assertTrue(np.allclose(dy_rlt._numpy(), static_rlt))
+        self.assertTrue(np.allclose(dy_rlt.numpy(), static_rlt))
 
     def test_bilinear_tensor_product(self):
         inp_np_x = np.array([[1, 2, 3]]).astype('float32')
@@ -410,7 +410,7 @@ class TestLayer(LayerTest):
             dy_rlt = btp(base.to_variable(inp_np_x), base.to_variable(inp_np_y))
 
         self.assertTrue(np.allclose(static_rlt2, static_rlt))
-        self.assertTrue(np.allclose(dy_rlt._numpy(), static_rlt))
+        self.assertTrue(np.allclose(dy_rlt.numpy(), static_rlt))
 
     def test_prelu(self):
         inp_np = np.ones([5, 200, 100, 100]).astype('float32')
@@ -451,7 +451,7 @@ class TestLayer(LayerTest):
             dy_rlt = prelu(base.to_variable(inp_np))
 
         self.assertTrue(np.allclose(static_rlt2, static_rlt))
-        self.assertTrue(np.allclose(dy_rlt._numpy(), static_rlt))
+        self.assertTrue(np.allclose(dy_rlt.numpy(), static_rlt))
 
     def test_embeding(self):
         inp_word = np.array([[[1]]]).astype('int64')
@@ -484,7 +484,7 @@ class TestLayer(LayerTest):
             static_rlt3 = emb2(base.to_variable(inp_word))
 
         self.assertTrue(np.allclose(static_rlt2, static_rlt))
-        self.assertTrue(np.allclose(static_rlt3._numpy(), static_rlt))
+        self.assertTrue(np.allclose(static_rlt3.numpy(), static_rlt))
 
     def test_nce(self):
         window_size = 5
@@ -598,7 +598,7 @@ class TestLayer(LayerTest):
             nce_loss3 = nce(embs3, words[label_word])
 
         self.assertTrue(np.allclose(static_rlt2, static_rlt))
-        self.assertTrue(np.allclose(nce_loss3._numpy(), static_rlt))
+        self.assertTrue(np.allclose(nce_loss3.numpy(), static_rlt))
 
     def test_conv3d(self):
         with self.static_graph():
@@ -625,7 +625,7 @@ class TestLayer(LayerTest):
             conv3d = nn.Conv3D('conv3d', num_filters=3, filter_size=2)
             dy_ret = conv3d(base.to_variable(images))
 
-        self.assertTrue(np.allclose(static_ret, dy_ret._numpy()))
+        self.assertTrue(np.allclose(static_ret, dy_ret.numpy()))
         self.assertTrue(np.allclose(static_ret, static_ret2))
 
     def test_row_conv(self):
@@ -719,7 +719,7 @@ class TestLayer(LayerTest):
             groupNorm = nn.GroupNorm('GroupNorm', groups=2)
             dy_ret = groupNorm(base.to_variable(input))
 
-        self.assertTrue(np.allclose(static_ret, dy_ret._numpy()))
+        self.assertTrue(np.allclose(static_ret, dy_ret.numpy()))
         self.assertTrue(np.allclose(static_ret, static_ret2))
 
     def test_spectral_norm(self):
@@ -769,7 +769,7 @@ class TestLayer(LayerTest):
             spectralNorm = nn.SpectralNorm('SpectralNorm', dim=1, power_iters=2)
             dy_ret = spectralNorm(base.to_variable(input))
 
-        self.assertTrue(np.allclose(static_ret, dy_ret._numpy()))
+        self.assertTrue(np.allclose(static_ret, dy_ret.numpy()))
         self.assertTrue(np.allclose(static_ret, static_ret2))
 
     def test_tree_conv(self):
@@ -842,7 +842,7 @@ class TestLayer(LayerTest):
             dy_ret = treeConv(base.to_variable(vectors), base.to_variable(adj))
 
         self.assertTrue(np.allclose(static_ret, static_ret2))
-        self.assertTrue(np.allclose(static_ret, dy_ret._numpy()))
+        self.assertTrue(np.allclose(static_ret, dy_ret.numpy()))
 
     def test_conv3d_transpose(self):
         input_array = np.arange(0, 48).reshape(
@@ -872,7 +872,7 @@ class TestLayer(LayerTest):
                 use_cudnn=False)
             dy_rlt = conv3d_transpose(base.to_variable(input_array))
         self.assertTrue(np.allclose(static_rlt2, static_rlt))
-        self.assertTrue(np.allclose(dy_rlt._numpy(), static_rlt))
+        self.assertTrue(np.allclose(dy_rlt.numpy(), static_rlt))
 
 
 class TestBook(LayerTest):
@@ -907,7 +907,7 @@ class TestBook(LayerTest):
                 if isinstance(dy_result, tuple):
                     dy_result = dy_result[0]
 
-        self.assertTrue(np.array_equal(static_result[0], dy_result._numpy()))
+        self.assertTrue(np.array_equal(static_result[0], dy_result.numpy()))
 
     def _get_np_data(self, shape, dtype, append_batch_size=True):
         np.random.seed(self.seed)
@@ -1925,6 +1925,13 @@ class TestBook(LayerTest):
             out = layers.flatten(x, axis=1, name="flatten")
             return (out)
 
+    def test_linspace(self):
+        program = Program()
+        with program_guard(program):
+            out = layers.linspace(20, 10, 5, 'float64')
+            self.assertIsNotNone(out)
+        print(str(program))
+
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/test_linspace.py

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestLinspaceOpCommonCase(OpTest):
+    def setUp(self):
+        self.op_type = "linspace"
+        dtype = 'float32'
+        self.inputs = {
+            'Start': np.array([0]).astype(dtype),
+            'Stop': np.array([10]).astype(dtype),
+            'Num': np.array([11]).astype('int32')
+        }
+
+        self.outputs = {'Out': np.arange(0, 11).astype(dtype)}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestLinspaceOpReverseCase(OpTest):
+    def setUp(self):
+        self.op_type = "linspace"
+        dtype = 'float32'
+        self.inputs = {
+            'Start': np.array([10]).astype(dtype),
+            'Stop': np.array([0]).astype(dtype),
+            'Num': np.array([11]).astype('int32')
+        }
+
+        self.outputs = {'Out': np.arange(10, -1, -1).astype(dtype)}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestLinspaceOpNumOneCase(OpTest):
+    def setUp(self):
+        self.op_type = "linspace"
+        dtype = 'float32'
+        self.inputs = {
+            'Start': np.array([10]).astype(dtype),
+            'Stop': np.array([0]).astype(dtype),
+            'Num': np.array([1]).astype('int32')
+        }
+
+        self.outputs = {'Out': np.array(10, dtype=dtype)}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/test_nearest_interp_op.py

@@ -73,7 +73,14 @@ class TestNearestInterpOp(OpTest):
         self.op_type = "nearest_interp"
         input_np = np.random.random(self.input_shape).astype("float32")
 
-        output_np = nearest_neighbor_interp_np(input_np, self.out_h, self.out_w,
+        if self.scale > 0:
+            out_h = int(self.input_shape[2] * self.scale)
+            out_w = int(self.input_shape[3] * self.scale)
+        else:
+            out_h = self.out_h
+            out_w = self.out_w
+
+        output_np = nearest_neighbor_interp_np(input_np, out_h, out_w,
                                                self.out_size, self.actual_shape,
                                                self.align_corners)
         self.inputs = {'X': input_np}
@@ -84,6 +91,7 @@ class TestNearestInterpOp(OpTest):
         self.attrs = {
             'out_h': self.out_h,
             'out_w': self.out_w,
+            'scale': self.scale,
             'interp_method': self.interp_method,
             'align_corners': self.align_corners,
         }
@@ -100,6 +108,7 @@ class TestNearestInterpOp(OpTest):
         self.input_shape = [2, 3, 4, 4]
         self.out_h = 2
         self.out_w = 2
+        self.scale = 0.
         self.out_size = np.array([3, 3]).astype("int32")
         self.align_corners = True
 
@@ -110,6 +119,7 @@ class TestNearestNeighborInterpCase1(TestNearestInterpOp):
         self.input_shape = [4, 1, 7, 8]
         self.out_h = 1
         self.out_w = 1
+        self.scale = 0.
         self.align_corners = True
 
 
@@ -119,6 +129,7 @@ class TestNearestNeighborInterpCase2(TestNearestInterpOp):
         self.input_shape = [3, 3, 9, 6]
         self.out_h = 12
         self.out_w = 12
+        self.scale = 0.
         self.align_corners = True
 
 
@@ -128,6 +139,7 @@ class TestNearestNeighborInterpCase3(TestNearestInterpOp):
         self.input_shape = [1, 1, 128, 64]
         self.out_h = 64
         self.out_w = 128
+        self.scale = 0.
         self.align_corners = True
 
 
@@ -137,6 +149,7 @@ class TestNearestNeighborInterpCase4(TestNearestInterpOp):
         self.input_shape = [4, 1, 7, 8]
         self.out_h = 1
         self.out_w = 1
+        self.scale = 0.
         self.out_size = np.array([2, 2]).astype("int32")
         self.align_corners = True
 
@@ -147,6 +160,7 @@ class TestNearestNeighborInterpCase5(TestNearestInterpOp):
         self.input_shape = [3, 3, 9, 6]
         self.out_h = 12
         self.out_w = 12
+        self.scale = 0.
         self.out_size = np.array([11, 11]).astype("int32")
         self.align_corners = True
 
@@ -157,6 +171,7 @@ class TestNearestNeighborInterpCase6(TestNearestInterpOp):
         self.input_shape = [1, 1, 128, 64]
         self.out_h = 64
         self.out_w = 128
+        self.scale = 0.
         self.out_size = np.array([65, 129]).astype("int32")
         self.align_corners = True
 
@@ -167,6 +182,7 @@ class TestNearestNeighborInterpActualShape(TestNearestInterpOp):
         self.input_shape = [3, 2, 32, 16]
         self.out_h = 64
         self.out_w = 32
+        self.scale = 0.
         self.out_size = np.array([66, 40]).astype("int32")
         self.align_corners = True
 
@@ -179,7 +195,15 @@ class TestNearestInterpOpUint8(OpTest):
         self.op_type = "nearest_interp"
         input_np = np.random.randint(
             low=0, high=256, size=self.input_shape).astype("uint8")
-        output_np = nearest_neighbor_interp_np(input_np, self.out_h, self.out_w,
+
+        if self.scale > 0:
+            out_h = int(self.input_shape[2] * self.scale)
+            out_w = int(self.input_shape[3] * self.scale)
+        else:
+            out_h = self.out_h
+            out_w = self.out_w
+
+        output_np = nearest_neighbor_interp_np(input_np, out_h, out_w,
                                                self.out_size, self.actual_shape,
                                                self.align_corners)
         self.inputs = {'X': input_np}
@@ -188,6 +212,7 @@ class TestNearestInterpOpUint8(OpTest):
         self.attrs = {
             'out_h': self.out_h,
             'out_w': self.out_w,
+            'scale': self.scale,
             'interp_method': self.interp_method,
             'align_corners': self.align_corners
         }
@@ -201,6 +226,7 @@ class TestNearestInterpOpUint8(OpTest):
         self.input_shape = [1, 3, 9, 6]
         self.out_h = 10
         self.out_w = 9
+        self.scale = 0.
         self.align_corners = True
 
 
@@ -210,6 +236,7 @@ class TestNearestNeighborInterpCase1Uint8(TestNearestInterpOpUint8):
         self.input_shape = [2, 3, 128, 64]
         self.out_h = 120
         self.out_w = 50
+        self.scale = 0.
         self.align_corners = True
 
 
@@ -219,6 +246,7 @@ class TestNearestNeighborInterpCase2Uint8(TestNearestInterpOpUint8):
         self.input_shape = [4, 1, 7, 8]
         self.out_h = 5
         self.out_w = 13
+        self.scale = 0.
         self.out_size = np.array([6, 15]).astype("int32")
         self.align_corners = True
 
@@ -228,5 +256,38 @@ class TestNearestInterpWithoutCorners(TestNearestInterpOp):
         self.align_corners = False
 
 
+class TestNearestNeighborInterpScale1(TestNearestInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [3, 2, 32, 16]
+        self.out_h = 64
+        self.out_w = 32
+        self.scale = 2.
+        self.out_size = np.array([66, 40]).astype("int32")
+        self.align_corners = True
+
+
+class TestNearestNeighborInterpScale2(TestNearestInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [3, 2, 32, 16]
+        self.out_h = 64
+        self.out_w = 32
+        self.scale = 1.5
+        self.out_size = np.array([66, 40]).astype("int32")
+        self.align_corners = True
+
+
+class TestNearestNeighborInterpScale3(TestNearestInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [3, 2, 32, 16]
+        self.out_h = 64
+        self.out_w = 32
+        self.scale = 1.
+        self.out_size = np.array([66, 40]).astype("int32")
+        self.align_corners = True
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/test_parallel_executor_seresnext.py

@@ -29,7 +29,7 @@ import unittest
 import math
 import numpy as np
 from functools import partial
-
+os.environ['CPU_NUM'] = str(4)
 # FIXME(zcd): If the neural net has dropout_op, the output of ParallelExecutor
 # and Executor is different. Because, for ParallelExecutor, the dropout_op of
 # the neural net will be copied N copies(N is the number of device). This will
@@ -113,7 +113,6 @@ def bottleneck_block(input, num_filters, stride, cardinality, reduction_ratio):
     return fluid.layers.elementwise_add(x=short, y=scale, act='relu')
 
 
-batch_size = 12
 img_shape = [3, 224, 224]
 
 
@@ -181,43 +180,84 @@ def optimizer(learning_rate=0.01):
     return optimizer
 
 
+def _batch_size():
+    return 12
+
+
+def _iter(use_cuda):
+    if use_cuda:
+        return 10
+    return 2
+
+
+gpu_img, gpu_label = init_data(
+    batch_size=_batch_size(), img_shape=img_shape, label_range=999)
+cpu_img, cpu_label = init_data(
+    batch_size=_batch_size(), img_shape=img_shape, label_range=999)
+feed_dict_gpu = {"image": gpu_img, "label": gpu_label}
+feed_dict_cpu = {"image": cpu_img, "label": cpu_label}
+model = SE_ResNeXt50Small
+
+
+def _feed_dict(use_cuda):
+    if use_cuda:
+        return feed_dict_gpu
+    return feed_dict_cpu
+
+
+def _get_result_of_origin_model(use_cuda):
+    global remove_bn
+    global remove_dropout
+    remove_bn = True
+    remove_dropout = True
+    first_loss, last_loss = TestParallelExecutorBase.check_network_convergence(
+        model,
+        feed_dict=_feed_dict(use_cuda),
+        iter=_iter(use_cuda),
+        batch_size=_batch_size(),
+        use_cuda=use_cuda,
+        use_reduce=False,
+        optimizer=optimizer)
+
+    return first_loss, last_loss
+
+
+origin_cpu_first_loss, origin_cpu_last_loss = _get_result_of_origin_model(False)
+if core.is_compiled_with_cuda():
+    origin_gpu_first_loss, origin_gpu_last_loss = _get_result_of_origin_model(
+        True)
+
+
+def _get_origin_result(use_cuda):
+    if use_cuda:
+        assert core.is_compiled_with_cuda(), "Doesn't compiled with CUDA."
+        return origin_gpu_first_loss, origin_gpu_last_loss
+    return origin_cpu_first_loss, origin_cpu_last_loss
+
+
 class TestResnet(TestParallelExecutorBase):
-    @classmethod
-    def setUpClass(cls):
-        os.environ['CPU_NUM'] = str(4)
-        global remove_dropout
-        global remove_bn
-        remove_dropout = False
-        remove_bn = False
-
-    def _compare_reduce_and_allreduce(self,
-                                      model,
-                                      use_cuda,
-                                      iter=20,
-                                      delta2=1e-5):
+    def _compare_reduce_and_allreduce(self, use_cuda, delta2=1e-5):
         if use_cuda and not core.is_compiled_with_cuda():
             return
 
         global remove_bn
+        global remove_dropout
         remove_bn = True
+        remove_dropout = True
 
-        img, label = init_data(
-            batch_size=batch_size, img_shape=img_shape, label_range=999)
         all_reduce_first_loss, all_reduce_last_loss = self.check_network_convergence(
             model,
-            feed_dict={"image": img,
-                       "label": label},
-            iter=iter,
-            batch_size=batch_size,
+            feed_dict=_feed_dict(use_cuda),
+            iter=_iter(use_cuda),
+            batch_size=_batch_size(),
             use_cuda=use_cuda,
             use_reduce=False,
             optimizer=optimizer)
         reduce_first_loss, reduce_last_loss = self.check_network_convergence(
             model,
-            feed_dict={"image": img,
-                       "label": label},
-            iter=iter,
-            batch_size=batch_size,
+            feed_dict=_feed_dict(use_cuda),
+            iter=_iter(use_cuda),
+            batch_size=_batch_size(),
             use_cuda=use_cuda,
             use_reduce=True,
             optimizer=optimizer)
@@ -232,10 +272,9 @@ class TestResnet(TestParallelExecutorBase):
 
         all_reduce_first_loss_seq, all_reduce_last_loss_seq = self.check_network_convergence(
             model,
-            feed_dict={"image": img,
-                       "label": label},
-            iter=iter,
-            batch_size=batch_size,
+            feed_dict=_feed_dict(use_cuda),
+            iter=_iter(use_cuda),
+            batch_size=_batch_size(),
             use_cuda=use_cuda,
             use_reduce=False,
             optimizer=optimizer,
@@ -243,10 +282,9 @@ class TestResnet(TestParallelExecutorBase):
 
         reduce_first_loss_seq, reduce_last_loss_seq = self.check_network_convergence(
             model,
-            feed_dict={"image": img,
-                       "label": label},
-            iter=iter,
-            batch_size=batch_size,
+            feed_dict=_feed_dict(use_cuda),
+            iter=_iter(use_cuda),
+            batch_size=_batch_size(),
             use_cuda=use_cuda,
             use_reduce=True,
             optimizer=optimizer,
@@ -267,37 +305,28 @@ class TestResnet(TestParallelExecutorBase):
         for loss in zip(all_reduce_last_loss_seq, reduce_last_loss_seq):
             self.assertAlmostEquals(loss[0], loss[1], delta=delta2)
 
-    def _check_resnet_convergence(self,
-                                  model,
-                                  check_func_1,
-                                  check_func_2,
-                                  use_cuda,
-                                  iter=20,
-                                  delta2=1e-5,
-                                  compare_seperately=True):
+    def _compare_result_with_origin_model(self,
+                                          get_origin_result,
+                                          check_func_2,
+                                          use_cuda,
+                                          delta2=1e-5,
+                                          compare_seperately=True,
+                                          rm_drop_out=False,
+                                          rm_bn=False):
         if use_cuda and not core.is_compiled_with_cuda():
             return
 
-        global remove_dropout
         global remove_bn
-        remove_dropout = True
-        remove_bn = True
+        global remove_dropout
+        remove_bn = rm_bn or use_cuda
+        remove_dropout = rm_drop_out
 
-        img, label = init_data(
-            batch_size=batch_size, img_shape=img_shape, label_range=999)
-        func_1_first_loss, func_1_last_loss = check_func_1(
-            model,
-            feed_dict={"image": img,
-                       "label": label},
-            iter=iter,
-            batch_size=batch_size,
-            use_cuda=use_cuda)
+        func_1_first_loss, func_1_last_loss = get_origin_result(use_cuda)
         func_2_first_loss, func_2_last_loss = check_func_2(
             model,
-            feed_dict={"image": img,
-                       "label": label},
-            iter=iter,
-            batch_size=batch_size,
+            feed_dict=_feed_dict(use_cuda),
+            iter=_iter(use_cuda),
+            batch_size=_batch_size(),
             use_cuda=use_cuda)
 
         if compare_seperately:
@@ -311,97 +340,55 @@ class TestResnet(TestParallelExecutorBase):
             self.assertAlmostEquals(
                 np.mean(func_1_last_loss), func_2_last_loss[0], delta=delta2)
 
-    def _compare_with_fused_all_reduce(self,
-                                       model,
-                                       use_cuda,
-                                       iter=20,
-                                       delta2=1e-5):
-        if use_cuda and not core.is_compiled_with_cuda():
-            return
-
-        global remove_bn
-        remove_bn = True
-
-        img, label = init_data(
-            batch_size=batch_size, img_shape=img_shape, label_range=999)
-        all_reduce_first_loss, all_reduce_last_loss = self.check_network_convergence(
-            model,
-            feed_dict={"image": img,
-                       "label": label},
-            iter=iter,
-            batch_size=batch_size,
-            use_cuda=use_cuda,
-            fuse_all_reduce_ops=False,
-            optimizer=optimizer)
-        reduce_first_loss, reduce_last_loss = self.check_network_convergence(
-            model,
-            feed_dict={"image": img,
-                       "label": label},
-            iter=iter,
-            batch_size=batch_size,
-            use_cuda=use_cuda,
-            fuse_all_reduce_ops=True,
-            optimizer=optimizer)
-
-        for loss in zip(all_reduce_first_loss, reduce_first_loss):
-            self.assertAlmostEquals(loss[0], loss[1], delta=1e-5)
-        for loss in zip(all_reduce_last_loss, reduce_last_loss):
-            self.assertAlmostEquals(loss[0], loss[1], delta=delta2)
-
     def test_seresnext_with_reduce(self):
-        self._compare_reduce_and_allreduce(
-            model=SE_ResNeXt50Small, use_cuda=True, delta2=1e-2)
-        self._compare_reduce_and_allreduce(
-            model=SE_ResNeXt50Small, use_cuda=False, iter=5)
-
-    def test_seresnext_with_fused_all_reduce(self):
-        self._compare_with_fused_all_reduce(
-            model=SE_ResNeXt50Small, use_cuda=True, delta2=1e-3)
-        self._compare_with_fused_all_reduce(
-            model=SE_ResNeXt50Small, use_cuda=False, iter=2, delta2=1e-3)
+        self._compare_reduce_and_allreduce(use_cuda=False, delta2=1e-3)
+        self._compare_reduce_and_allreduce(use_cuda=True, delta2=1e-2)
 
     def test_seresnext_with_learning_rate_decay(self):
-        check_func_1 = partial(
-            self.check_network_convergence,
-            optimizer=optimizer,
-            use_parallel_executor=True)
+        # NOTE(zcd): This test is compare the result of use parallel_executor and executor,
+        # and the result of drop_out op and batch_norm op in this two executor
+        # have diff, so the two ops should be removed from the model.
+        check_func_1 = _get_origin_result
         check_func_2 = partial(
             self.check_network_convergence,
             optimizer=optimizer,
             use_parallel_executor=False)
-        self._check_resnet_convergence(
-            SE_ResNeXt50Small,
-            check_func_1,
-            check_func_2,
-            use_cuda=True,
-            compare_seperately=False)
-        self._check_resnet_convergence(
-            SE_ResNeXt50Small,
+        self._compare_result_with_origin_model(
             check_func_1,
             check_func_2,
             use_cuda=False,
+            rm_drop_out=True,
+            rm_bn=True,
             compare_seperately=False,
-            iter=2,
             delta2=1e-3)
+        self._compare_result_with_origin_model(
+            check_func_1,
+            check_func_2,
+            use_cuda=True,
+            rm_drop_out=True,
+            rm_bn=True,
+            compare_seperately=False)
 
-    def test_seresnext_with_fused_optimizer_ops(self):
-        check_func_1 = partial(
-            self.check_network_convergence, fuse_all_optimizer_ops=False)
+    def test_seresnext_with_fused_all_reduce(self):
+        # NOTE(zcd): In order to make the program faster,
+        # this unit test remove drop_out and batch_norm.
+        check_func_1 = _get_origin_result
         check_func_2 = partial(
-            self.check_network_convergence, fuse_all_optimizer_ops=True)
-        # TODO(zcd): this test failed random, I will fix it in next PR.
-        # self._check_resnet_convergence(
-        #     SE_ResNeXt50Small,
-        #     check_func_1,
-        #     check_func_2,
-        #     use_cuda=True,
-        #     delta2=1e-3)
-        self._check_resnet_convergence(
-            SE_ResNeXt50Small,
+            self.check_network_convergence,
+            optimizer=optimizer,
+            fuse_all_reduce_ops=True)
+        self._compare_result_with_origin_model(
             check_func_1,
             check_func_2,
             use_cuda=False,
-            iter=2,
+            rm_drop_out=True,
+            rm_bn=True)
+        self._compare_result_with_origin_model(
+            check_func_1,
+            check_func_2,
+            use_cuda=True,
+            rm_drop_out=True,
+            rm_bn=True,
             delta2=1e-3)
 
 

python/paddle/fluid/tests/unittests/test_reduce_op.py

@@ -91,6 +91,78 @@ class TestProdOp(OpTest):
         self.check_grad(['X'], 'Out')
 
 
+class TestAllOp(OpTest):
+    def setUp(self):
+        self.op_type = "reduce_all"
+        self.inputs = {'X': np.random.randint(0, 2, (5, 6, 10)).astype("bool")}
+        self.outputs = {'Out': self.inputs['X'].all()}
+        self.attrs = {'reduce_all': True}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestAllOpWithDim(OpTest):
+    def setUp(self):
+        self.op_type = "reduce_all"
+        self.inputs = {'X': np.random.randint(0, 2, (5, 6, 10)).astype("bool")}
+        self.attrs = {'dim': [1]}
+        self.outputs = {'Out': self.inputs['X'].all(axis=1)}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestAllOpWithKeepDim(OpTest):
+    def setUp(self):
+        self.op_type = "reduce_all"
+        self.inputs = {'X': np.random.randint(0, 2, (5, 6, 10)).astype("bool")}
+        self.attrs = {'dim': [1], 'keep_dim': True}
+        self.outputs = {
+            'Out': np.expand_dims(
+                self.inputs['X'].all(axis=1), axis=1)
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestAnyOp(OpTest):
+    def setUp(self):
+        self.op_type = "reduce_any"
+        self.inputs = {'X': np.random.randint(0, 2, (5, 6, 10)).astype("bool")}
+        self.outputs = {'Out': self.inputs['X'].any()}
+        self.attrs = {'reduce_all': True}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestAnyOpWithDim(OpTest):
+    def setUp(self):
+        self.op_type = "reduce_any"
+        self.inputs = {'X': np.random.randint(0, 2, (5, 6, 10)).astype("bool")}
+        self.attrs = {'dim': [1]}
+        self.outputs = {'Out': self.inputs['X'].any(axis=1)}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestAnyOpWithKeepDim(OpTest):
+    def setUp(self):
+        self.op_type = "reduce_any"
+        self.inputs = {'X': np.random.randint(0, 2, (5, 6, 10)).astype("bool")}
+        self.attrs = {'dim': [1], 'keep_dim': True}
+        self.outputs = {
+            'Out': np.expand_dims(
+                self.inputs['X'].any(axis=1), axis=1)
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+
 class Test1DReduce(OpTest):
     def setUp(self):
         self.op_type = "reduce_sum"