Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into optimize-thread-pool

test=develop

CMakeLists.txt

@@ -62,7 +62,6 @@ option(WITH_DISTRIBUTE  "Compile with distributed support"              OFF)
 option(USE_EIGEN_FOR_BLAS   "Use matrix multiplication in Eigen"        OFF)
 option(EIGEN_USE_THREADS "Compile with multi-threaded Eigen"            OFF)
 option(WITH_ARM_FP16    "Use half precision support on armv8.2-a cpu"   OFF)
-option(WITH_FAST_BUNDLE_TEST    "Bundle tests that can be run in a single process together to reduce launch overhead"   OFF)
 option(WITH_CONTRIB     "Compile the third-party contributation"        OFF)
 option(REPLACE_ENFORCE_GLOG "Replace PADDLE_ENFORCE with glog/CHECK for better debug." OFF)
 option(WITH_ANAKIN      "Compile with Anakin library"                   OFF)

cmake/configure.cmake

@@ -50,11 +50,7 @@ if(NOT WITH_PROFILER)
 endif(NOT WITH_PROFILER)
 
 if(NOT CMAKE_CROSSCOMPILING)
-    if(WITH_AVX AND AVX512F_FOUND)
-        set(SIMD_FLAG ${AVX512F_FLAG})
-    elseif(WITH_AVX AND AVX2_FOUND)
-        set(SIMD_FLAG ${AVX2_FLAG})
-    elseif(WITH_AVX AND AVX_FOUND)
+    if(WITH_AVX AND AVX_FOUND)
         set(SIMD_FLAG ${AVX_FLAG})
     elseif(SSE3_FOUND)
         set(SIMD_FLAG ${SSE3_FLAG})

cmake/simd.cmake

@@ -89,7 +89,9 @@ CHECK_CXX_SOURCE_RUNS("
 #include <immintrin.h>
 int main()
 {
-    __m512i a = _mm512_undefined_epi32();
+    __m512i a = _mm512_set_epi32 (-1, 2, -3, 4, -1, 2, -3, 4,
+                                  13, -5, 6, -7, 9, 2, -6, 3);
+    __m512i result = _mm512_abs_epi32 (a);
     return 0;
 }" AVX512F_FOUND)
 

paddle/fluid/API.spec

@@ -67,8 +67,8 @@ paddle.fluid.layers.conv3d ArgSpec(args=['input', 'num_filters', 'filter_size',
 paddle.fluid.layers.sequence_pool ArgSpec(args=['input', 'pool_type', 'is_test'], varargs=None, keywords=None, defaults=(False,))
 paddle.fluid.layers.sequence_softmax ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(False, None))
 paddle.fluid.layers.softmax ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(True, None))
-paddle.fluid.layers.pool2d ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None))
-paddle.fluid.layers.pool3d ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None))
+paddle.fluid.layers.pool2d ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True))
+paddle.fluid.layers.pool3d ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True))
 paddle.fluid.layers.batch_norm ArgSpec(args=['input', 'act', 'is_test', 'momentum', 'epsilon', 'param_attr', 'bias_attr', 'data_layout', 'in_place', 'name', 'moving_mean_name', 'moving_variance_name', 'do_model_average_for_mean_and_var', 'fuse_with_relu'], varargs=None, keywords=None, defaults=(None, False, 0.9, 1e-05, None, None, 'NCHW', False, None, None, None, False, False))
 paddle.fluid.layers.beam_search_decode ArgSpec(args=['ids', 'scores', 'beam_size', 'end_id', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.conv2d_transpose ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None))
@@ -103,7 +103,7 @@ paddle.fluid.layers.beam_search ArgSpec(args=['pre_ids', 'pre_scores', 'ids', 's
 paddle.fluid.layers.row_conv ArgSpec(args=['input', 'future_context_size', 'param_attr', 'act'], varargs=None, keywords=None, defaults=(None, None))
 paddle.fluid.layers.multiplex ArgSpec(args=['inputs', 'index'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.layer_norm ArgSpec(args=['input', 'scale', 'shift', 'begin_norm_axis', 'epsilon', 'param_attr', 'bias_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(True, True, 1, 1e-05, None, None, None, None))
-paddle.fluid.layers.softmax_with_cross_entropy ArgSpec(args=['logits', 'label', 'soft_label', 'ignore_index'], varargs=None, keywords=None, defaults=(False, -100))
+paddle.fluid.layers.softmax_with_cross_entropy ArgSpec(args=['logits', 'label', 'soft_label', 'ignore_index', 'numeric_stable_mode'], varargs=None, keywords=None, defaults=(False, -100, False))
 paddle.fluid.layers.smooth_l1 ArgSpec(args=['x', 'y', 'inside_weight', 'outside_weight', 'sigma'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.layers.one_hot ArgSpec(args=['input', 'depth'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.autoincreased_step_counter ArgSpec(args=['counter_name', 'begin', 'step'], varargs=None, keywords=None, defaults=(None, 1, 1))
@@ -174,9 +174,11 @@ paddle.fluid.layers.mean ArgSpec(args=['x', 'name'], varargs=None, keywords=None
 paddle.fluid.layers.mul ArgSpec(args=['x', 'y', 'x_num_col_dims', 'y_num_col_dims', 'name'], varargs=None, keywords=None, defaults=(1, 1, None))
 paddle.fluid.layers.sigmoid_cross_entropy_with_logits ArgSpec(args=['x', 'label', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.maxout ArgSpec(args=['x', 'groups', 'name'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.layers.affine_grid ArgSpec(args=['theta', 'out_shape', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.sequence_reverse ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.affine_channel ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None))
 paddle.fluid.layers.hash ArgSpec(args=['input', 'hash_size', 'num_hash', 'name'], varargs=None, keywords=None, defaults=(1, None))
+paddle.fluid.layers.grid_sampler ArgSpec(args=['x', 'grid', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.log_loss ArgSpec(args=['input', 'label', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(0.0001, None))
 paddle.fluid.layers.add_position_encoding ArgSpec(args=['input', 'alpha', 'beta', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))

paddle/fluid/framework/details/CMakeLists.txt

@@ -35,13 +35,15 @@ if(WITH_GPU)
           all_reduce_op_handle reduce_op_handle broadcast_op_handle data_balance_op_handle graph graph_helper pass)
 endif()
 
+cc_library(sequential_execution_pass SRCS sequential_execution_pass.cc DEPS graph graph_helper pass)
+
 cc_library(multi_devices_graph_pass SRCS multi_devices_graph_pass.cc DEPS multi_devices_helper computation_op_handle
         scale_loss_grad_op_handle rpc_op_handle all_reduce_op_handle reduce_op_handle broadcast_op_handle data_balance_op_handle fused_broadcast_op_handle)
 
 if(WITH_GPU)
-  cc_library(ssa_graph_executor SRCS ssa_graph_executor.cc DEPS graph framework_proto reference_count_pass)
+  cc_library(ssa_graph_executor SRCS ssa_graph_executor.cc DEPS graph framework_proto reference_count_pass sequential_execution_pass)
 else()
-  cc_library(ssa_graph_executor SRCS ssa_graph_executor.cc DEPS graph framework_proto)
+  cc_library(ssa_graph_executor SRCS ssa_graph_executor.cc DEPS graph framework_proto sequential_execution_pass)
 endif()
 
 cc_library(threaded_ssa_graph_executor SRCS threaded_ssa_graph_executor.cc DEPS fetch_op_handle ssa_graph_executor scope

paddle/fluid/framework/details/build_strategy.cc

@@ -16,6 +16,7 @@ limitations under the License. */
 
 #include "paddle/fluid/framework/details/multi_devices_graph_check_pass.h"
 #include "paddle/fluid/framework/details/multi_devices_graph_print_pass.h"
+#include "paddle/fluid/framework/details/sequential_execution_pass.h"
 #include "paddle/fluid/framework/ir/graph.h"
 #include "paddle/fluid/framework/ir/graph_viz_pass.h"
 
@@ -27,6 +28,10 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
  public:
   explicit ParallelExecutorPassBuilder(const BuildStrategy &strategy)
       : ir::PassBuilder(), strategy_(strategy) {
+    if (strategy_.enable_sequential_execution_) {
+      AppendPass("sequential_execution_pass");
+    }
+
     // Add a graph viz pass to record a graph.
     if (!strategy_.debug_graphviz_path_.empty()) {
       auto viz_pass = AppendPass("graph_viz_pass");
@@ -110,6 +115,11 @@ std::unique_ptr<ir::Graph> BuildStrategy::Apply(
       pass->Erase("nccl_ctxs");
       pass->SetNotOwned<platform::NCCLContextMap>("nccl_ctxs", nctx);
 #endif
+    } else if (pass->Type() == "sequential_execution_pass") {
+      pass->Erase(kAllOpDescs);
+      pass->Set<const std::vector<OpDesc *>>(
+          kAllOpDescs,
+          new std::vector<OpDesc *>(main_program.Block(0).AllOps()));
     }
     graph = pass->Apply(std::move(graph));
   }
@@ -125,3 +135,4 @@ USE_PASS(multi_batch_merge_pass);
 USE_PASS(multi_devices_pass);
 USE_PASS(multi_devices_check_pass);
 USE_PASS(multi_devices_print_pass);
+USE_PASS(sequential_execution_pass);

paddle/fluid/framework/details/build_strategy.h

@@ -69,6 +69,8 @@ struct BuildStrategy {
 
   bool enable_data_balance_{false};
 
+  bool enable_sequential_execution_{false};
+
   bool fuse_broadcast_op_{false};
 
   // User normally doesn't need to call this API.

paddle/fluid/framework/details/rpc_op_handle.cc

@@ -29,22 +29,19 @@ RPCOpHandle::RPCOpHandle(ir::Node *node, const framework::OpDesc &op_desc,
       place_(place) {}
 
 void RPCOpHandle::RunImpl() {
-  // TODO(wuyi): need further analysis whether wait VarDummyHandle.
-  // Wait input done
   for (auto *in : inputs_) {
     auto &p = static_cast<VarHandle *>(in)->place_;
-    // FIXME(Yancey1989): need a better solution instead of use DebugString()
-    if (ir::IsControlDepVar(*in->Node())) {  // HACK
+    if (ir::IsControlDepVar(*in->Node())) {
       continue;
     }
     if (in->GeneratedOp()) {
       in->GeneratedOp()->RecordWaitEventOnCtx(dev_ctxes_.at(p));
     }
   }
-  auto &tmp_scope = local_scope_->FindVar(kLocalExecScopeName)->Get<Scope *>();
-  // FIXME(wuyi): can not use RunAndRecordEvent here, for it will cause dead
-  // lock.
-  op_->Run(*tmp_scope, place_);
+  this->RunAndRecordEvent([this] {
+    op_->Run(*local_scope_->FindVar(kLocalExecScopeName)->Get<Scope *>(),
+             place_);
+  });
 }
 
 std::string RPCOpHandle::Name() const { return name_; }

paddle/fluid/framework/details/sequential_execution_pass.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/framework/details/sequential_execution_pass.h"
+#include <string>
+#include <unordered_map>
+#include <unordered_set>
+#include <vector>
+#include "paddle/fluid/framework/op_proto_maker.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+static bool IsSameOpDesc(OpDesc *op1, OpDesc *op2) {
+  return op1->Type() == op2->Type() && op1->Inputs() == op2->Inputs() &&
+         op1->Outputs() == op2->Outputs();
+}
+
+std::unique_ptr<ir::Graph> SequentialExecutionPass::ApplyImpl(
+    std::unique_ptr<ir::Graph> graph) const {
+  // FIXME(zjl): Insert dependencies between some distributed ops may cause
+  // the multi_devices_graph_pass fails. So we skip these ops here.
+  // Indeed, maybe we should not insert dependencies between these ops
+  // casually, which may cause deadlock easily.
+  // We should add more skipped distributed ops when found errors in
+  // multi_devices_graph_pass
+  static std::unordered_set<std::string> skip_dist_ops{
+      "send", "recv", "send_barrier", "fetch_barrier"};
+
+  auto &ops = Get<const std::vector<OpDesc *>>(kAllOpDescs);
+  std::vector<ir::Node *> op_node_list;
+  op_node_list.reserve(ops.size());
+
+  std::unordered_map<ir::Node *, size_t> op_deps;
+  std::unordered_map<ir::Node *, std::unordered_set<ir::Node *>> pending_ops;
+  std::unordered_set<ir::Node *> ready_ops;
+
+  for (ir::Node *node : graph->Nodes()) {
+    if (!node->IsOp()) continue;
+    std::unordered_set<ir::Node *> preceding_ops;
+    for (auto *in : node->inputs) {
+      PADDLE_ENFORCE(in->IsVar(),
+                     "Preceding Node of Op Nodes must be Var Node");
+      if (in->inputs.empty()) continue;
+      PADDLE_ENFORCE(in->inputs.size() == 1 && in->inputs[0]->IsOp(),
+                     "Preceding Op Node of Var Node must be unique");
+      preceding_ops.insert(in->inputs[0]);
+      pending_ops[in->inputs[0]].insert(node);
+    }
+    op_deps[node] = preceding_ops.size();
+    if (preceding_ops.empty()) {
+      ready_ops.insert(node);
+    }
+  }
+
+  for (auto *op_desc : ops) {
+    ir::Node *found_node = nullptr;
+    for (auto *node : ready_ops) {
+      if (IsSameOpDesc(op_desc, node->Op())) {
+        PADDLE_ENFORCE(found_node == nullptr,
+                       "Found multiple op_desc in graph: %s", op_desc->Type());
+        found_node = node;
+      }
+    }
+
+    PADDLE_ENFORCE_NOT_NULL(found_node, "Cannot find op_desc in graph: %s",
+                            op_desc->Type());
+    for (auto *pending_op : pending_ops[found_node]) {
+      if (--op_deps.at(pending_op) == 0) {
+        ready_ops.insert(pending_op);
+      }
+    }
+    ready_ops.erase(found_node);
+    if (skip_dist_ops.count(op_desc->Type()) == 0) {
+      op_node_list.push_back(found_node);
+    }
+  }
+
+  for (size_t i = 1; i < op_node_list.size(); ++i) {
+    auto *dep_var = graph->CreateControlDepVar();
+    op_node_list[i]->inputs.push_back(dep_var);
+    op_node_list[i - 1]->outputs.push_back(dep_var);
+    dep_var->outputs.push_back(op_node_list[i]);
+    dep_var->inputs.push_back(op_node_list[i - 1]);
+    VLOG(10) << "Add dependencies between " << op_node_list[i - 1]->Name()
+             << " and " << op_node_list[i]->Name();
+  }
+  return graph;
+}
+
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle
+
+REGISTER_PASS(sequential_execution_pass,
+              paddle::framework::details::SequentialExecutionPass)
+    .RequirePassAttr(paddle::framework::details::kAllOpDescs);

paddle/fluid/framework/details/sequential_execution_pass.h

+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include "paddle/fluid/framework/ir/graph.h"
+#include "paddle/fluid/framework/ir/pass.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+constexpr char kAllOpDescs[] = "all_op_descs";
+
+class SequentialExecutionPass : public ir::Pass {
+ protected:
+  std::unique_ptr<ir::Graph> ApplyImpl(
+      std::unique_ptr<ir::Graph> graph) const override;
+};
+
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/executor.cc

@@ -85,8 +85,10 @@ Executor::Executor(const platform::Place& place) : place_(place) {}
 
 void Executor::Close() {
 #ifdef PADDLE_WITH_DISTRIBUTE
+  // TODO(typhoonzero): complete message will need to use real trainer_id,
+  // except 0.
   ::paddle::operators::distributed::RPCClient::GetInstance<
-      ::paddle::operators::distributed::GRPCClient>()
+      ::paddle::operators::distributed::GRPCClient>(0)
       ->SendComplete();
 #endif
 }

paddle/fluid/framework/ir/CMakeLists.txt

@@ -41,6 +41,7 @@ pass_library(conv_bn_fuse_pass inference)
 pass_library(seqconv_eltadd_relu_fuse_pass inference)
 if(WITH_MKLDNN)
     pass_library(mkldnn_placement_pass base)
+    pass_library(depthwise_conv_mkldnn_pass base)
     pass_library(conv_bias_mkldnn_fuse_pass inference)
     pass_library(conv_relu_mkldnn_fuse_pass inference)
     pass_library(conv_elementwise_add_mkldnn_fuse_pass inference)
@@ -59,6 +60,7 @@ cc_test(graph_to_program_pass_test SRCS graph_to_program_pass_test.cc DEPS graph
 cc_test(test_graph_pattern_detector SRCS graph_pattern_detector_tester.cc DEPS graph_pattern_detector)
 cc_test(test_fc_fuse_pass SRCS fc_fuse_pass_tester.cc DEPS fc_fuse_pass framework_proto)
 if (WITH_MKLDNN)
+    cc_test(test_depthwise_conv_mkldnn_pass SRCS depthwise_conv_mkldnn_pass_tester.cc DEPS depthwise_conv_mkldnn_pass)
     cc_test(test_conv_relu_mkldnn_fuse_pass SRCS conv_relu_mkldnn_fuse_pass_tester.cc DEPS conv_relu_mkldnn_fuse_pass)
     cc_test(test_conv_elementwise_add_mkldnn_fuse_pass SRCS conv_elementwise_add_mkldnn_fuse_pass_tester.cc DEPS conv_elementwise_add_mkldnn_fuse_pass)
 endif ()

paddle/fluid/framework/ir/conv_relu_mkldnn_fuse_pass.h

@@ -31,7 +31,8 @@ class ConvReLUFusePass : public FusePassBase {
   virtual ~ConvReLUFusePass() {}
 
  protected:
-  std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
+  std::unique_ptr<ir::Graph> ApplyImpl(
+      std::unique_ptr<ir::Graph> graph) const override;
 };
 
 }  // namespace ir

paddle/fluid/framework/ir/conv_relu_mkldnn_fuse_pass_tester.cc

@@ -15,6 +15,7 @@
 #include "paddle/fluid/framework/ir/conv_relu_mkldnn_fuse_pass.h"
 
 #include <gtest/gtest.h>
+#include "paddle/fluid/framework/op_proto_maker.h"
 
 namespace paddle {
 namespace framework {
@@ -36,6 +37,8 @@ void SetOp(ProgramDesc* prog, const std::string& type, const std::string& name,
     op->SetInput("X", inputs);
   }
   op->SetOutput("Out", outputs);
+  op->SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(),
+              static_cast<int>(OpRole::kForward));
 }
 
 // a->OP0->b

paddle/fluid/framework/ir/depthwise_conv_mkldnn_pass.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/framework/ir/depthwise_conv_mkldnn_pass.h"
+#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+#define GET_NODE(id, pattern)                               \
+  PADDLE_ENFORCE(subgraph.count(pattern.RetrieveNode(#id)), \
+                 "pattern has no Node called %s", #id);     \
+  auto* id = subgraph.at(pattern.RetrieveNode(#id));        \
+  PADDLE_ENFORCE_NOT_NULL(id, "subgraph has no node %s", #id);
+
+std::unique_ptr<ir::Graph> DepthwiseConvMKLDNNPass::ApplyImpl(
+    std::unique_ptr<ir::Graph> graph) const {
+  PADDLE_ENFORCE(graph.get());
+  FusePassBase::Init("depthwise_conv_mkldnn_pass", graph.get());
+  GraphPatternDetector gpd;
+
+  auto* pattern = gpd.mutable_pattern();
+  pattern->NewNode("depthwise_conv")
+      ->assert_is_op("depthwise_conv2d")
+      ->assert_op_attr("use_mkldnn", true);
+
+  int found_depthwise_conv_mkldnn_count = 0;
+  auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
+                     Graph* g) {
+    VLOG(3) << "handle DepthwiseConvMKLDNN fuse";
+    GET_NODE(depthwise_conv, (*pattern));
+    depthwise_conv->Op()->SetType("conv2d");
+    found_depthwise_conv_mkldnn_count++;
+  };
+
+  gpd(graph.get(), handler);
+  AddStatis(found_depthwise_conv_mkldnn_count);
+  return graph;
+}
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+REGISTER_PASS(depthwise_conv_mkldnn_pass,
+              paddle::framework::ir::DepthwiseConvMKLDNNPass);

paddle/fluid/framework/ir/depthwise_conv_mkldnn_pass.h

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+
+#include "paddle/fluid/framework/ir/fuse_pass_base.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+class DepthwiseConvMKLDNNPass : public FusePassBase {
+ public:
+  virtual ~DepthwiseConvMKLDNNPass() {}
+
+ protected:
+  std::unique_ptr<ir::Graph> ApplyImpl(
+      std::unique_ptr<ir::Graph> graph) const override;
+};
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/ir/depthwise_conv_mkldnn_pass_tester.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/framework/ir/depthwise_conv_mkldnn_pass.h"
+
+#include <gtest/gtest.h>
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+void SetOp(ProgramDesc* prog, const std::string& type, const std::string& name,
+           const std::vector<std::string>& inputs,
+           const std::vector<std::string>& outputs, bool use_mkldnn = false) {
+  auto* op = prog->MutableBlock(0)->AppendOp();
+  op->SetType(type);
+  op->SetAttr("use_mkldnn", use_mkldnn);
+  op->SetAttr("name", name);
+  op->SetInput("Input", {inputs[0]});
+  op->SetInput("Filter", {inputs[1]});
+  op->SetInput("Bias", {inputs[2]});
+  op->SetOutput("Out", outputs);
+}
+
+// (a, weights, bias)->depthwise conv mkldnn->b
+// (b, weights2, bias2)->depthwise conv no mkldnn->c
+// (c, weights3, bias3)->conv mkldnn->d
+// (d, weights3, bias3)->conv no mkldnn->e
+ProgramDesc BuildProgramDesc() {
+  ProgramDesc prog;
+  for (auto& v : std::vector<std::string>(
+           {"a", "b", "c", "d", "e", "weights", "bias", "weights2", "bias2",
+            "weights3", "bias3", "weights4", "bias4"})) {
+    auto* var = prog.MutableBlock(0)->Var(v);
+    var->SetType(proto::VarType::SELECTED_ROWS);
+    if (v == "weights" || v == "bias" || v == "weights2" || v == "bias2" ||
+        v == "weights3" || v == "bias3" || v == "weights4" || v == "bias4") {
+      var->SetPersistable(true);
+    }
+  }
+
+  // depthwise conv with MKL-DNN
+  SetOp(&prog, "depthwise_conv2d", "conv1",
+        std::vector<std::string>({"a", "weights", "bias"}),
+        std::vector<std::string>({"b"}), true);
+  // depthwise conv without MKL-DNN
+  SetOp(&prog, "depthwise_conv2d", "conv2",
+        std::vector<std::string>({"b", "weights2", "bias2"}),
+        std::vector<std::string>({"c"}), false);
+  // conv with MKL-DNN
+  SetOp(&prog, "conv2d", "conv3",
+        std::vector<std::string>({"c", "weights3", "bias3"}),
+        std::vector<std::string>({"d"}), true);
+  // conv without MKL-dNN
+  SetOp(&prog, "conv2d", "conv4",
+        std::vector<std::string>({"d", "weights4", "bias4"}),
+        std::vector<std::string>({"e"}), false);
+
+  return prog;
+}
+
+TEST(DepthwiseConvMKLDNNPass, basic) {
+  auto prog = BuildProgramDesc();
+
+  std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
+
+  auto pass = PassRegistry::Instance().Get("depthwise_conv_mkldnn_pass");
+
+  struct counters {
+    int mkldnn_depthwise_conv_nodes;
+    int other_depthwise_conv_nodes;
+    int mkldnn_conv_nodes;
+    int other_conv_nodes;
+  };
+
+  counters before{1, 1, 1, 1};
+
+  graph = pass->Apply(std::move(graph));
+
+  // initialize counters before loop
+  counters after{0, 0, 0, 0};
+
+  for (auto* node : graph->Nodes()) {
+    if (node->IsOp()) {
+      auto* op = node->Op();
+      if (op->Type() == "conv2d") {
+        if (boost::get<bool>(op->GetAttr("use_mkldnn")))
+          after.mkldnn_conv_nodes++;
+        else
+          after.other_conv_nodes++;
+      } else if (op->Type() == "depthwise_conv2d") {
+        if (boost::get<bool>(op->GetAttr("use_mkldnn")))
+          after.mkldnn_depthwise_conv_nodes++;
+        else
+          after.other_depthwise_conv_nodes++;
+      }
+    }
+  }
+
+  EXPECT_EQ(after.other_depthwise_conv_nodes,
+            before.other_depthwise_conv_nodes);
+  EXPECT_EQ(after.other_conv_nodes, before.other_conv_nodes);
+  EXPECT_EQ(after.mkldnn_depthwise_conv_nodes,
+            before.mkldnn_depthwise_conv_nodes - 1);
+  EXPECT_EQ(after.mkldnn_conv_nodes, before.mkldnn_conv_nodes + 1);
+}
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+USE_PASS(depthwise_conv_mkldnn_pass);

paddle/fluid/framework/ir/fc_fuse_pass_tester.cc

@@ -15,6 +15,7 @@
 #include "paddle/fluid/framework/ir/fc_fuse_pass.h"
 
 #include <gtest/gtest.h>
+#include "paddle/fluid/framework/op_proto_maker.h"
 
 namespace paddle {
 namespace framework {
@@ -32,6 +33,8 @@ void SetOp(ProgramDesc* prog, const std::string& type,
     op->SetInput("X", inputs);
   }
   op->SetOutput("Out", outputs);
+  op->SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(),
+              static_cast<int>(OpRole::kForward));
 }
 
 // a->OP0->b

paddle/fluid/framework/ir/graph.cc

@@ -23,8 +23,67 @@ limitations under the License. */
 namespace paddle {
 namespace framework {
 namespace ir {
+namespace {
+
+void CheckProgram(const ProgramDesc &program) {
+#define _INT(role) static_cast<int>(role)
+
+  std::map<int, bool> visit;
+  for (OpDesc *op : program.Block(0).AllOps()) {
+    // For backward compatibility, some program doesn't have role added.
+    if (!op->HasAttr(OpProtoAndCheckerMaker::OpRoleAttrName())) continue;
+    int role_id =
+        boost::get<int>(op->GetAttr(OpProtoAndCheckerMaker::OpRoleAttrName()));
+    visit[role_id] = true;
+    switch (role_id) {
+      case _INT(OpRole::kForward):
+        if (visit.find(_INT(OpRole::kBackward)) != visit.end()) {
+          LOG(ERROR)
+              << "Cannot add backward operator before forward operator %s."
+              << op->Type();
+        }
+        break;
+      case _INT(OpRole::kBackward):
+      case _INT(OpRole::kBackward) | _INT(OpRole::kLoss):
+        PADDLE_ENFORCE(
+            visit.find(_INT(OpRole::kOptimize)) == visit.end(),
+            "Cannot add backward operator %s after optimize operator.",
+            op->Type());
+        break;
+      case _INT(OpRole::kForward) | _INT(OpRole::kLoss):
+        PADDLE_ENFORCE(visit.find(_INT(OpRole::kBackward) |
+                                  _INT(OpRole::kLoss)) == visit.end(),
+                       "Cannot add backward|loss operator before "
+                       "forward|loss operator %s.",
+                       op->Type());
+        PADDLE_ENFORCE(
+            visit.find(_INT(OpRole::kOptimize)) == visit.end(),
+            "Cannot add forward|loss operator %s after optimize operator.",
+            op->Type());
+        break;
+      case _INT(OpRole::kOptimize):
+      case _INT(OpRole::kOptimize) | _INT(OpRole::kLRSched):
+        PADDLE_ENFORCE(visit.find(_INT(OpRole::kBackward)) != visit.end(),
+                       "Optimize operators %s must follow backward operator.",
+                       op->Type());
+        break;
+      case _INT(OpRole::kLRSched):
+      case _INT(OpRole::kDist):
+      case _INT(OpRole::kRPC):
+      case _INT(OpRole::kNotSpecified):
+        break;
+      default:
+        LOG(FATAL) << "Unknown operator role. Don't add new role because "
+                      "you don't know what you are doing.";
+    }
+  }
+
+#undef _INT
+}
+}  // namespace
 
 Graph::Graph(const ProgramDesc &program) : program_(program) {
+  CheckProgram(program_);
   // Make the nodes id start from 0.
   Node::ResetId();
   auto var_nodes = InitFromProgram(program_);

paddle/fluid/framework/ir/graph_pattern_detector.cc

@@ -259,6 +259,15 @@ GraphPatternDetector::DetectPatterns() {
   return result;
 }
 
+bool GraphItemCMP(const std::pair<PDNode *, Node *> &a,
+                  const std::pair<PDNode *, Node *> &b) {
+  if (a.first != b.first) {
+    return a.first < b.first;
+  } else {
+    return a.second < b.second;
+  }
+}
+
 // TODO(Superjomn) enhance the function as it marks unique unique as duplicates
 // see https://github.com/PaddlePaddle/Paddle/issues/13550
 void GraphPatternDetector::UniquePatterns(
@@ -267,12 +276,16 @@ void GraphPatternDetector::UniquePatterns(
   std::vector<GraphPatternDetector::subgraph_t> result;
 
   std::unordered_set<size_t> set;
+  std::hash<std::string> hasher;
   for (auto &g : *subgraphs) {
-    size_t key = 0;
-    for (auto &item : g) {
-      key ^= std::hash<void *>{}(item.first);
-      key ^= std::hash<void *>{}(item.second);
-    }
+    // Sort the items in the sub-graph, and transform to a string key.
+    std::vector<std::pair<PDNode *, Node *>> sorted_keys(g.begin(), g.end());
+    std::sort(sorted_keys.begin(), sorted_keys.end(), GraphItemCMP);
+    std::stringstream ss;
+    for (auto &item : sorted_keys) {
+      ss << item.first << ":" << item.second;
+    }
+    auto key = hasher(ss.str());
     if (!set.count(key)) {
       result.emplace_back(g);
       set.insert(key);

paddle/fluid/framework/lod_tensor.cc

@@ -418,7 +418,7 @@ void LoDTensor::MergeLoDTensor(
     PADDLE_ENFORCE_EQ(new_lod.size(), lod.size());
     for (size_t j = 0; j < lod.size(); ++j) {
       auto &sub_lod = new_lod[j];
-      auto &offset = sub_lod.back();
+      size_t offset = sub_lod.back();
       for (size_t k = 1; k < lod[j].size(); ++k) {
         sub_lod.push_back(lod[j][k] + offset);
       }

paddle/fluid/framework/tensor_util.cc

@@ -153,6 +153,12 @@ void TensorCopySync(const Tensor& src, const platform::Place& dst_place,
     auto src_gpu_place = boost::get<platform::CUDAPlace>(src_place);
     auto dst_gpu_place = boost::get<platform::CUDAPlace>(dst_place);
     memory::Copy(dst_gpu_place, dst_ptr, src_gpu_place, src_ptr, size, nullptr);
+  } else if (platform::is_cuda_pinned_place(src_place) &&
+             platform::is_gpu_place(dst_place)) {
+    auto src_pinned_place = boost::get<platform::CUDAPinnedPlace>(src_place);
+    auto dst_gpu_place = boost::get<platform::CUDAPlace>(dst_place);
+    memory::Copy(dst_gpu_place, dst_ptr, src_pinned_place, src_ptr, size,
+                 nullptr);
   }
 #endif
 }

paddle/fluid/inference/CMakeLists.txt

+if(WITH_TESTING)
+  include(test.cmake) # some generic cmake funtion for inference
+endif()
 # analysis and tensorrt must be added before creating static library,
 # otherwise, there would be undefined reference to them in static library.
 add_subdirectory(analysis)

paddle/fluid/inference/analysis/CMakeLists.txt

@@ -20,22 +20,17 @@ cc_test(test_node SRCS node_tester.cc DEPS analysis)
 cc_test(test_dot SRCS dot_tester.cc DEPS analysis)
 cc_binary(inference_analyzer SRCS analyzer_main.cc DEPS analysis paddle_fluid)
 
-function (inference_analysis_test TARGET)
+function(inference_analysis_test TARGET)
   if(WITH_TESTING)
      set(options "")
      set(oneValueArgs "")
      set(multiValueArgs SRCS ARGS EXTRA_DEPS)
      cmake_parse_arguments(analysis_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
-        set(mem_opt "")
-        if(WITH_GPU)
-            set(mem_opt "--fraction_of_gpu_memory_to_use=0.5")
-        endif()
-        cc_test(${TARGET}
-                SRCS "${analysis_test_SRCS}"
+     inference_base_test(${TARGET}
+             SRCS ${analysis_test_SRCS}
              DEPS analysis pass ${GLOB_PASS_LIB} ${analysis_test_EXTRA_DEPS}
-                ARGS --inference_model_dir=${PYTHON_TESTS_DIR}/book/word2vec.inference.model ${mem_opt} ${analysis_test_ARGS})
-        set_tests_properties(${TARGET} PROPERTIES DEPENDS test_word2vec)
-    endif(WITH_TESTING)
+             ARGS --inference_model_dir=${WORD2VEC_MODEL_DIR} ${analysis_test_ARGS})
+  endif()
 endfunction(inference_analysis_test)
 
 inference_analysis_test(test_analyzer SRCS analyzer_tester.cc EXTRA_DEPS paddle_inference_api)

paddle/fluid/inference/analysis/analyzer.h

@@ -79,6 +79,7 @@ class Analyzer : public OrderedRegistry<PassManager> {
       "conv_bn_fuse_pass",              //
       "conv_eltwiseadd_bn_fuse_pass",   //
 #ifdef PADDLE_WITH_MKLDNN
+      "depthwise_conv_mkldnn_pass",             //
       "conv_bias_mkldnn_fuse_pass",             //
       "conv_relu_mkldnn_fuse_pass",             //
       "conv_elementwise_add_mkldnn_fuse_pass",  //

paddle/fluid/inference/analysis/data_flow_graph_tester.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/inference/analysis/data_flow_graph.h"
+#include "paddle/fluid/framework/op_proto_maker.h"
 #include "paddle/fluid/framework/program_desc.h"
 #include "paddle/fluid/inference/analysis/ut_helper.h"
 
@@ -130,6 +131,8 @@ void SetOp(framework::ProgramDesc* prog, const std::string& type,
   op->SetType(type);
   op->SetInput("Xs", inputs);
   op->SetOutput("Xs", outputs);
+  op->SetAttr(framework::OpProtoAndCheckerMaker::OpRoleAttrName(),
+              static_cast<int>(framework::OpRole::kForward));
 }
 
 TEST(DataFlowGraph, Build_IR_Graph) {

paddle/fluid/inference/api/CMakeLists.txt

@@ -17,39 +17,12 @@ if(APPLE)
     set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-error=pessimizing-move")
 endif(APPLE)
 
-
-set(inference_deps paddle_inference_api paddle_fluid_api analysis pass ir_pass_manager naive_executor ${GLOB_PASS_LIB}
-        )
+set(inference_deps paddle_inference_api paddle_fluid_api analysis pass ir_pass_manager naive_executor ${GLOB_PASS_LIB})
 
 if(WITH_GPU AND TENSORRT_FOUND)
     set(inference_deps ${inference_deps} paddle_inference_tensorrt_subgraph_engine analysis_predictor)
 endif()
 
-function(inference_api_test TARGET_NAME)
-    if (WITH_TESTING)
-        set(options "")
-        set(oneValueArgs SRC)
-        set(multiValueArgs ARGS)
-        cmake_parse_arguments(inference_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
-
-	if (WITH_GPU)
-		cc_test(${TARGET_NAME}
-			SRCS ${inference_test_SRC}
-			DEPS "${inference_deps}"
-			ARGS --dirname=${PYTHON_TESTS_DIR}/book/ --fraction_of_gpu_memory_to_use=0.15)
-        else()
-		cc_test(${TARGET_NAME}
-			SRCS ${inference_test_SRC}
-			DEPS "${inference_deps}"
-			ARGS --dirname=${PYTHON_TESTS_DIR}/book/)
-	endif()
-        if(inference_test_ARGS)
-            set_tests_properties(${TARGET_NAME}
-                    PROPERTIES DEPENDS "${inference_test_ARGS}")
-        endif()
-    endif(WITH_TESTING)
-endfunction(inference_api_test)
-
 cc_library(reset_tensor_array SRCS details/reset_tensor_array.cc DEPS lod_tensor scope)
 cc_library(paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS reset_tensor_array lod_tensor scope)
 cc_library(analysis_predictor SRCS analysis_predictor.cc DEPS paddle_inference_api analysis naive_executor zero_copy_tensor)
@@ -59,8 +32,11 @@ cc_test(test_paddle_inference_api
         SRCS api_tester.cc
         DEPS paddle_inference_api)
 
-inference_api_test(test_api_impl SRC api_impl_tester.cc
-                    ARGS test_word2vec test_image_classification)
+if(WITH_TESTING)
+  inference_base_test(test_api_impl SRCS api_impl_tester.cc DEPS ${inference_deps}
+                      ARGS --word2vec_dirname=${WORD2VEC_MODEL_DIR} --book_dirname=${PYTHON_TESTS_DIR}/book)
+  set_tests_properties(test_api_impl PROPERTIES DEPENDS test_image_classification)
+endif()
 cc_test(test_analysis_predictor SRCS analysis_predictor_tester.cc DEPS analysis_predictor ${inference_deps} paddle_inference_api
         ARGS --dirname=${PYTHON_TESTS_DIR}/book)
 
@@ -68,8 +44,10 @@ if(WITH_GPU AND TENSORRT_FOUND)
 cc_library(paddle_inference_tensorrt_subgraph_engine
         SRCS api_tensorrt_subgraph_engine.cc
         DEPS paddle_inference_api analysis tensorrt_engine paddle_inference_api paddle_fluid_api tensorrt_converter zero_copy_tensor_dummy)
-
-inference_api_test(test_api_tensorrt_subgraph_engine SRC api_tensorrt_subgraph_engine_tester.cc ARGS test_word2vec)
+  if(WITH_TESTING)
+    inference_base_test(test_api_tensorrt_subgraph_engine SRCS api_tensorrt_subgraph_engine_tester.cc DEPS ${inference_deps}
+                      ARGS --dirname=${WORD2VEC_MODEL_DIR})
+  endif()
 endif()
 
 if (WITH_ANAKIN AND WITH_MKL) # only needed in CI

paddle/fluid/inference/api/api_impl_tester.cc

@@ -22,12 +22,14 @@ limitations under the License. */
 #include "paddle/fluid/inference/tests/test_helper.h"
 
 #ifdef __clang__
-#define ACC_DIFF 4e-2
+#define ACC_DIFF 4e-3
 #else
-#define ACC_DIFF 1e-2
+#define ACC_DIFF 1e-3
 #endif
 
-DEFINE_string(dirname, "", "Directory of the inference model.");
+DEFINE_string(word2vec_dirname, "",
+              "Directory of the word2vec inference model.");
+DEFINE_string(book_dirname, "", "Directory of the book inference model.");
 
 namespace paddle {
 
@@ -49,7 +51,7 @@ PaddleTensor LodTensorToPaddleTensor(framework::LoDTensor* t) {
 
 NativeConfig GetConfig() {
   NativeConfig config;
-  config.model_dir = FLAGS_dirname + "/word2vec.inference.model";
+  config.model_dir = FLAGS_word2vec_dirname;
   LOG(INFO) << "dirname  " << config.model_dir;
   config.fraction_of_gpu_memory = 0.15;
 #ifdef PADDLE_WITH_CUDA
@@ -116,7 +118,7 @@ void MainImageClassification(bool use_gpu) {
   NativeConfig config = GetConfig();
   config.use_gpu = use_gpu;
   config.model_dir =
-      FLAGS_dirname + "/image_classification_resnet.inference.model";
+      FLAGS_book_dirname + "/image_classification_resnet.inference.model";
 
   const bool is_combined = false;
   std::vector<std::vector<int64_t>> feed_target_shapes =
@@ -220,7 +222,7 @@ void MainThreadsImageClassification(bool use_gpu) {
   NativeConfig config = GetConfig();
   config.use_gpu = use_gpu;
   config.model_dir =
-      FLAGS_dirname + "/image_classification_resnet.inference.model";
+      FLAGS_book_dirname + "/image_classification_resnet.inference.model";
 
   auto main_predictor = CreatePaddlePredictor<NativeConfig>(config);
   std::vector<framework::LoDTensor> jobs(num_jobs);

paddle/fluid/inference/api/api_tensorrt_subgraph_engine_tester.cc

@@ -29,13 +29,13 @@ void CompareTensorRTWithFluid(bool enable_tensorrt) {
 
   //# 1. Create PaddlePredictor with a config.
   NativeConfig config0;
-  config0.model_dir = FLAGS_dirname + "word2vec.inference.model";
+  config0.model_dir = FLAGS_dirname;
   config0.use_gpu = true;
   config0.fraction_of_gpu_memory = 0.3;
   config0.device = 0;
 
   MixedRTConfig config1;
-  config1.model_dir = FLAGS_dirname + "word2vec.inference.model";
+  config1.model_dir = FLAGS_dirname;
   config1.use_gpu = true;
   config1.fraction_of_gpu_memory = 0.3;
   config1.device = 0;

paddle/fluid/inference/api/demo_ci/run.sh

@@ -62,7 +62,7 @@ for WITH_STATIC_LIB in ON OFF; do
     -DWITH_GPU=$TEST_GPU_CPU \
     -DWITH_STATIC_LIB=$WITH_STATIC_LIB
   make -j
-  word2vec_model=${PADDLE_ROOT}'/build/python/paddle/fluid/tests/book/word2vec.inference.model'
+  word2vec_model=$DATA_DIR'/word2vec/word2vec.inference.model'
   if [ -d $word2vec_model ]; then
     for use_gpu in $use_gpu_list; do
       ./simple_on_word2vec \

paddle/fluid/inference/api/demo_ci/simple_on_word2vec.cc

@@ -70,12 +70,8 @@ void Main(bool use_gpu) {
     // The outputs' buffers are in CPU memory.
     for (size_t i = 0; i < std::min(static_cast<size_t>(5), num_elements);
          i++) {
-      // Here will result random fail, for that the model is trained by CI, the
-      // train phase is not stable, so the result will be random.
-      // TODO(Superjomn) will restore after the model is upload.
-      // CHECK_NEAR(static_cast<float*>(outputs.front().data.data())[i],
-      // result[i],
-      // 0.001);
+      CHECK_NEAR(static_cast<float*>(outputs.front().data.data())[i], result[i],
+                 0.001);
     }
   }
 }

paddle/fluid/inference/test.cmake

+set(INFERENCE_URL "http://paddle-inference-dist.cdn.bcebos.com" CACHE STRING "inference download url")
+set(INFERENCE_DEMO_INSTALL_DIR "${THIRD_PARTY_PATH}/inference_demo" CACHE STRING
+    "A path setting inference demo download directories.")
+function (inference_download install_dir url filename)
+    message(STATUS "Download inference test stuff from ${url}/${filename}")
+    execute_process(COMMAND bash -c "mkdir -p ${install_dir}")
+    execute_process(COMMAND bash -c "cd ${install_dir} && wget -q ${url}/${filename}")
+    message(STATUS "finish downloading ${filename}")
+endfunction()
+
+function (inference_download_and_uncompress install_dir url filename)
+    inference_download(${install_dir} ${url} ${filename})
+    execute_process(COMMAND bash -c "cd ${install_dir} && tar xzf ${filename}")
+endfunction()
+
+set(WORD2VEC_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/word2vec")
+if (NOT EXISTS ${WORD2VEC_INSTALL_DIR})
+    inference_download_and_uncompress(${WORD2VEC_INSTALL_DIR} ${INFERENCE_URL} "word2vec.inference.model.tar.gz")
+endif()
+set(WORD2VEC_MODEL_DIR "${WORD2VEC_INSTALL_DIR}/word2vec.inference.model")
+
+function (inference_base_test TARGET)
+   set(options "")
+   set(oneValueArgs "")
+   set(multiValueArgs SRCS ARGS DEPS)
+   cmake_parse_arguments(base_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
+   if(WITH_GPU)
+       set(mem_opt "--fraction_of_gpu_memory_to_use=0.5")
+   endif()
+   cc_test(${TARGET} SRCS ${base_test_SRCS} DEPS ${base_test_DEPS} ARGS ${mem_opt} ${base_test_ARGS})
+endfunction()

paddle/fluid/inference/tests/api/CMakeLists.txt

-set(INFERENCE_URL "http://paddle-inference-dist.cdn.bcebos.com")
-set(INFERENCE_DEMO_INSTALL_DIR "${THIRD_PARTY_PATH}/inference_demo" CACHE STRING
-    "A path setting inference demo download directories.")
 set(INFERENCE_EXTRA_DEPS paddle_inference_api paddle_fluid_api ir_pass_manager analysis_predictor)
-function (inference_download install_dir url filename)
-    message(STATUS "Download inference test stuff from ${url}/${filename}")
-    execute_process(COMMAND bash -c "mkdir -p ${install_dir}")
-    execute_process(COMMAND bash -c "cd ${install_dir} && wget -q ${url}/${filename}")
-    message(STATUS "finish downloading ${filename}")
-endfunction()
-
-function (inference_download_and_uncompress install_dir url filename)
-    inference_download(${install_dir} ${url} ${filename})
-    execute_process(COMMAND bash -c "cd ${install_dir} && tar xzf ${filename}")
-endfunction()
 
 function(download_model_and_data install_dir model_name data_name)
     if (NOT EXISTS ${install_dir})

paddle/fluid/operators/affine_grid_cudnn_op.cu.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/framework/op_registry.h"
+#include "paddle/fluid/platform/cudnn_helper.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+using ScopedSpatialTransformerDescriptor =
+    platform::ScopedSpatialTransformerDescriptor;
+
+template <typename T>
+class CUDNNAffineGridOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
+                   "It must use CUDAPlace.");
+    auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
+    auto handle = dev_ctx.cudnn_handle();
+    auto* theta = ctx.Input<Tensor>("Theta");
+    auto* output = ctx.Output<Tensor>("Output");
+    const T* theta_data = theta->data<T>();
+
+    int n = theta->dims()[0];
+    auto size_attr = ctx.Attr<std::vector<int>>("output_shape");
+    Tensor h_sizes;
+    int* h_size_data;
+    if (size_attr.size() == 0) {
+      auto* output_shape = ctx.Input<Tensor>("OutputShape");
+      framework::TensorCopy(*output_shape, platform::CPUPlace(), &h_sizes);
+      h_size_data = h_sizes.data<int>();
+    } else {
+      h_size_data = h_sizes.mutable_data<int>({4}, platform::CPUPlace());
+      h_size_data[0] = n;
+      h_size_data[1] = size_attr[1];
+      h_size_data[2] = size_attr[2];
+      h_size_data[3] = size_attr[3];
+    }
+
+    T* output_data = output->mutable_data<T>(
+        {n, h_size_data[2], h_size_data[3], 2}, ctx.GetPlace());
+    ScopedSpatialTransformerDescriptor st_desc;
+    cudnnSpatialTransformerDescriptor_t cudnn_st_desc =
+        st_desc.descriptor<T>(4, h_size_data);
+
+    PADDLE_ENFORCE(platform::dynload::cudnnSpatialTfGridGeneratorForward(
+        handle, cudnn_st_desc, theta_data, output_data));
+  }
+};
+
+template <typename T>
+class CUDNNAffineGridGradOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
+                   "It must use CUDAPlace.");
+    auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
+    auto handle = dev_ctx.cudnn_handle();
+    auto output_grad = ctx.Input<Tensor>(framework::GradVarName("Output"));
+    auto theta_grad = ctx.Output<Tensor>(framework::GradVarName("Theta"));
+
+    int n = output_grad->dims()[0];
+    auto size_attr = ctx.Attr<std::vector<int>>("output_shape");
+    Tensor h_sizes;
+    int* h_size_data;
+    if (size_attr.size() == 0) {
+      auto* output_shape = ctx.Input<Tensor>("OutputShape");
+      framework::TensorCopy(*output_shape, platform::CPUPlace(), &h_sizes);
+      h_size_data = h_sizes.data<int>();
+    } else {
+      h_size_data = h_sizes.mutable_data<int>({4}, platform::CPUPlace());
+      h_size_data[0] = n;
+      h_size_data[1] = size_attr[1];
+      h_size_data[2] = size_attr[2];
+      h_size_data[3] = size_attr[3];
+    }
+
+    ScopedSpatialTransformerDescriptor st_desc;
+    cudnnSpatialTransformerDescriptor_t cudnn_st_desc =
+        st_desc.descriptor<T>(4, h_size_data);
+
+    const T* output_grad_data = output_grad->data<T>();
+    T* theta_grad_data = theta_grad->mutable_data<T>(ctx.GetPlace());
+
+    PADDLE_ENFORCE(platform::dynload::cudnnSpatialTfGridGeneratorBackward(
+        handle, cudnn_st_desc, output_grad_data, theta_grad_data));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace plat = paddle::platform;
+REGISTER_OP_KERNEL(affine_grid, CUDNN, plat::CUDAPlace,
+                   paddle::operators::CUDNNAffineGridOpKernel<float>,
+                   paddle::operators::CUDNNAffineGridOpKernel<double>);
+REGISTER_OP_KERNEL(affine_grid_grad, CUDNN, plat::CUDAPlace,
+                   paddle::operators::CUDNNAffineGridGradOpKernel<float>,
+                   paddle::operators::CUDNNAffineGridGradOpKernel<double>);

paddle/fluid/operators/affine_grid_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/affine_grid_op.h"
+#include <string>
+#include "paddle/fluid/framework/op_registry.h"
+#ifdef PADDLE_WITH_CUDA
+#include "paddle/fluid/platform/cudnn_helper.h"
+#endif
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename T>
+struct Linspace<paddle::platform::CPUDeviceContext, T> {
+  void operator()(T start, T end, int count, framework::Tensor* numbers,
+                  const framework::ExecutionContext& ctx) {
+    T* number_data = numbers->mutable_data<T>({count}, platform::CPUPlace());
+    T slice = (end - start) / (T)(count - 1);
+    for (int i = 0; i < count; ++i) {
+      number_data[i] = start + (T)i * slice;
+    }
+  }
+};
+
+class AffineGridOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("Theta"),
+                   "Input(Theta) of AffineGridOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Output"),
+                   "Output(Output) of AffineGridOp should not be null.");
+    auto theta_dims = ctx->GetInputDim("Theta");
+    PADDLE_ENFORCE(theta_dims.size() == 3,
+                   "AffineGrid's Input(Theta) should be 3-D tensor.");
+
+    auto output_shape = ctx->Attrs().Get<std::vector<int>>("output_shape");
+    if (output_shape.size() == 0) {
+      PADDLE_ENFORCE(ctx->HasInput("OutputShape"),
+                     "Input(OutputShape) of AffineGridOp should not be null if "
+                     "attr(output_shape) is not configured.");
+      auto output_shape_dims = ctx->GetInputDim("OutputShape");
+      PADDLE_ENFORCE(output_shape_dims.size() == 1,
+                     "AffineGrid's Input(OutputShape) should be 1-D tensor.");
+    } else {
+      PADDLE_ENFORCE(output_shape.size() == 4,
+                     "The size of attr(output_shape) should be 4.");
+    }
+
+    PADDLE_ENFORCE(theta_dims[1] == 2, "Input(theta) dims[1] should be 2.");
+    PADDLE_ENFORCE(theta_dims[2] == 3, "Input(theta) dims[2] should be 3.");
+    // N * H * W * 2
+    ctx->SetOutputDim("Output",
+                      framework::make_ddim({theta_dims[0], -1, -1, 2}));
+    ctx->ShareLoD("Theta", "Output");
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    framework::LibraryType library{framework::LibraryType::kPlain};
+#ifdef PADDLE_WITH_CUDA
+    if (platform::CanCUDNNBeUsed(ctx)) {
+      library = framework::LibraryType::kCUDNN;
+    }
+#endif
+    auto data_type = framework::ToDataType(ctx.Input<Tensor>("Theta")->type());
+    return framework::OpKernelType(data_type, ctx.GetPlace(),
+                                   framework::DataLayout::kAnyLayout, library);
+  }
+};
+
+class AffineGridOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput(
+        "Theta",
+        "(Tensor) A batch of affine transform parameters with shape [N, 2, 3]. "
+        "It is used to transform coordinate (x_0, y_0) to coordinate (x_1, "
+        "y_1).");
+    AddInput("OutputShape",
+             "(Tensor) The shape of target image with format [N, C, H, W].")
+        .AsDispensable();
+    AddOutput("Output", "(Tensor) Output Tensor with shape [N, H, W, 2].");
+    AddAttr<bool>(
+        "use_cudnn",
+        "(bool, default false) Only used in cudnn kernel, need install cudnn")
+        .SetDefault(true);
+    AddAttr<std::vector<int>>(
+        "output_shape",
+        "The target output image shape with format [N, C, H, W].")
+        .SetDefault(std::vector<int>());
+
+    AddComment(R"DOC(
+    It generates a grid of (x,y) coordinates using the parameters of the
+    affine transformation that correspond to a set of points where the input
+    feature map should be sampled to produce the transformed output feature map.
+
+    Given:
+        Theta = [[[x_11, x_12, x_13]
+                  [x_14, x_15, x_16]]
+                 [[x_21, x_22, x_23]
+                  [x_24, x_25, x_26]]]
+    
+        OutputShape = [2, 3, 5, 5]
+
+    Step 1:
+
+        Generate relative coordinates according to OutputShape.
+        The values of relative coordinates are in the interval between -1 and 1.
+        The shape of the relative coordinates is [2, H, W] as below:
+    
+        C = [[[-1.  -1.  -1.  -1.  -1. ]
+              [-0.5 -0.5 -0.5 -0.5 -0.5]
+              [ 0.   0.   0.   0.   0. ]
+              [ 0.5  0.5  0.5  0.5  0.5]
+              [ 1.   1.   1.   1.   1. ]] 
+             [[-1.  -0.5  0.   0.5  1. ]
+              [-1.  -0.5  0.   0.5  1. ]
+              [-1.  -0.5  0.   0.5  1. ]
+              [-1.  -0.5  0.   0.5  1. ]
+              [-1.  -0.5  0.   0.5  1. ]]]
+        C[0] is the coordinates in height axis and  C[1] is the coordinates in width axis.
+    
+    Step2:
+        Tanspose and reshape C to shape [H * W, 2] and append ones to last dimension. The we get:
+        C_ = [[-1.  -1.   1. ]
+              [-0.5 -1.   1. ]
+              [ 0.  -1.   1. ]
+              [ 0.5 -1.   1. ]
+              [ 1.  -1.   1. ]
+              [-1.  -0.5  1. ]
+              [-0.5 -0.5  1. ]
+              [ 0.  -0.5  1. ]
+              [ 0.5 -0.5  1. ]
+              [ 1.  -0.5  1. ]
+              [-1.   0.   1. ]
+              [-0.5  0.   1. ]
+              [ 0.   0.   1. ]
+              [ 0.5  0.   1. ]
+              [ 1.   0.   1. ]
+              [-1.   0.5  1. ]
+              [-0.5  0.5  1. ]
+              [ 0.   0.5  1. ]
+              [ 0.5  0.5  1. ]
+              [ 1.   0.5  1. ]
+              [-1.   1.   1. ]
+              [-0.5  1.   1. ]
+              [ 0.   1.   1. ]
+              [ 0.5  1.   1. ]
+              [ 1.   1.   1. ]]
+    Step3:
+        Compute output by equation $$Output[i] = C_ * Theta[i]^T$$
+    )DOC");
+  }
+};
+
+class AffineGridOpGrad : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    auto theta_dims = ctx->GetInputDim("Theta");
+    if (ctx->HasOutput(framework::GradVarName("Theta"))) {
+      ctx->SetOutputDim(framework::GradVarName("Theta"), theta_dims);
+    }
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    framework::LibraryType library_{framework::LibraryType::kPlain};
+#ifdef PADDLE_WITH_CUDA
+    if (platform::CanCUDNNBeUsed(ctx)) {
+      library_ = framework::LibraryType::kCUDNN;
+    }
+#endif
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<Tensor>("Theta")->type()),
+        ctx.GetPlace(), framework::DataLayout::kAnyLayout, library_);
+  }
+};
+
+class AffineGridGradMaker : public framework::SingleGradOpDescMaker {
+ public:
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  std::unique_ptr<framework::OpDesc> Apply() const override {
+    auto* op = new framework::OpDesc();
+    op->SetType("affine_grid_grad");
+    op->SetInput("Theta", Input("Theta"));
+    op->SetInput("OutputShape", Input("OutputShape"));
+    op->SetInput(framework::GradVarName("Output"), OutputGrad("Output"));
+
+    op->SetAttrMap(Attrs());
+
+    op->SetOutput(framework::GradVarName("Theta"), InputGrad("Theta"));
+    return std::unique_ptr<framework::OpDesc>(op);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(affine_grid, ops::AffineGridOp, ops::AffineGridOpMaker,
+                  ops::AffineGridGradMaker);
+REGISTER_OPERATOR(affine_grid_grad, ops::AffineGridOpGrad);
+
+REGISTER_OP_CPU_KERNEL(
+    affine_grid,
+    ops::AffineGridOpKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::AffineGridOpKernel<paddle::platform::CPUDeviceContext, double>);
+REGISTER_OP_CPU_KERNEL(
+    affine_grid_grad,
+    ops::AffineGridGradOpKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::AffineGridGradOpKernel<paddle::platform::CPUDeviceContext, double>);

paddle/fluid/operators/affine_grid_op.h

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+#include <vector>
+#include "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/math/blas.h"
+#include "paddle/fluid/operators/math/math_function.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+template <typename T, size_t D, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
+
+using Array1 = Eigen::DSizes<int64_t, 1>;
+using Array2 = Eigen::DSizes<int64_t, 2>;
+using Array3 = Eigen::DSizes<int64_t, 3>;
+using Array4 = Eigen::DSizes<int64_t, 4>;
+
+/**
+ *Return a tensor with evenly spaced numbers over a specified interval.
+ */
+template <typename DeviceContext, typename T>
+struct Linspace {
+  void operator()(T start, T end, int count, framework::Tensor* numbers,
+                  const framework::ExecutionContext& ctx);
+};
+
+template <typename DeviceContext, typename T>
+inline void GetIdxMap(int n, int h, int w, Tensor* grid,
+                      const framework::ExecutionContext& ctx) {
+  auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
+  grid->mutable_data<T>({n, h, w, 3}, ctx.GetPlace());
+  auto grid_t = EigenTensor<T, 4>::From(*grid);
+  // Get indexes of height with shape [height, width, 1]
+  Tensor h_idx;
+  Linspace<DeviceContext, T> linspace;
+  linspace((T)-1, (T)1, h, &h_idx, ctx);
+  auto h_idx_t = EigenTensor<T, 1>::From(h_idx);
+  // Get indexes of width with shape [height, width, 1]
+  Tensor w_idx;
+  linspace((T)-1, (T)1, w, &w_idx, ctx);
+  auto w_idx_t = EigenTensor<T, 1>::From(w_idx);
+  // Get constant ones tensor with shape [height, width, 1]
+  Tensor ones;
+  ones.mutable_data<T>({h, w, 1}, ctx.GetPlace());
+  auto ones_t = EigenTensor<T, 3>::From(ones).setConstant((T)1);
+  // Get grid tensor with shape [n, h, w, 3] by concatenating h_idx, w_idx and
+  // ones
+  Tensor w_idx_map;
+  w_idx_map.mutable_data<T>({h, w, 1}, ctx.GetPlace());
+  auto w_idx_map_t = EigenTensor<T, 3>::From(w_idx_map);
+  Tensor h_idx_map;
+  h_idx_map.mutable_data<T>({h, w, 1}, ctx.GetPlace());
+  auto h_idx_map_t = EigenTensor<T, 3>::From(h_idx_map);
+  Tensor w_h_idx_map;
+  w_h_idx_map.mutable_data<T>({h, w, 2}, ctx.GetPlace());
+  auto w_h_idx_map_t = EigenTensor<T, 3>::From(w_h_idx_map);
+  Tensor w_h_one_idx_map;
+  w_h_one_idx_map.mutable_data<T>({h, w, 3}, ctx.GetPlace());
+  auto w_h_one_idx_map_t = EigenTensor<T, 3>::From(w_h_one_idx_map);
+
+  w_idx_map_t.device(place) = w_idx_t.reshape(Array2(1, w))
+                                  .broadcast(Array2(h, 1))
+                                  .reshape(Array3(h, w, 1));
+
+  h_idx_map_t.device(place) = h_idx_t.reshape(Array2(1, h))
+                                  .broadcast(Array2(w, 1))
+                                  .shuffle(Array2(1, 0))
+                                  .reshape(Array3(h, w, 1));
+
+  w_h_idx_map_t.device(place) = w_idx_map_t.concatenate(h_idx_map_t, 2);
+  w_h_one_idx_map_t.device(place) = w_h_idx_map_t.concatenate(ones_t, 2);
+  grid_t.device(place) = w_h_one_idx_map_t.reshape(Array4(1, h, w, 3))
+                             .broadcast(Array4(n, 1, 1, 1));
+}
+
+template <typename DeviceContext, typename T>
+class AffineGridOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* theta = ctx.Input<Tensor>("Theta");
+    int n = theta->dims()[0];
+    auto size_attr = ctx.Attr<std::vector<int>>("output_shape");
+    int h = 0;
+    int w = 0;
+    if (size_attr.size() == 0) {
+      auto* output_shape = ctx.Input<Tensor>("OutputShape");
+      Tensor h_sizes;
+      framework::TensorCopy(*output_shape, platform::CPUPlace(), &h_sizes);
+      const int* h_size_data = h_sizes.data<int>();
+      h = h_size_data[2];
+      w = h_size_data[3];
+    } else {
+      h = size_attr[2];
+      w = size_attr[3];
+    }
+    auto* output = ctx.Output<Tensor>("Output");
+    output->mutable_data<T>({n, h, w, 2}, ctx.GetPlace());
+    math::SetConstant<DeviceContext, T>()(
+        ctx.template device_context<DeviceContext>(), output,
+        static_cast<T>(0));
+    Tensor grid;
+    GetIdxMap<DeviceContext, T>(n, h, w, &grid, ctx);
+    // output = grid * theta.T
+    // TODO(wanghaoshuang): Refine batched matrix multiply
+    auto blas = math::GetBlas<DeviceContext, T>(ctx);
+    for (int i = 0; i < n; ++i) {
+      Tensor sliced_grid = grid.Slice(i, i + 1).Resize({h * w, 3});
+      Tensor sliced_theta = theta->Slice(i, i + 1).Resize({2, 3});
+      Tensor sliced_out = output->Slice(i, i + 1).Resize({h * w, 2});
+      blas.MatMul(sliced_grid, false, sliced_theta, true, T(1), &sliced_out,
+                  T(0));
+    }
+  }
+};
+
+template <typename DeviceContext, typename T>
+class AffineGridGradOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto output_grad = ctx.Input<Tensor>(framework::GradVarName("Output"));
+    auto theta_grad = ctx.Output<Tensor>(framework::GradVarName("Theta"));
+    int n = output_grad->dims()[0];
+    auto size_attr = ctx.Attr<std::vector<int>>("output_shape");
+    int h = 0;
+    int w = 0;
+    if (size_attr.size() == 0) {
+      auto* output_shape = ctx.Input<Tensor>("OutputShape");
+      Tensor h_sizes;
+      framework::TensorCopy(*output_shape, platform::CPUPlace(), &h_sizes);
+      const int* h_size_data = h_sizes.data<int>();
+      h = h_size_data[2];
+      w = h_size_data[3];
+    } else {
+      h = size_attr[2];
+      w = size_attr[3];
+    }
+    theta_grad->mutable_data<T>({n, 2, 3}, ctx.GetPlace());
+    math::SetConstant<DeviceContext, T>()(
+        ctx.template device_context<DeviceContext>(), theta_grad,
+        static_cast<T>(0));
+    Tensor grid;
+    GetIdxMap<DeviceContext, T>(n, h, w, &grid, ctx);
+    // output = grid * theta.T
+    // TODO(wanghaoshuang): Refine batched matrix multiply
+    auto blas = math::GetBlas<DeviceContext, T>(ctx);
+    for (int i = 0; i < n; ++i) {
+      Tensor sliced_grid = grid.Slice(i, i + 1).Resize({h * w, 3});
+      Tensor sliced_out_grad = output_grad->Slice(i, i + 1).Resize({h * w, 2});
+      Tensor sliced_theta_grad = theta_grad->Slice(i, i + 1).Resize({2, 3});
+      blas.MatMul(sliced_out_grad, true, sliced_grid, false, T(1),
+                  &sliced_theta_grad, T(0));
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/checkpoint_notify_op.cc

@@ -38,9 +38,10 @@ class CheckpointNotifyOp : public framework::OperatorBase {
     std::vector<std::string> epmap = Attr<std::vector<std::string>>("epmap");
     std::string dir = Attr<std::string>("dir");
     std::string lookup_table_name = Attr<std::string>("lookup_table");
+    int trainer_id = Attr<int>("trainer_id");
 
     distributed::RPCClient* rpc_client =
-        distributed::RPCClient::GetInstance<RPCCLIENT_T>();
+        distributed::RPCClient::GetInstance<RPCCLIENT_T>(trainer_id);
     for (size_t i = 0; i < epmap.size(); i++) {
       auto lookup_table_save_dir =
           string::Sprintf("%s/%s_%d", dir, lookup_table_name, i);
@@ -63,6 +64,7 @@ class CheckpointNotifyOpMaker : public framework::OpProtoAndCheckerMaker {
         "dir", "(string, default '') indicate the folder checkpoint will use");
     AddAttr<std::string>("lookup_table",
                          "(string, default '') the lookup table name");
+    AddAttr<int>("trainer_id", "trainer id from 0 ~ worker_num.").SetDefault(0);
     AddComment(R"DOC(
 CheckpointNotify operator
 

paddle/fluid/operators/conv_mkldnn_op.cc

@@ -15,6 +15,8 @@
 #include "paddle/fluid/operators/conv_op.h"
 #include "paddle/fluid/platform/mkldnn_helper.h"
 
+#include "paddle/fluid/framework/data_layout_transform.h"
+
 namespace paddle {
 namespace operators {
 
@@ -57,6 +59,11 @@ class ConvMKLDNNHandler : public platform::MKLDNNHandler {
     return conv_pd_->dst_primitive_desc().get_size();
   }
 
+  mkldnn::memory::format GetDstFormat() const {
+    return static_cast<mkldnn::memory::format>(
+        conv_pd_->dst_primitive_desc().desc().data.format);
+  }
+
   size_t GetDiffWeightsMemorySize() const {
     return conv_bwd_weights_pd_->diff_weights_primitive_desc().get_size();
   }
@@ -108,6 +115,20 @@ class ConvMKLDNNHandler : public platform::MKLDNNHandler {
                                "@data-weights_mem_p", pipeline);
   }
 
+  std::shared_ptr<mkldnn::memory> AcquireResidualDataMemory(
+      const mkldnn::memory::desc& md, void* ptr) {
+    return this->AcquireMemory(md, ptr, "@user_residual_data_mem_p");
+  }
+
+  std::shared_ptr<mkldnn::memory> AcquireDstMemoryFromResidualDataMemory(
+      const std::shared_ptr<mkldnn::memory>& user_residual_memory_p,
+      void* dst_ptr,
+      std::vector<mkldnn::primitive>& pipeline) {  // NOLINT
+    return this->AcquireMemory(user_residual_memory_p,
+                               this->AcquireDstMemoryFromPrimitive(dst_ptr),
+                               "@residual_data_mem_p", pipeline);
+  }
+
   std::shared_ptr<mkldnn::memory> AcquireDiffSrcMemoryFromDataPrimitive(
       void* ptr) {
     return this->AcquireMemoryFromPrimitive(
@@ -386,7 +407,13 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
     auto user_weights_memory_p = handler.AcquireWeightsMemory(
         user_weights_md, to_void_cast<T>(filter_data));
 
-    T* output_data = nullptr;
+    // create reorder primitive if the input format is not the preferred one
+    auto src_memory_p =
+        handler.AcquireSrcMemoryFromPrimitive(user_src_memory_p, pipeline);
+    auto weights_memory_p = handler.AcquireWeightsMemoryFromPrimitive(
+        user_weights_memory_p, pipeline, is_test);
+
+    std::shared_ptr<mkldnn::memory> dst_memory_p;
 
     if (fuse_residual_conn) {
       auto residual_param = ctx.Input<Tensor>("ResidualData");
@@ -399,20 +426,33 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
                         "Output and elementwise parameter need to have the "
                         "same dimension sizes");
 
+      if (residual_param->format() != handler.GetDstFormat()) {
+        auto output_data =
+            output->mutable_data<T>(ctx.GetPlace(), handler.GetDstMemorySize());
+        auto residual_data_tz =
+            paddle::framework::vectorize2int(residual_param->dims());
+        auto residual_data_type =
+            paddle::framework::ToMKLDNNDataType(residual_param->type());
+
+        auto user_residual_md = platform::MKLDNNMemDesc(
+            residual_data_tz, residual_data_type, residual_param->format());
+        auto user_residual_memory_p = handler.AcquireResidualDataMemory(
+            user_residual_md, to_void_cast<T>(residual_param_data));
+
+        dst_memory_p = handler.AcquireDstMemoryFromResidualDataMemory(
+            user_residual_memory_p, to_void_cast<T>(output_data), pipeline);
+      } else {
         output->ShareDataWith(*residual_param);
-      output_data = output->mutable_data<T>(ctx.GetPlace());
+        auto output_data = output->mutable_data<T>(ctx.GetPlace());
+        dst_memory_p =
+            handler.AcquireDstMemoryFromPrimitive(to_void_cast<T>(output_data));
+      }
     } else {
-      output_data =
+      auto output_data =
           output->mutable_data<T>(ctx.GetPlace(), handler.GetDstMemorySize());
-    }
-
-    // create reorder primitive if the input format is not the preferred one
-    auto src_memory_p =
-        handler.AcquireSrcMemoryFromPrimitive(user_src_memory_p, pipeline);
-    auto weights_memory_p = handler.AcquireWeightsMemoryFromPrimitive(
-        user_weights_memory_p, pipeline, is_test);
-    auto dst_memory_p =
+      dst_memory_p =
           handler.AcquireDstMemoryFromPrimitive(to_void_cast<T>(output_data));
+    }
 
     // create convolution op primitive
     std::shared_ptr<mkldnn::convolution_forward> conv_p;

paddle/fluid/operators/delete_var_op.cc

@@ -32,6 +32,11 @@ class DeleteVarOp : public framework::OperatorBase {
   }
 };
 
+class DeleteVarOpShapeInference : public framework::InferShapeBase {
+ public:
+  void operator()(framework::InferShapeContext *ctx) const override {}
+};
+
 class DeleteVarOpInfoMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
@@ -48,4 +53,5 @@ It should not be configured by users directly.
 
 REGISTER_OPERATOR(delete_var, paddle::operators::DeleteVarOp,
                   paddle::framework::EmptyGradOpMaker,
-                  paddle::operators::DeleteVarOpInfoMaker);
+                  paddle::operators::DeleteVarOpInfoMaker,
+                  paddle::operators::DeleteVarOpShapeInference);

paddle/fluid/operators/distributed/grpc_client.cc

@@ -79,7 +79,7 @@ VarHandlePtr GRPCClient::AsyncSendVar(const std::string& ep,
     auto* var = p_scope->FindVar(var_name_val);
 
     ::grpc::ByteBuffer req;
-    SerializeToByteBuffer(var_name_val, var, *p_ctx, &req);
+    SerializeToByteBuffer(var_name_val, var, *p_ctx, &req, "", trainer_id_);
 
     VLOG(3) << s->GetVarHandlePtr()->String() << " begin";
 
@@ -105,7 +105,10 @@ VarHandlePtr GRPCClient::AsyncSendVar(const std::string& ep,
 void ProcGetResponse(const VarHandle& var_h,
                      const ::grpc::ByteBuffer& ret_msg) {
   framework::Variable* outvar = nullptr;
-  DeserializeFromByteBuffer(ret_msg, *var_h.ctx(), var_h.scope(), &outvar);
+  // get response's trainer_id is not used
+  int trainer_id;
+  DeserializeFromByteBuffer(ret_msg, *var_h.ctx(), var_h.scope(), &outvar,
+                            &trainer_id);
 }
 
 template <typename T>
@@ -135,6 +138,7 @@ VarHandlePtr GRPCClient::AsyncGetVar(const std::string& ep,
     // prepare input
     sendrecv::VariableMessage req;
     req.set_varname(var_name_val);
+    req.set_trainer_id(trainer_id_);
     ::grpc::ByteBuffer buf;
     RequestToByteBuffer<sendrecv::VariableMessage>(req, &buf);
 

paddle/fluid/operators/distributed/grpc_serde.cc

@@ -34,8 +34,8 @@ namespace distributed {
 
 void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
                            const platform::DeviceContext& ctx,
-                           ::grpc::ByteBuffer* msg,
-                           const std::string& out_name) {
+                           ::grpc::ByteBuffer* msg, const std::string& out_name,
+                           const int trainer_id) {
   platform::RecordRPCEvent record_event("serial", &ctx);
   // Default DestroyCallback does nothing, When using GPU
   // the CPU buffer need to be freed.
@@ -45,6 +45,7 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
   size_t payload_size;
 
   request.set_varname(name);
+  request.set_trainer_id(trainer_id);
   // Note: normally the profiler is enabled in 1 trainer, hence only
   // 1 trainer returns true for ShouldSendProfileState(). It tells PS
   // servers the trainer's profiling state so that PS can follow the
@@ -147,11 +148,12 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
 void DeserializeFromByteBuffer(const ::grpc::ByteBuffer& msg,
                                const platform::DeviceContext& ctx,
                                const framework::Scope* scope,
-                               framework::Variable** var) {
+                               framework::Variable** var, int* trainer_id) {
   platform::RecordRPCEvent record_event("deserial", &ctx);
   operators::distributed::GRPCVariableResponse resp(scope, &ctx);
   PADDLE_ENFORCE(resp.Parse(msg) == 0, "parse bytebuffer to tensor error!");
   *var = resp.GetVar();
+  *trainer_id = resp.GetTrainerId();
 }
 
 }  // namespace distributed

paddle/fluid/operators/distributed/grpc_serde.h

@@ -38,12 +38,13 @@ typedef void (*DestroyCallback)(void*);
 void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
                            const platform::DeviceContext& ctx,
                            ::grpc::ByteBuffer* msg,
-                           const std::string& out_varname = std::string());
+                           const std::string& out_varname = std::string(),
+                           const int trainer_id = 0);
 
 void DeserializeFromByteBuffer(const ::grpc::ByteBuffer& msg,
                                const platform::DeviceContext& ctx,
                                const framework::Scope* scope,
-                               framework::Variable** var);
+                               framework::Variable** var, int* trainer_id);
 
 }  // namespace distributed
 }  // namespace operators

paddle/fluid/operators/distributed/grpc_server.cc

@@ -102,9 +102,10 @@ class RequestSend final : public RequestBase {
 
     auto scope = request_->GetMutableLocalScope();
     auto invar = request_->GetVar();
+    int trainer_id = request_->GetTrainerId();
     framework::Variable* outvar = nullptr;
 
-    request_handler_->Handle(varname, scope, invar, &outvar);
+    request_handler_->Handle(varname, scope, invar, &outvar, trainer_id);
     Finish(reply_, &responder_);
   }
 
@@ -133,13 +134,14 @@ class RequestGet final : public RequestBase {
   void Process() override {
     // proc request.
     std::string varname = request_.varname();
+    int trainer_id = request_.trainer_id();
     VLOG(4) << "RequestGet " << varname;
 
     auto scope = request_handler_->scope();
     auto invar = scope->FindVar(varname);
     framework::Variable* outvar = nullptr;
 
-    request_handler_->Handle(varname, scope, invar, &outvar);
+    request_handler_->Handle(varname, scope, invar, &outvar, trainer_id);
 
     if (outvar) {
       SerializeToByteBuffer(varname, outvar, *request_handler_->dev_ctx(),
@@ -179,6 +181,7 @@ class RequestPrefetch final : public RequestBase {
     // prefetch process...
     std::string in_var_name = request_->Varname();
     std::string out_var_name = request_->OutVarname();
+    int trainer_id = request_->GetTrainerId();
     VLOG(4) << "RequestPrefetch, in_var_name: " << in_var_name
             << " out_var_name: " << out_var_name;
 
@@ -187,7 +190,8 @@ class RequestPrefetch final : public RequestBase {
     // out var must be created in local scope!
     framework::Variable* outvar = scope->Var(out_var_name);
 
-    request_handler_->Handle(in_var_name, scope, invar, &outvar, out_var_name);
+    request_handler_->Handle(in_var_name, scope, invar, &outvar, trainer_id,
+                             out_var_name);
 
     SerializeToByteBuffer(out_var_name, outvar, *request_handler_->dev_ctx(),
                           &reply_);
@@ -225,12 +229,13 @@ class RequestCheckpointNotify final : public RequestBase {
 
     std::string checkpoint_notify = request_->Varname();
     std::string checkpoint_dir = request_->OutVarname();
+    int trainer_id = request_->GetTrainerId();
 
     VLOG(4) << "RequestCheckpointNotify notify: " << checkpoint_notify
             << ", dir: " << checkpoint_dir;
 
     request_handler_->Handle(checkpoint_notify, scope, nullptr, nullptr,
-                             checkpoint_dir);
+                             trainer_id, checkpoint_dir);
     Finish(reply_, &responder_);
   }
 

paddle/fluid/operators/distributed/grpc_variable_response.cc

@@ -293,6 +293,14 @@ int GRPCVariableResponse::Parse(Source* source) {
         }
         break;
       }
+      case sendrecv::VariableMessage::kTrainerIdFieldNumber: {
+        uint64_t trainer_id = 0;
+        if (!input.ReadVarint64(&trainer_id)) {
+          return tag;
+        }
+        meta_.set_trainer_id(trainer_id);
+        break;
+      }
       default: {
         // Unknown tag, return unknown error.
         return -1;

paddle/fluid/operators/distributed/request_handler.h

@@ -190,6 +190,7 @@ class RequestHandler {
   //    }
   virtual bool Handle(const std::string& varname, framework::Scope* scope,
                       framework::Variable* var, framework::Variable** outvar,
+                      const int trainer_id,
                       const std::string& out_var_name = "") = 0;
 
  protected:

paddle/fluid/operators/distributed/request_handler_impl.cc

@@ -36,6 +36,7 @@ bool RequestSendHandler::Handle(const std::string& varname,
                                 framework::Scope* scope,
                                 framework::Variable* invar,
                                 framework::Variable** outvar,
+                                const int trainer_id,
                                 const std::string& out_var_name) {
   VLOG(4) << "RequestSendHandler:" << varname;
 
@@ -76,6 +77,7 @@ bool RequestGetHandler::Handle(const std::string& varname,
                                framework::Scope* scope,
                                framework::Variable* invar,
                                framework::Variable** outvar,
+                               const int trainer_id,
                                const std::string& out_var_name) {
   VLOG(4) << "RequestGetHandler:" << varname;
   if (sync_mode_) {
@@ -88,6 +90,19 @@ bool RequestGetHandler::Handle(const std::string& varname,
     }
   } else {
     if (varname != FETCH_BARRIER_MESSAGE && varname != COMPLETE_MESSAGE) {
+      if (enable_dc_asgd_) {
+        // NOTE: the format is determined by distributed_transpiler.py
+        std::string param_bak_name =
+            string::Sprintf("%s.trainer_%d_bak", varname, trainer_id);
+        VLOG(3) << "getting " << param_bak_name << " trainer_id " << trainer_id;
+        auto var = scope_->FindVar(varname);
+        auto t_orig = var->Get<framework::LoDTensor>();
+        auto param_bak = scope_->Var(param_bak_name);
+        auto t = param_bak->GetMutable<framework::LoDTensor>();
+        t->mutable_data(dev_ctx_->GetPlace(), t_orig.type());
+        VLOG(3) << "copying " << varname << " to " << param_bak_name;
+        framework::TensorCopy(t_orig, dev_ctx_->GetPlace(), t);
+      }
       *outvar = scope_->FindVar(varname);
     }
   }
@@ -98,6 +113,7 @@ bool RequestPrefetchHandler::Handle(const std::string& varname,
                                     framework::Scope* scope,
                                     framework::Variable* invar,
                                     framework::Variable** outvar,
+                                    const int trainer_id,
                                     const std::string& out_var_name) {
   VLOG(4) << "RequestPrefetchHandler " << varname;
 
@@ -113,6 +129,7 @@ bool RequestCheckpointHandler::Handle(const std::string& varname,
                                       framework::Scope* scope,
                                       framework::Variable* invar,
                                       framework::Variable** outvar,
+                                      const int trainer_id,
                                       const std::string& out_var_name) {
   PADDLE_ENFORCE(
       checkpoint_notify_id != -1,

paddle/fluid/operators/distributed/request_handler_impl.h

@@ -36,20 +36,34 @@ namespace distributed {
 
 class RequestSendHandler final : public RequestHandler {
  public:
-  explicit RequestSendHandler(bool sync_mode) : RequestHandler(sync_mode) {}
+  explicit RequestSendHandler(bool sync_mode, bool enable_dc_asgd = false)
+      : RequestHandler(sync_mode) {
+    enable_dc_asgd_ = enable_dc_asgd;
+  }
   virtual ~RequestSendHandler() {}
   bool Handle(const std::string& varname, framework::Scope* scope,
               framework::Variable* var, framework::Variable** outvar,
+              const int trainer_id,
               const std::string& out_var_name = "") override;
+
+ private:
+  bool enable_dc_asgd_;
 };
 
 class RequestGetHandler final : public RequestHandler {
  public:
-  explicit RequestGetHandler(bool sync_mode) : RequestHandler(sync_mode) {}
+  explicit RequestGetHandler(bool sync_mode, bool enable_dc_asgd = false)
+      : RequestHandler(sync_mode) {
+    enable_dc_asgd_ = enable_dc_asgd;
+  }
   virtual ~RequestGetHandler() {}
   bool Handle(const std::string& varname, framework::Scope* scope,
               framework::Variable* var, framework::Variable** outvar,
+              const int trainer_id,
               const std::string& out_var_name = "") override;
+
+ private:
+  bool enable_dc_asgd_;
 };
 
 class RequestPrefetchHandler final : public RequestHandler {
@@ -58,6 +72,7 @@ class RequestPrefetchHandler final : public RequestHandler {
   virtual ~RequestPrefetchHandler() {}
   bool Handle(const std::string& varname, framework::Scope* scope,
               framework::Variable* var, framework::Variable** outvar,
+              const int trainer_id,
               const std::string& out_var_name = "") override;
 };
 
@@ -70,6 +85,7 @@ class RequestCheckpointHandler final : public RequestHandler {
   virtual ~RequestCheckpointHandler() {}
   bool Handle(const std::string& varname, framework::Scope* scope,
               framework::Variable* var, framework::Variable** outvar,
+              const int trainer_id,
               const std::string& out_var_name = "") override;
 
  private:

paddle/fluid/operators/distributed/rpc_client.cc

@@ -24,6 +24,7 @@ namespace distributed {
 
 std::once_flag RPCClient::init_flag_;
 std::unique_ptr<RPCClient> RPCClient::rpc_client_(nullptr);
+int RPCClient::trainer_id_ = 0;
 
 }  // namespace distributed
 }  // namespace operators

paddle/fluid/operators/distributed/rpc_client.h

@@ -72,14 +72,15 @@ class RPCClient {
   virtual bool Wait() = 0;
 
   template <typename T>
-  static RPCClient* GetInstance() {
-    std::call_once(init_flag_, &RPCClient::Init<T>);
+  static RPCClient* GetInstance(int trainer_id) {
+    std::call_once(init_flag_, &RPCClient::Init<T>, trainer_id);
     return rpc_client_.get();
   }
 
   // Init is called by GetInstance.
   template <typename T>
-  static void Init() {
+  static void Init(int trainer_id) {
+    trainer_id_ = trainer_id;
     if (rpc_client_.get() == nullptr) {
       rpc_client_.reset(new T());
       rpc_client_->InitImpl();
@@ -88,6 +89,8 @@ class RPCClient {
 
  protected:
   virtual void InitImpl() {}
+  // each trainer have exact one trainer id, it should be static
+  static int trainer_id_;
 
  private:
   static std::once_flag init_flag_;

paddle/fluid/operators/distributed/rpc_server_test.cc

@@ -125,7 +125,7 @@ TEST(PREFETCH, CPU) {
   g_req_handler.reset(new distributed::RequestPrefetchHandler(true));
   g_rpc_service.reset(new RPCSERVER_T("127.0.0.1:0", 1));
   distributed::RPCClient* client =
-      distributed::RPCClient::GetInstance<RPCCLIENT_T>();
+      distributed::RPCClient::GetInstance<RPCCLIENT_T>(0);
 
   std::thread server_thread(StartServer, distributed::kRequestPrefetch);
   g_rpc_service->WaitServerReady();
@@ -165,7 +165,7 @@ TEST(COMPLETE, CPU) {
   g_req_handler.reset(new distributed::RequestSendHandler(true));
   g_rpc_service.reset(new RPCSERVER_T("127.0.0.1:0", 2));
   distributed::RPCClient* client =
-      distributed::RPCClient::GetInstance<RPCCLIENT_T>();
+      distributed::RPCClient::GetInstance<RPCCLIENT_T>(0);
   PADDLE_ENFORCE(client != nullptr);
   std::thread server_thread(StartServer, distributed::kRequestSend);
   g_rpc_service->WaitServerReady();

paddle/fluid/operators/distributed/send_recv.proto.in

@@ -79,6 +79,7 @@ message VariableMessage {
   // server stops profiling and generates a profile to /tmp/profile_ps_*
   // when profile switches from 1 to 2.
   int64 profile = 11;
+  int64 trainer_id = 12;
 }
 
 message VoidMessage {}

paddle/fluid/operators/distributed/variable_response.h

@@ -92,6 +92,8 @@ class VariableResponse {
     return scope_->FindVar(meta_.varname());
   }
 
+  int GetTrainerId() { return static_cast<int>(meta_.trainer_id()); }
+
  protected:
   bool ReadRaw(::google::protobuf::io::CodedInputStream* input,
                const platform::DeviceContext& dev_ctx, platform::Place place,

paddle/fluid/operators/fetch_barrier_op.cc

@@ -37,7 +37,8 @@ class FetchBarrierOp : public framework::OperatorBase {
                const platform::Place& place) const override {
     std::vector<std::string> eps = Attr<std::vector<std::string>>("endpoints");
     distributed::RPCClient* rpc_client =
-        distributed::RPCClient::GetInstance<RPCCLIENT_T>();
+        distributed::RPCClient::GetInstance<RPCCLIENT_T>(
+            Attr<int>("trainer_id"));
 
     PADDLE_ENFORCE(rpc_client->Wait(), "internal error in RPCClient");
 
@@ -61,6 +62,7 @@ This operator will send a send barrier signal to list_and_serv op, so that
 the Parameter Server would knew all variables have been sent.
 )DOC");
 
+    AddAttr<int>("trainer_id", "trainer id from 0 ~ worker_num.").SetDefault(0);
     AddAttr<std::vector<std::string>>("endpoints",
                                       "(string vector, default 127.0.0.1:6164)"
                                       "Server endpoints to send variables to.")

paddle/fluid/operators/gen_nccl_id_op.cc

@@ -61,7 +61,7 @@ class GenNCCLIdOp : public framework::OperatorBase {
     std::vector<std::string> endpoint_list =
         Attr<std::vector<std::string>>("endpoint_list");
     distributed::RPCClient* client =
-        distributed::RPCClient::GetInstance<RPCCLIENT_T>();
+        distributed::RPCClient::GetInstance<RPCCLIENT_T>(0);
 
     for (auto& ep : endpoint_list) {
       VLOG(3) << "sending nccl id to " << ep;

paddle/fluid/operators/grid_sampler_cudnn_op.cu.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/framework/op_registry.h"
+#include "paddle/fluid/platform/cudnn_helper.h"
+
+namespace paddle {
+namespace operators {
+
+using framework::Tensor;
+using ScopedTensorDescriptor = platform::ScopedTensorDescriptor;
+using DataLayout = platform::DataLayout;
+using ScopedSpatialTransformerDescriptor =
+    platform::ScopedSpatialTransformerDescriptor;
+template <typename T>
+using CudnnDataType = platform::CudnnDataType<T>;
+
+template <typename T>
+class CUDNNGridSampleOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
+                   "It must use CUDAPlace");
+    auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
+    auto handle = dev_ctx.cudnn_handle();
+    auto* input = ctx.Input<Tensor>("X");
+    auto* grid = ctx.Input<Tensor>("Grid");
+    auto* output = ctx.Output<Tensor>("Output");
+
+    int n = input->dims()[0];
+    int c = input->dims()[1];
+    int h = input->dims()[2];
+    int w = input->dims()[3];
+    const int size[4] = {n, c, h, w};
+
+    const T* input_data = input->data<T>();
+    const T* grid_data = grid->data<T>();
+    T* output_data = output->mutable_data<T>({n, c, h, w}, ctx.GetPlace());
+
+    ScopedSpatialTransformerDescriptor st_desc;
+    cudnnSpatialTransformerDescriptor_t cudnn_st_desc =
+        st_desc.descriptor<T>(4, size);
+
+    ScopedTensorDescriptor input_desc;
+    ScopedTensorDescriptor output_desc;
+    cudnnTensorDescriptor_t cudnn_input_desc = input_desc.descriptor<T>(
+        DataLayout::kNCHW, framework::vectorize2int(input->dims()));
+    cudnnTensorDescriptor_t cudnn_output_desc = output_desc.descriptor<T>(
+        DataLayout::kNCHW, framework::vectorize2int(output->dims()));
+
+    CUDNN_ENFORCE(platform::dynload::cudnnSpatialTfSamplerForward(
+        handle, cudnn_st_desc, CudnnDataType<T>::kOne(), cudnn_input_desc,
+        input_data, grid_data, CudnnDataType<T>::kZero(), cudnn_output_desc,
+        output_data));
+  }
+};
+
+template <typename T>
+class CUDNNGridSampleGradOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
+                   "It must use CUDAPlace");
+    auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
+    auto handle = dev_ctx.cudnn_handle();
+    auto* input = ctx.Input<Tensor>("X");
+    auto* grid = ctx.Input<Tensor>("Grid");
+    auto* output_grad = ctx.Input<Tensor>(framework::GradVarName("Output"));
+    auto* input_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto* grid_grad = ctx.Output<Tensor>(framework::GradVarName("Grid"));
+
+    auto output_grad_dims = output_grad->dims();
+    const int n = output_grad_dims[0];
+    const int c = output_grad_dims[1];
+    const int h = output_grad_dims[2];
+    const int w = output_grad_dims[3];
+    const int size[4] = {n, c, h, w};
+
+    ScopedSpatialTransformerDescriptor st_dest;
+    cudnnSpatialTransformerDescriptor_t cudnn_st_dest =
+        st_dest.descriptor<T>(4, size);
+
+    const T* input_data = input->data<T>();
+    const T* grid_data = grid->data<T>();
+    const T* output_grad_data = output_grad->data<T>();
+    T* input_grad_data =
+        input_grad->mutable_data<T>(output_grad_dims, ctx.GetPlace());
+    T* grid_grad_data =
+        grid_grad->mutable_data<T>({n, h, w, 2}, ctx.GetPlace());
+
+    ScopedTensorDescriptor input_desc;
+    ScopedTensorDescriptor input_grad_desc;
+    ScopedTensorDescriptor output_grad_desc;
+    cudnnTensorDescriptor_t cudnn_input_desc = input_desc.descriptor<T>(
+        DataLayout::kNCHW, framework::vectorize2int(input->dims()));
+    cudnnTensorDescriptor_t cudnn_input_grad_desc =
+        input_grad_desc.descriptor<T>(
+            DataLayout::kNCHW, framework::vectorize2int(input_grad->dims()));
+    cudnnTensorDescriptor_t cudnn_output_grad_desc =
+        output_grad_desc.descriptor<T>(
+            DataLayout::kNCHW, framework::vectorize2int(output_grad->dims()));
+
+    CUDNN_ENFORCE(platform::dynload::cudnnSpatialTfSamplerBackward(
+        handle, cudnn_st_dest, CudnnDataType<T>::kOne(), cudnn_input_desc,
+        input_data, CudnnDataType<T>::kZero(), cudnn_input_grad_desc,
+        input_grad_data, CudnnDataType<T>::kOne(), cudnn_output_grad_desc,
+        output_grad_data, grid_data, CudnnDataType<T>::kZero(),
+        grid_grad_data));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace plat = paddle::platform;
+REGISTER_OP_KERNEL(grid_sampler, CUDNN, plat::CUDAPlace,
+                   paddle::operators::CUDNNGridSampleOpKernel<float>,
+                   paddle::operators::CUDNNGridSampleOpKernel<double>);
+REGISTER_OP_KERNEL(grid_sampler_grad, CUDNN, plat::CUDAPlace,
+                   paddle::operators::CUDNNGridSampleGradOpKernel<float>,
+                   paddle::operators::CUDNNGridSampleGradOpKernel<double>);

paddle/fluid/operators/grid_sampler_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/grid_sampler_op.h"
+#include "paddle/fluid/framework/op_registry.h"
+#ifdef PADDLE_WITH_CUDA
+#include "paddle/fluid/platform/cudnn_helper.h"
+#endif
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+class GridSampleOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "Input(X) of GridSampleOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Grid"),
+                   "Input(Grid) of GridSampleOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Output"),
+                   "Output(Output) of GridSampleOp should not be null.");
+
+    auto x_dims = ctx->GetInputDim("X");
+    auto grid_dims = ctx->GetInputDim("Grid");
+    PADDLE_ENFORCE(x_dims.size() == 4,
+                   "Input(X) of GridSampleOp should be 4-D Tensor.");
+    PADDLE_ENFORCE(grid_dims.size() == 4,
+                   "Input(Grid) of GridSampleOp should be 4-D Tensor.");
+    PADDLE_ENFORCE(grid_dims[3] == 2, "Input(Grid) dims[3] should be 2.");
+    PADDLE_ENFORCE_EQ(grid_dims[0], x_dims[0],
+                      "Input(X) and Input(Grid) dims[0] should be equal.");
+    PADDLE_ENFORCE_EQ(
+        grid_dims[1], x_dims[2],
+        "Input(X) dims[2] and Input(Grid) dims[1] should be equal.");
+    PADDLE_ENFORCE_EQ(
+        grid_dims[2], x_dims[3],
+        "Input(X) dims[3] and Input(Grid) dims[2] should be equal.");
+
+    ctx->SetOutputDim("Output", x_dims);
+    ctx->ShareLoD("X", "Output");
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    framework::LibraryType library_{framework::LibraryType::kPlain};
+#ifdef PADDLE_WITH_CUDA
+    if (platform::CanCUDNNBeUsed(ctx)) {
+      library_ = framework::LibraryType::kCUDNN;
+    }
+#endif
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace(),
+        framework::DataLayout::kAnyLayout, library_);
+  }
+};
+
+class GridSampleOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X",
+             "(Tensor) The input data of GridSampleOp, "
+             "This is a 4-D tensor with shape of [N, C, H, W]");
+    AddInput(
+        "Grid",
+        "(Tensor) The input grid of GridSampleOp generated by AffineGridOp, "
+        "This is a 4-D tensor with shape of [N, H, W, 2] is the concatenation "
+        "of x and y coordinates with shape [N, H, W] in last dimention");
+    AddOutput("Output", "(Tensor) Output tensor with shape [N, C, H, W]");
+    AddAttr<bool>(
+        "use_cudnn",
+        "(bool, default true) Only used in cudnn kernel, need install cudnn")
+        .SetDefault(true);
+
+    AddComment(R"DOC(
+      This operation samples input X by using bilinear interpolation based on 
+      flow field grid, which is usually gennerated by affine_grid. The grid of
+      shape [N, H, W, 2] is the concatenation of (grid_x, grid_y) coordinates 
+      with shape [N, H, W] each, where grid_x is indexing the 4th dimension 
+      (in width dimension) of input data x and grid_y is indexng the 3rd 
+      dimention (in height dimension), finally results is the bilinear 
+      interpolation value of 4 nearest corner points.
+
+      Step 1:
+        Get (x, y) grid coordinates and scale to [0, H-1/W-1].
+
+        grid_x = 0.5 * (grid[:, :, :, 0] + 1) * (W - 1)
+        grid_y = 0.5 * (grid[:, :, :, 1] + 1) * (H - 1)
+
+      Step 2:
+        Indices input data X with grid (x, y) in each [H, W] area, and bilinear 
+        interpolate point value by 4 nearest points.
+
+          wn ------- y_n ------- en
+          |           |           |
+          |          d_n          |
+          |           |           |
+         x_w --d_w-- grid--d_e-- x_e
+          |           |           |
+          |          d_s          |
+          |           |           |
+          ws ------- y_s ------- wn
+
+        x_w = floor(x)              // west side x coord
+        x_e = x_w + 1               // east side x coord
+        y_n = floor(y)              // north side y coord
+        y_s = y_s + 1               // south side y coord
+
+        d_w = grid_x - x_w          // distance to west side
+        d_e = x_e - grid_x          // distance to east side
+        d_n = grid_y - y_n          // distance to north side
+        d_s = y_s - grid_y          // distance to south side
+
+        wn = X[:, :, y_n, x_w]      // north-west point value
+        en = X[:, :, y_n, x_e]      // north-east point value
+        ws = X[:, :, y_s, x_w]      // south-east point value
+        es = X[:, :, y_s, x_w]      // north-east point value
+
+        output = wn * d_e * d_s + en * d_w * d_s
+               + ws * d_e * d_n + es * d_w * d_n
+        )DOC");
+  }
+};
+
+class GridSampleOpGrad : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    auto input_dims = ctx->GetInputDim("X");
+    auto grid_dims = ctx->GetInputDim("Grid");
+    if (ctx->HasOutput(framework::GradVarName("X"))) {
+      ctx->SetOutputDim(framework::GradVarName("X"), input_dims);
+    }
+    if (ctx->HasOutput(framework::GradVarName("Grid"))) {
+      ctx->SetOutputDim(framework::GradVarName("Grid"), grid_dims);
+    }
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    framework::LibraryType library_{framework::LibraryType::kPlain};
+#ifdef PADDLE_WITH_CUDA
+    if (platform::CanCUDNNBeUsed(ctx)) {
+      library_ = framework::LibraryType::kCUDNN;
+    }
+#endif
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace(),
+        framework::DataLayout::kAnyLayout, library_);
+  }
+};
+
+class GridSampleGradMaker : public framework::SingleGradOpDescMaker {
+ public:
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  std::unique_ptr<framework::OpDesc> Apply() const override {
+    auto* op = new framework::OpDesc();
+    op->SetType("grid_sampler_grad");
+    op->SetInput("X", Input("X"));
+    op->SetInput("Grid", Input("Grid"));
+    op->SetInput(framework::GradVarName("Output"), OutputGrad("Output"));
+
+    op->SetAttrMap(Attrs());
+
+    op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+    op->SetOutput(framework::GradVarName("Grid"), InputGrad("Grid"));
+    return std::unique_ptr<framework::OpDesc>(op);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(grid_sampler, ops::GridSampleOp, ops::GridSampleOpMaker,
+                  ops::GridSampleGradMaker);
+REGISTER_OPERATOR(grid_sampler_grad, ops::GridSampleOpGrad);
+
+REGISTER_OP_CPU_KERNEL(
+    grid_sampler,
+    ops::GridSampleOpKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::GridSampleOpKernel<paddle::platform::CPUDeviceContext, double>);
+REGISTER_OP_CPU_KERNEL(
+    grid_sampler_grad,
+    ops::GridSampleGradOpKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::GridSampleGradOpKernel<paddle::platform::CPUDeviceContext, double>);

paddle/fluid/operators/grid_sampler_op.h

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+#include "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/gather.h"
+#include "paddle/fluid/operators/math/math_function.h"
+#include "paddle/fluid/platform/hostdevice.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+template <typename T, size_t D, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
+
+using Array3 = Eigen::DSizes<int64_t, 3>;
+using Array4 = Eigen::DSizes<int64_t, 4>;
+
+template <typename T>
+static inline bool isInBound(T x, T y, T x_max, T y_max) {
+  if (x < 0 || x > x_max || y < 0 || y > y_max) {
+    return false;
+  }
+  return true;
+}
+
+template <typename T>
+static void CalcGridLocations(const platform::CPUDeviceContext& ctx,
+                              const Tensor& grid, Tensor* x_w, Tensor* x_e,
+                              Tensor* y_n, Tensor* y_s, Tensor* d_w,
+                              Tensor* d_e, Tensor* d_n, Tensor* d_s) {
+  auto& place = *ctx.eigen_device();
+  const int n = grid.dims()[0];
+  const int h = grid.dims()[1];
+  const int w = grid.dims()[2];
+  const T x_max = static_cast<T>(w - 1);
+  const T y_max = static_cast<T>(h - 1);
+
+  // split grid with shape (n, h, w, 2) into (x, y) by the 3rd Dim
+  Tensor grid_x, grid_y;
+  T* grid_x_data = grid_x.mutable_data<T>({n, h, w}, ctx.GetPlace());
+  T* grid_y_data = grid_y.mutable_data<T>({n, h, w}, ctx.GetPlace());
+  const T* grid_data = grid.data<T>();
+  for (int i = 0; i < n * h * w; i++) {
+    grid_x_data[i] = grid_data[2 * i];
+    grid_y_data[i] = grid_data[(2 * i) + 1];
+  }
+
+  Tensor ones;
+  ones.mutable_data<T>({n, h, w}, ctx.GetPlace());
+  auto ones_t = EigenTensor<T, 3>::From(ones).setConstant(1.0);
+
+  // scale grid to [0, h-1/w-1]
+  auto grid_x_t = EigenTensor<T, 3>::From(grid_x);
+  auto grid_y_t = EigenTensor<T, 3>::From(grid_y);
+  grid_x_t.device(place) = 0.5 * ((grid_x_t + ones_t) * x_max);
+  grid_y_t.device(place) = 0.5 * ((grid_y_t + ones_t) * y_max);
+
+  // calculate coords of 4 corner points
+  x_w->mutable_data<T>({n, h, w}, ctx.GetPlace());
+  x_e->mutable_data<T>({n, h, w}, ctx.GetPlace());
+  y_n->mutable_data<T>({n, h, w}, ctx.GetPlace());
+  y_s->mutable_data<T>({n, h, w}, ctx.GetPlace());
+  auto x_w_t = EigenTensor<T, 3>::From(*x_w);
+  auto x_e_t = EigenTensor<T, 3>::From(*x_e);
+  auto y_n_t = EigenTensor<T, 3>::From(*y_n);
+  auto y_s_t = EigenTensor<T, 3>::From(*y_s);
+  x_w_t.device(place) = grid_x_t.floor();
+  x_e_t.device(place) = x_w_t + ones_t;
+  y_n_t.device(place) = grid_y_t.floor();
+  y_s_t.device(place) = y_n_t + ones_t;
+
+  // calculate distances to 4 sides
+  d_w->mutable_data<T>({n, h, w}, ctx.GetPlace());
+  d_e->mutable_data<T>({n, h, w}, ctx.GetPlace());
+  d_n->mutable_data<T>({n, h, w}, ctx.GetPlace());
+  d_s->mutable_data<T>({n, h, w}, ctx.GetPlace());
+  auto d_w_t = EigenTensor<T, 3>::From(*d_w);
+  auto d_e_t = EigenTensor<T, 3>::From(*d_e);
+  auto d_n_t = EigenTensor<T, 3>::From(*d_n);
+  auto d_s_t = EigenTensor<T, 3>::From(*d_s);
+  d_w_t.device(place) = grid_x_t - x_w_t;
+  d_e_t.device(place) = x_e_t - grid_x_t;
+  d_n_t.device(place) = grid_y_t - y_n_t;
+  d_s_t.device(place) = y_s_t - grid_y_t;
+}
+
+template <typename T>
+static void GetGridPointValue(const Tensor& input, Tensor* output,
+                              const Tensor& x, const Tensor& y) {
+  const int n = input.dims()[0];
+  const int c = input.dims()[1];
+  const int h = input.dims()[2];
+  const int w = input.dims()[3];
+  auto x_t = EigenTensor<T, 3>::From(x);
+  auto y_t = EigenTensor<T, 3>::From(y);
+  auto output_t = EigenTensor<T, 4>::From(*output).setConstant((T)0);
+  auto input_t = EigenTensor<T, 4>::From(input);
+
+  for (int i = 0; i < n; i++) {
+    for (int k = 0; k < h; k++) {
+      for (int l = 0; l < w; l++) {
+        if (isInBound(x_t(i, k, l), y_t(i, k, l), (T)(w - 1), (T)(h - 1))) {
+          for (int j = 0; j < c; j++) {
+            output_t(i, j, k, l) =
+                input_t(i, j, static_cast<int>(round(y_t(i, k, l))),
+                        static_cast<int>(round(x_t(i, k, l))));
+          }
+        }
+      }
+    }
+  }
+}
+
+template <typename T>
+static void GatherOutputGradToInputGrad(const Tensor& output_grad,
+                                        Tensor* input_grad, const Tensor& x,
+                                        const Tensor& y, const Tensor& d1,
+                                        const Tensor& d2) {
+  const int n = output_grad.dims()[0];
+  const int c = output_grad.dims()[1];
+  const int h = output_grad.dims()[2];
+  const int w = output_grad.dims()[3];
+  auto x_t = EigenTensor<T, 3>::From(x);
+  auto y_t = EigenTensor<T, 3>::From(y);
+  auto d1_t = EigenTensor<T, 3>::From(d1);
+  auto d2_t = EigenTensor<T, 3>::From(d2);
+  auto input_grad_t = EigenTensor<T, 4>::From(*input_grad);
+  auto output_grad_t = EigenTensor<T, 4>::From(output_grad);
+
+  for (int i = 0; i < n; i++) {
+    for (int k = 0; k < h; k++) {
+      for (int l = 0; l < w; l++) {
+        if (isInBound(x_t(i, k, l), y_t(i, k, l), (T)(w - 1), (T)(h - 1))) {
+          for (int j = 0; j < c; j++) {
+            input_grad_t(i, j, static_cast<int>(round(y_t(i, k, l))),
+                         static_cast<int>(round(x_t(i, k, l)))) +=
+                output_grad_t(i, j, k, l) * d1_t(i, k, l) * d2_t(i, k, l);
+          }
+        }
+      }
+    }
+  }
+}
+
+template <typename DeviceContext, typename T>
+class GridSampleOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
+    auto* input = ctx.Input<Tensor>("X");
+    auto* grid = ctx.Input<Tensor>("Grid");
+
+    const int n = input->dims()[0];
+    const int c = input->dims()[1];
+    const int h = input->dims()[2];
+    const int w = input->dims()[3];
+
+    // calc locations and distances of 4 corner points
+    Tensor x_w, x_e, y_n, y_s;
+    Tensor d_w, d_e, d_n, d_s;
+    CalcGridLocations<T>(
+        ctx.template device_context<platform::CPUDeviceContext>(), *grid, &x_w,
+        &x_e, &y_n, &y_s, &d_w, &d_e, &d_n, &d_s);
+
+    auto* output = ctx.Output<Tensor>("Output");
+    output->mutable_data<T>({n, c, h, w}, ctx.GetPlace());
+    math::SetConstant<DeviceContext, T>()(
+        ctx.template device_context<DeviceContext>(), output,
+        static_cast<T>(0));
+
+    // calc 4 corner points value
+    Tensor v_wn, v_en, v_ws, v_es;
+    v_wn.mutable_data<T>({n, c, h, w}, ctx.GetPlace());
+    v_en.mutable_data<T>({n, c, h, w}, ctx.GetPlace());
+    v_ws.mutable_data<T>({n, c, h, w}, ctx.GetPlace());
+    v_es.mutable_data<T>({n, c, h, w}, ctx.GetPlace());
+    GetGridPointValue<T>(*input, &v_wn, x_w, y_n);
+    GetGridPointValue<T>(*input, &v_en, x_e, y_n);
+    GetGridPointValue<T>(*input, &v_ws, x_w, y_s);
+    GetGridPointValue<T>(*input, &v_es, x_e, y_s);
+
+    auto d_w_t = EigenTensor<T, 3>::From(d_w);
+    auto d_e_t = EigenTensor<T, 3>::From(d_e);
+    auto d_n_t = EigenTensor<T, 3>::From(d_n);
+    auto d_s_t = EigenTensor<T, 3>::From(d_s);
+    auto d_w_scaled_t =
+        d_w_t.reshape(Array4(n, 1, h, w)).broadcast(Array4(1, c, 1, 1));
+    auto d_e_scaled_t =
+        d_e_t.reshape(Array4(n, 1, h, w)).broadcast(Array4(1, c, 1, 1));
+    auto d_n_scaled_t =
+        d_n_t.reshape(Array4(n, 1, h, w)).broadcast(Array4(1, c, 1, 1));
+    auto d_s_scaled_t =
+        d_s_t.reshape(Array4(n, 1, h, w)).broadcast(Array4(1, c, 1, 1));
+    auto v_wn_t = EigenTensor<T, 4>::From(v_wn);
+    auto v_en_t = EigenTensor<T, 4>::From(v_en);
+    auto v_ws_t = EigenTensor<T, 4>::From(v_ws);
+    auto v_es_t = EigenTensor<T, 4>::From(v_es);
+    auto output_t = EigenTensor<T, 4>::From(*output);
+    // bilinear interpolaetion by 4 corner points
+    output_t.device(place) = v_wn_t * d_e_scaled_t * d_s_scaled_t +
+                             v_en_t * d_w_scaled_t * d_s_scaled_t +
+                             v_ws_t * d_e_scaled_t * d_n_scaled_t +
+                             v_es_t * d_w_scaled_t * d_n_scaled_t;
+  }
+};
+
+template <typename DeviceContext, typename T>
+class GridSampleGradOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* input = ctx.Input<Tensor>("X");
+    auto* grid = ctx.Input<Tensor>("Grid");
+    auto* output_grad = ctx.Input<Tensor>(framework::GradVarName("Output"));
+
+    const int n = input->dims()[0];
+    const int c = input->dims()[1];
+    const int h = input->dims()[2];
+    const int w = input->dims()[3];
+
+    auto* input_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
+    input_grad->mutable_data<T>({n, c, h, w}, ctx.GetPlace());
+    math::SetConstant<DeviceContext, T>()(
+        ctx.template device_context<DeviceContext>(), input_grad,
+        static_cast<T>(0));
+    auto* grid_grad = ctx.Output<Tensor>(framework::GradVarName("Grid"));
+    grid_grad->mutable_data<T>({n, h, w, 2}, ctx.GetPlace());
+    math::SetConstant<DeviceContext, T>()(
+        ctx.template device_context<DeviceContext>(), grid_grad,
+        static_cast<T>(0));
+
+    Tensor x_w, x_e, y_n, y_s;
+    Tensor d_w, d_e, d_n, d_s;
+    CalcGridLocations<T>(
+        ctx.template device_context<platform::CPUDeviceContext>(), *grid, &x_w,
+        &x_e, &y_n, &y_s, &d_w, &d_e, &d_n, &d_s);
+
+    // gather output grad value to input grad by corner point coords and weight
+    GatherOutputGradToInputGrad<T>(*output_grad, input_grad, x_w, y_n, d_e,
+                                   d_s);
+    GatherOutputGradToInputGrad<T>(*output_grad, input_grad, x_w, y_s, d_e,
+                                   d_n);
+    GatherOutputGradToInputGrad<T>(*output_grad, input_grad, x_e, y_n, d_w,
+                                   d_s);
+    GatherOutputGradToInputGrad<T>(*output_grad, input_grad, x_e, y_s, d_w,
+                                   d_n);
+
+    // calc 4 corner points value
+    Tensor v_wn, v_en, v_ws, v_es;
+    v_wn.mutable_data<T>({n, c, h, w}, ctx.GetPlace());
+    v_en.mutable_data<T>({n, c, h, w}, ctx.GetPlace());
+    v_ws.mutable_data<T>({n, c, h, w}, ctx.GetPlace());
+    v_es.mutable_data<T>({n, c, h, w}, ctx.GetPlace());
+    GetGridPointValue<T>(*input, &v_wn, x_w, y_n);
+    GetGridPointValue<T>(*input, &v_en, x_e, y_n);
+    GetGridPointValue<T>(*input, &v_ws, x_w, y_s);
+    GetGridPointValue<T>(*input, &v_es, x_e, y_s);
+    auto v_wn_t = EigenTensor<T, 4>::From(v_wn);
+    auto v_en_t = EigenTensor<T, 4>::From(v_en);
+    auto v_ws_t = EigenTensor<T, 4>::From(v_ws);
+    auto v_es_t = EigenTensor<T, 4>::From(v_es);
+
+    auto d_w_t = EigenTensor<T, 3>::From(d_w);
+    auto d_e_t = EigenTensor<T, 3>::From(d_e);
+    auto d_n_t = EigenTensor<T, 3>::From(d_n);
+    auto d_s_t = EigenTensor<T, 3>::From(d_s);
+
+    auto output_grad_t = EigenTensor<T, 4>::From(*output_grad);
+
+    Tensor grid_grad_x, grid_grad_y;
+    grid_grad_x.mutable_data<T>({n, h, w}, ctx.GetPlace());
+    grid_grad_y.mutable_data<T>({n, h, w}, ctx.GetPlace());
+    auto grid_grad_x_t = EigenTensor<T, 3>::From(grid_grad_x).setConstant(0.0);
+    auto grid_grad_y_t = EigenTensor<T, 3>::From(grid_grad_y).setConstant(0.0);
+    for (int i = 0; i < n; i++) {
+      for (int j = 0; j < c; j++) {
+        for (int k = 0; k < h; k++) {
+          for (int l = 0; l < w; l++) {
+            grid_grad_x_t(i, k, l) +=
+                ((v_en_t(i, j, k, l) - v_wn_t(i, j, k, l)) * d_s_t(i, k, l) +
+                 (v_es_t(i, j, k, l) - v_ws_t(i, j, k, l)) * d_n_t(i, k, l)) *
+                output_grad_t(i, j, k, l);
+            grid_grad_y_t(i, k, l) +=
+                ((v_ws_t(i, j, k, l) - v_wn_t(i, j, k, l)) * d_e_t(i, k, l) +
+                 (v_es_t(i, j, k, l) - v_en_t(i, j, k, l)) * d_w_t(i, k, l)) *
+                output_grad_t(i, j, k, l);
+          }
+        }
+      }
+    }
+    const T x_max = static_cast<T>(w - 1);
+    const T y_max = static_cast<T>(h - 1);
+    grid_grad_x_t = grid_grad_x_t * (x_max / (T)2);
+    grid_grad_y_t = grid_grad_y_t * (y_max / (T)2);
+
+    // gather grid_grad [x, y] in 3rd Dim
+    T* grid_grad_data = grid_grad->data<T>();
+    T* grid_grad_x_data = grid_grad_x.data<T>();
+    T* grid_grad_y_data = grid_grad_y.data<T>();
+    for (int i = 0; i < n * h * w; i++) {
+      grid_grad_data[2 * i] = grid_grad_x_data[i];
+      grid_grad_data[2 * i + 1] = grid_grad_y_data[i];
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/listen_and_serv_op.cc

@@ -218,23 +218,26 @@ void ListenAndServOp::RunAsyncLoop(framework::Executor *executor,
                                    framework::ProgramDesc *program,
                                    framework::Scope *recv_scope) const {
   VLOG(2) << "RunAsyncLoop";
-  // grad name to block id
-  std::unordered_map<std::string, int32_t> grad_to_block_id;
-  std::unordered_map<int32_t, std::string> id_to_grad;
-
   auto grad_to_block_id_str =
       Attr<std::vector<std::string>>("grad_to_block_id");
-  for (const auto &grad_and_id : grad_to_block_id_str) {
+  DoubleFindMap<std::string, int32_t> grad_to_block_id;
+
+  auto append_block_maps = [](DoubleFindMap<std::string, int32_t> *out_map,
+                              const std::string &grad_and_id) {
     std::vector<std::string> pieces;
     split(grad_and_id, ':', &pieces);
-    VLOG(3) << "after split, grad = " << pieces[0] << ", id=" << pieces[1];
+    VLOG(3) << "after split, key = " << pieces[0] << ", id=" << pieces[1];
     PADDLE_ENFORCE_EQ(pieces.size(), 2);
-    PADDLE_ENFORCE_EQ(grad_to_block_id.count(pieces[0]), 0);
+    PADDLE_ENFORCE_EQ(out_map->count(pieces[0]), 0);
 
     int block_id = std::stoi(pieces[1]);
-    grad_to_block_id[pieces[0]] = block_id;
-    id_to_grad[block_id] = pieces[0];
+    (*out_map)[pieces[0]] = block_id;
+  };
+
+  for (const auto &grad_and_id : grad_to_block_id_str) {
+    append_block_maps(&grad_to_block_id, grad_and_id);
   }
+
   size_t num_blocks = program->Size();
   PADDLE_ENFORCE_GE(num_blocks, 2,
                     "server program should have at least 2 blocks");
@@ -244,15 +247,22 @@ void ListenAndServOp::RunAsyncLoop(framework::Executor *executor,
     block_list.push_back(blkid);
   }
   auto optimize_prepared = executor->Prepare(*program, block_list);
-  // execute global block if needed
-  if (block_list[0] == 1 && id_to_grad.count(1) == 0) {
+  // execute global block if needed, block id 1 in the program is global
+  // block if it's not bind to a grad var for it's update.
+  if (block_list[0] == 1 &&
+      grad_to_block_id.find_value(static_cast<int32_t>(1)) ==
+          grad_to_block_id.end()) {
     executor->RunPreparedContext(optimize_prepared[0].get(), recv_scope);
   }
   std::unordered_map<std::string,
                      std::shared_ptr<framework::ExecutorPrepareContext>>
-      grad_to_prepared_ctx;
+      grad_to_prepared_ctx, param_to_prepared_ctx;
   for (size_t i = 0; i < block_list.size(); ++i) {
-    grad_to_prepared_ctx[id_to_grad[block_list[i]]] = optimize_prepared[i];
+    auto blkid = block_list[i];
+    auto it = grad_to_block_id.find_value(blkid);
+    if (it != grad_to_block_id.end()) {
+      grad_to_prepared_ctx[it->first] = optimize_prepared[i];
+    }
   }
 
   request_send_handler_->SetGradToPreparedCtx(&grad_to_prepared_ctx);
@@ -315,6 +325,7 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
   framework::Scope &recv_scope = scope.NewScope();
 
   bool sync_mode = Attr<bool>("sync_mode");
+  bool dc_sgd = Attr<bool>("dc_asgd");
   auto fan_in = Attr<int>("Fanin");
   auto inputs = Inputs("X");
 
@@ -328,8 +339,10 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
 
   rpc_service_.reset(new RPCSERVER_T(endpoint, fan_in));
 
-  request_send_handler_.reset(new distributed::RequestSendHandler(sync_mode));
-  request_get_handler_.reset(new distributed::RequestGetHandler(sync_mode));
+  request_send_handler_.reset(
+      new distributed::RequestSendHandler(sync_mode, dc_sgd));
+  request_get_handler_.reset(
+      new distributed::RequestGetHandler(sync_mode, dc_sgd));
   request_prefetch_handler_.reset(
       new distributed::RequestPrefetchHandler(sync_mode));
   request_checkpoint_handler_.reset(new distributed::RequestCheckpointHandler(
@@ -443,6 +456,8 @@ class ListenAndServOpMaker : public framework::OpProtoAndCheckerMaker {
         "a map from grad name to it's optimize block id")
         .SetDefault({});
     AddAttr<bool>("sync_mode", "if works at sync_mode or not").SetDefault(true);
+    AddAttr<bool>("dc_asgd", "set to true will enable DC-ASGD training.")
+        .SetDefault(false);
     AddAttr<std::vector<framework::BlockDesc *>>(
         kOptimizeBlocks, "Optimize blocks to run on server side.")
         .SetDefault({});

paddle/fluid/operators/listen_and_serv_op.h

@@ -18,6 +18,7 @@ limitations under the License. */
 #include <atomic>
 #include <set>
 #include <string>
+#include <utility>
 #include <vector>
 
 #include "paddle/fluid/framework/executor.h"
@@ -37,6 +38,17 @@ constexpr char kCheckpointBlockId[] = "checkpint_block_id";
 
 void RunServer(std::shared_ptr<distributed::RPCServer> service);
 
+template <class TKey, class TValue>
+class DoubleFindMap : public std::unordered_map<TKey, TValue> {
+ public:
+  typename std::unordered_map<TKey, TValue>::iterator find_value(TValue v) {
+    return std::find_if(this->begin(), this->end(),
+                        [&v](const std::pair<const std::string, int> p) {
+                          return p.second == v;
+                        });
+  }
+};
+
 class ListenAndServOp : public framework::OperatorBase {
  public:
   ListenAndServOp(const std::string& type,

paddle/fluid/operators/math/CMakeLists.txt

@@ -76,6 +76,6 @@ endif()
 cc_test(concat_test SRCS concat_test.cc DEPS concat_and_split)
 cc_test(cpu_vec_test SRCS cpu_vec_test.cc DEPS blas cpu_info)
 cc_library(jit_kernel 
-    SRCS jit_kernel.cc jit_kernel_blas.cc jit_kernel_exp.cc jit_kernel_rnn.cc jit_kernel_crf_decode.cc
-    DEPS cpu_info cblas)
+    SRCS jit_kernel.cc jit_gen.cc jit_code.cc jit_kernel_blas.cc jit_kernel_exp.cc jit_kernel_rnn.cc jit_kernel_crf_decode.cc
+    DEPS cpu_info cblas gflags enforce)
 cc_test(jit_kernel_test SRCS jit_kernel_test.cc DEPS jit_kernel)

paddle/fluid/operators/math/jit_code.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/math/jit_code.h"
+#include "paddle/fluid/operators/math/jit_kernel.h"
+#include "paddle/fluid/platform/cpu_info.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+namespace jitkernel {
+namespace gen {
+
+using namespace platform::jit;  // NOLINT
+
+bool VMulJitCode::init(int d) {
+  // It's not necessary to use avx512 since it would slow down the frequency
+  // and this kernel is not compute bound.
+  return MayIUse(avx);
+}
+
+void VMulJitCode::generate() {
+  // do not need push stack, and do not need save avx512reg if do not use avx512
+  int offset = 0;
+  for (int i = 0; i < num_ / AVX_FLOAT_BLOCK; ++i) {
+    vmovups(ymm_src1, ptr[param1 + offset]);
+    vmovups(ymm_src2, ptr[param2 + offset]);
+    vmulps(ymm_dst, ymm_src1, ymm_src2);
+    vmovups(ptr[param3 + offset], ymm_dst);
+    offset += sizeof(float) * AVX_FLOAT_BLOCK;
+  }
+  int rest = num_ % AVX_FLOAT_BLOCK;
+  if (rest >= 4) {
+    vmovups(xmm_src1, ptr[param1 + offset]);
+    vmovups(xmm_src2, ptr[param2 + offset]);
+    vmulps(xmm_dst, xmm_src1, xmm_src2);
+    vmovups(ptr[param3 + offset], xmm_dst);
+    offset += sizeof(float) * 4;
+    rest -= 4;
+  }
+  if (rest >= 2) {
+    vmovq(xmm_src1, ptr[param1 + offset]);
+    vmovq(xmm_src2, ptr[param2 + offset]);
+    vmulps(xmm_dst, xmm_src1, xmm_src2);
+    vmovq(ptr[param3 + offset], xmm_dst);
+    offset += sizeof(float) * 2;
+    rest -= 2;
+  }
+  if (rest > 0) {
+    vmovss(xmm_src1, ptr[param1 + offset]);
+    vmovss(xmm_src2, ptr[param2 + offset]);
+    vmulss(xmm_dst, xmm_src1, xmm_src2);
+    vmovss(ptr[param3 + offset], xmm_dst);
+  }
+  ret();
+}
+
+}  // namespace gen
+}  // namespace jitkernel
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/math/jit_code.h

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+
+#include "paddle/fluid/operators/math/jit_gen.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+namespace jitkernel {
+namespace gen {
+
+using reg64_t = const Xbyak::Reg64;
+using reg32_t = const Xbyak::Reg32;
+using xmm_t = const Xbyak::Xmm;
+using ymm_t = const Xbyak::Ymm;
+using zmm_t = const Xbyak::Zmm;
+using Label = Xbyak::Label;
+
+class VMulJitCode : public JitCode {
+ public:
+  DECLARE_JIT_CODE(VMulJitCode);
+  explicit VMulJitCode(int d, size_t code_size = 256 * 1024,
+                       void* code_ptr = nullptr)
+      : JitCode(code_size, code_ptr), num_(d) {}
+  static bool init(int d);
+  void generate() override;
+
+ private:
+  int num_;
+  reg64_t param1{abi_param1};
+  reg64_t param2{abi_param2};
+  reg64_t param3{abi_param3};
+
+  xmm_t xmm_src1 = xmm_t(0);
+  xmm_t xmm_src2 = xmm_t(1);
+  xmm_t xmm_dst = xmm_t(2);
+
+  ymm_t ymm_src1 = ymm_t(0);
+  ymm_t ymm_src2 = ymm_t(1);
+  ymm_t ymm_dst = ymm_t(2);
+};
+
+}  // namespace gen
+}  // namespace jitkernel
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/math/jit_gen.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/math/jit_gen.h"
+#include <fstream>
+#include <iostream>
+#include <sstream>
+#include "paddle/fluid/platform/cpu_info.h"
+
+DEFINE_bool(dump_jitcode, false, "Whether to dump the jitcode to file");
+
+namespace paddle {
+namespace operators {
+namespace math {
+namespace jitkernel {
+namespace gen {
+
+constexpr Xbyak::Operand::Code g_abi_regs[] = {
+    Xbyak::Operand::RBX, Xbyak::Operand::RBP, Xbyak::Operand::R12,
+    Xbyak::Operand::R13, Xbyak::Operand::R14, Xbyak::Operand::R15};
+
+constexpr int num_g_abi_regs = sizeof(g_abi_regs) / sizeof(g_abi_regs[0]);
+
+void JitCode::preCode() {
+  for (int i = 0; i < num_g_abi_regs; ++i) {
+    push(Xbyak::Reg64(g_abi_regs[i]));
+  }
+  if (platform::jit::MayIUse(platform::jit::avx512f)) {
+    mov(reg_EVEX_max_8b_offt, 2 * EVEX_max_8b_offt);
+  }
+}
+
+void JitCode::postCode() {
+  for (int i = 0; i < num_g_abi_regs; ++i) {
+    pop(Xbyak::Reg64(g_abi_regs[num_g_abi_regs - 1 - i]));
+  }
+  ret();
+}
+
+void JitCode::dumpCode(const Xbyak::uint8 *code) const {
+  if (code) {
+    static int counter = 0;
+    std::ostringstream filename;
+    filename << "paddle_jitcode_" << name() << "." << counter << ".bin";
+    counter++;
+    std::ofstream fout(filename.str(), std::ios::out);
+    if (fout.is_open()) {
+      fout.write(reinterpret_cast<const char *>(code), getSize());
+      fout.close();
+    }
+  }
+}
+
+Xbyak::Address JitCode::EVEX_compress_addr(Xbyak::Reg64 base, int offt,
+                                           bool bcast) {
+  int scale = 0;
+  if (EVEX_max_8b_offt <= offt && offt < 3 * EVEX_max_8b_offt) {
+    offt = offt - 2 * EVEX_max_8b_offt;
+    scale = 1;
+  } else if (3 * EVEX_max_8b_offt <= offt && offt < 5 * EVEX_max_8b_offt) {
+    offt = offt - 4 * EVEX_max_8b_offt;
+    scale = 2;
+  }
+  auto re = Xbyak::RegExp() + base + offt;
+  if (scale) {
+    re = re + reg_EVEX_max_8b_offt * scale;
+  }
+  if (bcast) {
+    return zword_b[re];
+  } else {
+    return zword[re];
+  }
+}
+
+}  // namespace gen
+}  // namespace jitkernel
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/math/jit_gen.h

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+
+#include <gflags/gflags.h>
+#include <type_traits>
+#include "paddle/fluid/platform/macros.h"
+
+#define XBYAK_USE_MMAP_ALLOCATOR
+#include "xbyak/xbyak.h"
+#include "xbyak/xbyak_util.h"
+
+DECLARE_bool(dump_jitcode);
+
+namespace paddle {
+namespace operators {
+namespace math {
+namespace jitkernel {
+namespace gen {
+
+#define DECLARE_JIT_CODE(codename) \
+  const char *name() const override { return #codename; }
+
+// Application Binary Interface
+constexpr Xbyak::Operand::Code abi_param1(Xbyak::Operand::RDI),
+    abi_param2(Xbyak::Operand::RSI), abi_param3(Xbyak::Operand::RDX),
+    abi_param4(Xbyak::Operand::RCX), abi_not_param1(Xbyak::Operand::RCX);
+
+class JitCode : public Xbyak::CodeGenerator {
+ public:
+  explicit JitCode(size_t code_size = 256 * 1024, void *code_ptr = nullptr)
+      : Xbyak::CodeGenerator(code_size, code_ptr) {}
+
+  virtual ~JitCode() {}
+  virtual const char *name() const = 0;
+  virtual void generate() = 0;
+
+  template <typename FUNC>
+  const FUNC getCode() {
+    this->generate();
+    const Xbyak::uint8 *code = CodeGenerator::getCode();
+    if (FLAGS_dump_jitcode) {
+      this->dumpCode(code);
+    }
+    return reinterpret_cast<const FUNC>(code);
+  }
+  DISABLE_COPY_AND_ASSIGN(JitCode);
+
+ protected:
+  Xbyak::Reg64 param1{abi_param1};
+  const int EVEX_max_8b_offt = 0x200;
+  const Xbyak::Reg64 reg_EVEX_max_8b_offt = rbp;
+
+  void preCode();
+  void postCode();
+  void dumpCode(const Xbyak::uint8 *code) const;
+  void L(const char *label) { Xbyak::CodeGenerator::L(label); }
+  void L(const Xbyak::Label &label) { Xbyak::CodeGenerator::L(label); }
+  // Enhanced vector extension
+  Xbyak::Address EVEX_compress_addr(Xbyak::Reg64 base, int offt,
+                                    bool bcast = false);
+};
+
+}  // namespace gen
+}  // namespace jitkernel
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/math/jit_kernel.h

@@ -39,6 +39,7 @@ class Kernel {
  public:
   Kernel() = default;
   virtual ~Kernel() = default;
+  // TODO(TJ): below members should be deprecated.
   int num_{0};
   int end_{0};
   int rest_{0};
@@ -64,7 +65,7 @@ class KernelPool {
 template <typename T>
 class VMulKernel : public Kernel {
  public:
-  virtual void Compute(const T *x, const T *y, T *z) const = 0;
+  void (*Compute)(const T *, const T *, T *, int);
 };
 
 template <typename T>

paddle/fluid/operators/math/jit_kernel_blas.cc

@@ -14,7 +14,10 @@ limitations under the License. */
 
 #include "paddle/fluid/operators/math/jit_kernel.h"
 #include <string>
+#include "paddle/fluid/operators/math/jit_code.h"
 #include "paddle/fluid/operators/math/jit_kernel_macro.h"
+#include "paddle/fluid/platform/enforce.h"
+
 #ifdef PADDLE_WITH_MKLML
 #include "paddle/fluid/platform/dynload/mklml.h"
 #endif
@@ -27,65 +30,77 @@ namespace paddle {
 namespace operators {
 namespace math {
 namespace jitkernel {
-
 namespace jit = platform::jit;
 
+template <typename T>
+void VMulRefer(const T* x, const T* y, T* z, int n) {
+  for (int i = 0; i < n; ++i) {
+    z[i] = x[i] * y[i];
+  }
+}
+
+#ifdef PADDLE_WITH_MKLML
+template <typename T>
+void VMulMKL(const T* x, const T* y, T* z, int n);
+
+template <>
+void VMulMKL<float>(const float* x, const float* y, float* z, int n) {
+  platform::dynload::vsMul(n, x, y, z);
+}
+template <>
+void VMulMKL<double>(const double* x, const double* y, double* z, int n) {
+  platform::dynload::vdMul(n, x, y, z);
+}
+#endif
+
 /* VMUL JitKernel */
-template <typename T, platform::jit::cpu_isa_t isa, jit_block>
+template <typename T>
 class VMulKernelImpl : public VMulKernel<T> {
  public:
-  explicit VMulKernelImpl(int d) : VMulKernel<T>() { this->num_ = d; }
-  void Compute(const T* x, const T* y, T* z) const override {
-    for (int i = 0; i < this->num_; ++i) {
-      z[i] = x[i] * y[i];
-    }
+  static inline std::string name(int d) {
+    PADDLE_THROW("DType should be either float or double");
   }
-};
+  static inline bool useJIT(int d) { return false; }
+  static inline bool useMKL(int d) { return false; }
 
+  explicit VMulKernelImpl(int d) : VMulKernel<T>() {
+    if (useJIT(d)) {
+      // roughly estimate the size of code
+      size_t sz = 96 + d / AVX_FLOAT_BLOCK * 4 * 8;
+      jitcode_.reset(new gen::VMulJitCode(d, sz > 4096 ? sz : 4096));
+      this->Compute =
+          jitcode_->getCode<void (*)(const T*, const T*, T*, int)>();
+      return;
+    }
 #ifdef PADDLE_WITH_MKLML
-#define MKL_FLOAT(isa, block)                           \
-  template <>                                           \
-  void VMulKernelImpl<float, isa, block>::Compute(      \
-      const float* x, const float* y, float* z) const { \
-    platform::dynload::vsMul(this->num_, x, y, z);      \
+    if (useMKL(d)) {
+      this->Compute = VMulMKL<T>;
+      return;
     }
-
-#define MKL_DOUBLE(isa, block)                             \
-  template <>                                              \
-  void VMulKernelImpl<double, isa, block>::Compute(        \
-      const double* x, const double* y, double* z) const { \
-    platform::dynload::vdMul(this->num_, x, y, z);         \
+#endif
+    this->Compute = VMulRefer<T>;
   }
 
-FOR_EACH_ISA(MKL_FLOAT, kGT16);
-FOR_EACH_ISA_BLOCK(MKL_DOUBLE);
-#endif
+ private:
+  std::unique_ptr<gen::VMulJitCode> jitcode_{nullptr};
+};
 
-#define INTRI8_FLOAT(isa)                               \
-  template <>                                           \
-  void VMulKernelImpl<float, isa, kEQ8>::Compute(       \
-      const float* x, const float* y, float* z) const { \
-    __m256 tmpx, tmpy;                                  \
-    tmpx = _mm256_loadu_ps(x);                          \
-    tmpy = _mm256_loadu_ps(y);                          \
-    tmpx = _mm256_mul_ps(tmpx, tmpy);                   \
-    _mm256_storeu_ps(z, tmpx);                          \
-  }
+template <>
+bool VMulKernelImpl<float>::useJIT(int d) {
+  return gen::VMulJitCode::init(d);
+}
 
-// avx > for > mkl
-#ifdef __AVX__
-INTRI8_FLOAT(jit::avx);
-#endif
-#ifdef __AVX2__
-INTRI8_FLOAT(jit::avx2);
-#endif
-#ifdef __AVX512F__
-INTRI8_FLOAT(jit::avx512f);
-#endif
-// TODO(TJ): eq16 test and complete avx512
-#undef INTRI8_FLOAT
-#undef MKL_FLOAT
-#undef MKL_DOUBLE
+template <>
+bool VMulKernelImpl<float>::useMKL(int d) {
+  return jit::MayIUse(jit::avx512f) && d > 512;
+}
+
+template <>
+bool VMulKernelImpl<double>::useMKL(int d) {
+  return true;
+}
+
+REGISTER_JITKERNEL(vmul, VMulKernel);
 
 /* VADD JitKernel */
 template <typename T, platform::jit::cpu_isa_t isa, jit_block>
@@ -465,13 +480,12 @@ INTRI_COMMON_FLOAT(jit::avx512f, kGT16);
 #undef INTRI16_FLOAT
 #undef INTRI_COMMON_FLOAT
 
-REGISTER_JITKERNEL(vmul, VMulKernel);
-REGISTER_JITKERNEL(vadd, VAddKernel);
-REGISTER_JITKERNEL(vscal, VScalKernel);
-REGISTER_JITKERNEL(vaddb, VAddBiasKernel);
-REGISTER_JITKERNEL(vrelu, VReluKernel);
-REGISTER_JITKERNEL(vaddrelu, VAddReluKernel);
-REGISTER_JITKERNEL(videntity, VIdentityKernel);
+REGISTER_JITKERNEL_DEPRECATED(vadd, VAddKernel);
+REGISTER_JITKERNEL_DEPRECATED(vscal, VScalKernel);
+REGISTER_JITKERNEL_DEPRECATED(vaddb, VAddBiasKernel);
+REGISTER_JITKERNEL_DEPRECATED(vrelu, VReluKernel);
+REGISTER_JITKERNEL_DEPRECATED(vaddrelu, VAddReluKernel);
+REGISTER_JITKERNEL_DEPRECATED(videntity, VIdentityKernel);
 
 }  // namespace jitkernel
 }  // namespace math

paddle/fluid/operators/math/jit_kernel_crf_decode.cc

@@ -288,7 +288,7 @@ INTRIAVX512_FLOAT(kGT16);
 #undef INIT_ALPHA
 #undef UPDATE_ALPHA
 
-REGISTER_JITKERNEL(crf_decode, CRFDecodeKernel);
+REGISTER_JITKERNEL_DEPRECATED(crf_decode, CRFDecodeKernel);
 
 }  // namespace jitkernel
 }  // namespace math

paddle/fluid/operators/math/jit_kernel_exp.cc

@@ -250,7 +250,7 @@ INTRI16_FLOAT(jit::avx512f, detail::ExpAVX2);
 #undef MKL_FLOAT
 #undef MKL_DOUBLE
 
-REGISTER_JITKERNEL(vexp, VExpKernel);
+REGISTER_JITKERNEL_DEPRECATED(vexp, VExpKernel);
 
 /* VSigmoid JitKernel */
 template <typename T, jit::cpu_isa_t isa, jit_block>
@@ -396,7 +396,7 @@ INTRI16_FLOAT(jit::avx512f, detail::ExpAVX2);
 #undef INTRI_GT16_FLOAT
 #undef INTRI_VSIGMOID
 
-REGISTER_JITKERNEL(vsigmoid, VSigmoidKernel);
+REGISTER_JITKERNEL_DEPRECATED(vsigmoid, VSigmoidKernel);
 
 /* VTanh JitKernel */
 template <typename T, jit::cpu_isa_t isa, jit_block>
@@ -531,7 +531,7 @@ INTRI16_FLOAT(jit::avx512f, detail::ExpAVX2);
 #undef INTRI_GT16_FLOAT
 #undef INTRI_VTANH
 
-REGISTER_JITKERNEL(vtanh, VTanhKernel);
+REGISTER_JITKERNEL_DEPRECATED(vtanh, VTanhKernel);
 
 #undef JITKERNEL_NEW_ACT_IMPL
 

paddle/fluid/operators/math/jit_kernel_macro.h

@@ -21,8 +21,71 @@ namespace operators {
 namespace math {
 namespace jitkernel {
 
-namespace jit = platform::jit;
+#define JITKERNEL_DEFINE_NAME(ker_key, ker_class)    \
+  template <>                                        \
+  std::string ker_class##Impl<float>::name(int d) {  \
+    std::string key(#ker_key "f");                   \
+    if (useJIT(d)) {                                 \
+      /* only jit code need record d*/               \
+      return key + "jit" + std::to_string(d);        \
+    } else if (useMKL(d)) {                          \
+      return key + "mkl";                            \
+    } else {                                         \
+      return key + "any";                            \
+    }                                                \
+  }                                                  \
+  template <>                                        \
+  std::string ker_class##Impl<double>::name(int d) { \
+    std::string key(#ker_key "d");                   \
+    /* jit code do not support double yet*/          \
+    if (useMKL(d)) {                                 \
+      return key + "mkl";                            \
+    } else {                                         \
+      return key + "any";                            \
+    }                                                \
+  }
+
+#define JITKERNEL_DECLARE(ker_class, ker_dtype) \
+  template <>                                   \
+  std::shared_ptr<const ker_class<ker_dtype>>   \
+  KernelPool::Get<ker_class<ker_dtype>, int>(int d)
+
+#define JITKERNEL_FIND_KEY(ker_class, ker_dtype) \
+  std::string key = ker_class##Impl<ker_dtype>::name(d)
 
+#define JITKERNEL_IMPL(ker_class, ker_dtype)           \
+  p = std::dynamic_pointer_cast<ker_class<ker_dtype>>( \
+      std::make_shared<ker_class##Impl<ker_dtype>>(d))
+
+#define REGISTER_JITKERNEL_WITH_DTYPE(ker_class, ker_dtype, marco_declare, \
+                                      macro_find_key, macro_impl)          \
+  marco_declare(ker_class, ker_dtype) {                                    \
+    macro_find_key(ker_class, ker_dtype);                                  \
+    if (kers_.find(key) == kers_.end()) {                                  \
+      std::shared_ptr<ker_class<ker_dtype>> p;                             \
+      macro_impl(ker_class, ker_dtype);                                    \
+      kers_.insert({key, std::dynamic_pointer_cast<Kernel>(p)});           \
+      return p;                                                            \
+    }                                                                      \
+    return std::dynamic_pointer_cast<const ker_class<ker_dtype>>(          \
+        kers_.at(key));                                                    \
+  }
+
+#define REGISTER_JITKERNEL_ARGS(ker_key, ker_class, marco_define_name,     \
+                                marco_declare, macro_find_key, macro_impl) \
+  marco_define_name(ker_key, ker_class);                                   \
+  REGISTER_JITKERNEL_WITH_DTYPE(ker_class, float, JITKERNEL_DECLARE,       \
+                                JITKERNEL_FIND_KEY, JITKERNEL_IMPL);       \
+  REGISTER_JITKERNEL_WITH_DTYPE(ker_class, double, JITKERNEL_DECLARE,      \
+                                JITKERNEL_FIND_KEY, JITKERNEL_IMPL)
+
+#define REGISTER_JITKERNEL(ker_key, ker_class)                       \
+  REGISTER_JITKERNEL_ARGS(ker_key, ker_class, JITKERNEL_DEFINE_NAME, \
+                          JITKERNEL_DECLARE, JITKERNEL_FIND_KEY,     \
+                          JITKERNEL_IMPL)
+
+namespace jit = platform::jit;
+// TODO(TJ): below defines are deprecated, would be remove recently
 #define SEARCH_BLOCK(macro_, ker, dtype, isa)                 \
   if (d < AVX_FLOAT_BLOCK) {                                  \
     macro_(ker, dtype, isa, kLT8);                            \
@@ -47,20 +110,16 @@ namespace jit = platform::jit;
     SEARCH_BLOCK(macro_, ker, dtype, jit::isa_any); \
   }
 
-#define JITKERNEL_DECLARE(ker_class, ker_dtype) \
-  template <>                                   \
-  std::shared_ptr<const ker_class<ker_dtype>>   \
-  KernelPool::Get<ker_class<ker_dtype>, int>(int d)
-
 #define JITKERNEL_KEY(ker_key, dtype_key) \
   #ker_key #dtype_key + std::to_string(d)
 
-#define JITKERNEL_NEW_IMPL(ker, dtype, isa, k) \
+#define JITKERNEL_NEW_IMPL_DEPRECATED(ker, dtype, isa, k) \
   p = std::dynamic_pointer_cast<ker<dtype>>(              \
       std::make_shared<ker##Impl<dtype, isa, k>>(d))
 
-#define JITKERNEL_WITH_DTYPE(ker_key, ker_class, ker_dtype, dtype_key, \
-                             marco_declare, macro_key, macro_impl)     \
+#define JITKERNEL_WITH_DTYPE_DEPRECATED(ker_key, ker_class, ker_dtype,       \
+                                        dtype_key, marco_declare, macro_key, \
+                                        macro_impl)                          \
   marco_declare(ker_class, ker_dtype) {                                      \
     std::string key = macro_key(ker_key, dtype_key);                         \
     if (kers_.find(key) == kers_.end()) {                                    \
@@ -73,18 +132,20 @@ namespace jit = platform::jit;
         kers_.at(key));                                                      \
   }
 
-#define REGISTER_JITKERNEL(ker_key, ker_class)                           \
-  JITKERNEL_WITH_DTYPE(ker_key, ker_class, float, f, JITKERNEL_DECLARE,  \
-                       JITKERNEL_KEY, JITKERNEL_NEW_IMPL);               \
-  JITKERNEL_WITH_DTYPE(ker_key, ker_class, double, d, JITKERNEL_DECLARE, \
-                       JITKERNEL_KEY, JITKERNEL_NEW_IMPL)
-
-#define REGISTER_JITKERNEL_ARGS(ker_key, ker_class, marco_declare, macro_key,  \
-                                macro_impl)                                    \
-  JITKERNEL_WITH_DTYPE(ker_key, ker_class, float, f, marco_declare, macro_key, \
-                       macro_impl);                                            \
-  JITKERNEL_WITH_DTYPE(ker_key, ker_class, double, d, marco_declare,           \
-                       macro_key, macro_impl)
+#define REGISTER_JITKERNEL_DEPRECATED(ker_key, ker_class)           \
+  JITKERNEL_WITH_DTYPE_DEPRECATED(ker_key, ker_class, float, f,     \
+                                  JITKERNEL_DECLARE, JITKERNEL_KEY, \
+                                  JITKERNEL_NEW_IMPL_DEPRECATED);   \
+  JITKERNEL_WITH_DTYPE_DEPRECATED(ker_key, ker_class, double, d,    \
+                                  JITKERNEL_DECLARE, JITKERNEL_KEY, \
+                                  JITKERNEL_NEW_IMPL_DEPRECATED)
+
+#define REGISTER_JITKERNEL_ARGS_DEPRECATED(ker_key, ker_class, marco_declare,  \
+                                           macro_key, macro_impl)              \
+  JITKERNEL_WITH_DTYPE_DEPRECATED(ker_key, ker_class, float, f, marco_declare, \
+                                  macro_key, macro_impl);                      \
+  JITKERNEL_WITH_DTYPE_DEPRECATED(ker_key, ker_class, double, d,               \
+                                  marco_declare, macro_key, macro_impl)
 
 #define FOR_EACH_ISA(macro_, block) \
   macro_(jit::avx512f, block);      \

paddle/fluid/operators/math/jit_kernel_rnn.cc

@@ -179,23 +179,23 @@ class LSTMKernelImpl : public LSTMKernel<T> {
 
     /* C_t = C_t-1 * fgated + cand_gated * igated */
     act_cand_d_->Compute(gates, gates);
-    vmul_d_->Compute(gates, gates + d_, gates + d_);
-    vmul_d_->Compute(ct_1, gates + d2_, gates + d2_);
+    vmul_d_->Compute(gates, gates + d_, gates + d_, d_);
+    vmul_d_->Compute(ct_1, gates + d2_, gates + d2_, d_);
     vadd_d_->Compute(gates + d_, gates + d2_, ct);
 
     /* H_t = act_cell(C_t) * ogated */
     act_cell_d_->Compute(ct, gates + d2_);
-    vmul_d_->Compute(gates + d2_, gates + d3_, ht);
+    vmul_d_->Compute(gates + d2_, gates + d3_, ht, d_);
   }
   void ComputeC1H1(T* gates, T* ct, T* ht, const T* wp_data) const override {
     /* C_t = igated * cgated*/
     act_gate_d_->Compute(gates + d_, gates + d_);
     act_cand_d_->Compute(gates, gates);
-    vmul_d_->Compute(gates, gates + d_, ct);
+    vmul_d_->Compute(gates, gates + d_, ct, d_);
     /* H_t = act_cell(C_t) * ogated */
     act_gate_d_->Compute(gates + d3_, gates + d3_);
     act_cell_d_->Compute(ct, gates + d2_);
-    vmul_d_->Compute(gates + d2_, gates + d3_, ht);
+    vmul_d_->Compute(gates + d2_, gates + d3_, ht, d_);
   }
 
  private:
@@ -289,36 +289,36 @@ class PeepholeKernelImpl : public LSTMKernel<T> {
   void ComputeCtHt(T* gates, const T* ct_1, T* ct, T* ht, const T* wp_data,
                    T* checked) const override {
     /* get fgated and igated*/
-    vmul_d_->Compute(wp_data, ct_1, checked);
-    vmul_d_->Compute(wp_data + d_, ct_1, checked + d_);
+    vmul_d_->Compute(wp_data, ct_1, checked, d_);
+    vmul_d_->Compute(wp_data + d_, ct_1, checked + d_, d_);
     vadd_d2_->Compute(checked, gates + d_, gates + d_);
     act_gate_d2_->Compute(gates + d_, gates + d_);
     /* C_t = C_t-1 * fgated + cand_gated * igated*/
     act_cand_d_->Compute(gates, gates);
-    vmul_d_->Compute(gates, gates + d_, gates + d_);
-    vmul_d_->Compute(ct_1, gates + d2_, gates + d2_);
+    vmul_d_->Compute(gates, gates + d_, gates + d_, d_);
+    vmul_d_->Compute(ct_1, gates + d2_, gates + d2_, d_);
     vadd_d_->Compute(gates + d_, gates + d2_, ct);
     /* get ogated*/
-    vmul_d_->Compute(wp_data + d2_, ct, gates + d_);
+    vmul_d_->Compute(wp_data + d2_, ct, gates + d_, d_);
     vadd_d_->Compute(gates + d_, gates + d3_, gates + d3_);
     act_gate_d_->Compute(gates + d3_, gates + d3_);
     /* H_t = act_cell(C_t) * ogated */
     act_cell_d_->Compute(ct, gates + d2_);
-    vmul_d_->Compute(gates + d2_, gates + d3_, ht);
+    vmul_d_->Compute(gates + d2_, gates + d3_, ht, d_);
   }
 
   void ComputeC1H1(T* gates, T* ct, T* ht, const T* wp_data) const override {
     /* C_t = igated * cgated*/
     act_gate_d_->Compute(gates + d_, gates + d_);
     act_cand_d_->Compute(gates, gates);
-    vmul_d_->Compute(gates, gates + d_, ct);
+    vmul_d_->Compute(gates, gates + d_, ct, d_);
     /* get outgated, put W_oc * C_t on igated */
-    vmul_d_->Compute(wp_data + d2_, ct, gates + d_);
+    vmul_d_->Compute(wp_data + d2_, ct, gates + d_, d_);
     vadd_d_->Compute(gates + d_, gates + d3_, gates + d3_);
     /* H_t = act_cell(C_t) * ogated */
     act_gate_d_->Compute(gates + d3_, gates + d3_);
     act_cell_d_->Compute(ct, gates + d2_);
-    vmul_d_->Compute(gates + d2_, gates + d3_, ht);
+    vmul_d_->Compute(gates + d2_, gates + d3_, ht, d_);
   }
 
  private:
@@ -352,7 +352,7 @@ class PeepholeKernelImpl : public LSTMKernel<T> {
                                                    act_cell, d));      \
   }
 
-REGISTER_JITKERNEL_ARGS(lstm, LSTMKernel, JITKERNEL_DECLARE_LSTM,
+REGISTER_JITKERNEL_ARGS_DEPRECATED(lstm, LSTMKernel, JITKERNEL_DECLARE_LSTM,
                                    JITKERNEL_KEY_LSTM, JITKERNEL_NEW_LSTM_IMPL);
 
 #undef INTRI8_FLOAT
@@ -378,13 +378,13 @@ class GRUKernelImpl : public GRUKernel<T> {
   void ComputeH1(T* gates, T* ht) const override {
     act_gate_d_->Compute(gates, gates);
     act_state_d_->Compute(gates + d2_, gates + d2_);
-    vmul_d_->Compute(gates, gates + d2_, ht);
+    vmul_d_->Compute(gates, gates + d2_, ht, d_);
   }
 
   void ComputeHtPart1(T* gates, const T* ht_1, T* ht) const override {
     // W: {W_update, W_reset; W_state}
     act_gate_d2_->Compute(gates, gates);
-    vmul_d_->Compute(ht_1, gates + d_, ht);
+    vmul_d_->Compute(ht_1, gates + d_, ht, d_);
   }
 
   void ComputeHtPart2(T* gates, const T* ht_1, T* ht) const override {
@@ -472,7 +472,7 @@ INTRI8_FLOAT(jit::avx512f);
   p = std::dynamic_pointer_cast<ker<dtype>>(       \
       std::make_shared<ker##Impl<dtype, isa, k>>(act_gate, act_state, d));
 
-REGISTER_JITKERNEL_ARGS(gru, GRUKernel, JITKERNEL_DECLARE_GRU,
+REGISTER_JITKERNEL_ARGS_DEPRECATED(gru, GRUKernel, JITKERNEL_DECLARE_GRU,
                                    JITKERNEL_KEY_GRU, JITKERNEL_NEW_GRU_IMPL);
 
 #undef INTRI8_FLOAT

paddle/fluid/operators/math/jit_kernel_test.cc

@@ -369,12 +369,12 @@ void lstm_ctht_better(
   int d2 = d * 2;
   vsigmoid_3d->Compute(gates + d, gates + d);
   vtanh_d->Compute(gates, gates);
-  vmul_d->Compute(gates, gates + d, gates + d);
-  vmul_d->Compute(ct_1, gates + d2, gates + d2);
+  vmul_d->Compute(gates, gates + d, gates + d, d);
+  vmul_d->Compute(ct_1, gates + d2, gates + d2, d);
   vadd_d->Compute(gates + d, gates + d2, ct);
   /* H_t = act_cell(C_t) * ogated */
   vtanh_d->Compute(ct, gates + d2);
-  vmul_d->Compute(gates + d2, gates + d * 3, ht);
+  vmul_d->Compute(gates + d2, gates + d * 3, ht, d);
 }
 
 TEST(JitKernel, lstm) {
@@ -578,7 +578,7 @@ void vmul_mkl(const int n, const float* x, const float* y, float* z) {
 
 TEST(JitKernel, vmul) {
   namespace jit = paddle::operators::math::jitkernel;
-  for (int d : {7, 8, 15, 16, 30, 256, 512}) {
+  for (int d : {7, 8, 15, 16, 20, 30, 256, 512, 1000, 1024}) {
     std::vector<float> x(d), y(d);
     std::vector<float> zref(d), ztgt(d);
     RandomVec<float>(d, x.data());
@@ -616,7 +616,7 @@ TEST(JitKernel, vmul) {
 
     auto ttgts = GetCurrentUS();
     for (int i = 0; i < repeat; ++i) {
-      ker->Compute(x_data, y_data, ztgt_data);
+      ker->Compute(x_data, y_data, ztgt_data, d);
     }
     auto ttgte = GetCurrentUS();
 
@@ -800,8 +800,8 @@ TEST(JitKernel, pool) {
   EXPECT_TRUE(std::dynamic_pointer_cast<const jit::Kernel>(pvmul_f) !=
               std::dynamic_pointer_cast<const jit::Kernel>(pvmul_d));
 
-  const auto& pvmul_from_key = jit::KernelPool::Instance().Get("vmulf4");
+  const auto& pvmul_from_key = jit::KernelPool::Instance().Get("vmulfjit4");
   EXPECT_EQ(pvmul_f, pvmul_from_key);
-  const auto& pvmul_from_key2 = jit::KernelPool::Instance().Get("vmulf5");
+  const auto& pvmul_from_key2 = jit::KernelPool::Instance().Get("vmulfjit");
   EXPECT_TRUE(pvmul_from_key2 == nullptr);
 }

paddle/fluid/operators/math/pooling.cc

@@ -31,7 +31,7 @@ class Pool2dFunctor<platform::CPUDeviceContext, PoolProcess, T> {
                   const framework::Tensor& input, const std::vector<int>& ksize,
                   const std::vector<int>& strides,
                   const std::vector<int>& paddings, PoolProcess pool_process,
-                  framework::Tensor* output) {
+                  bool exclusive, framework::Tensor* output) {
     const int batch_size = input.dims()[0];
     const int input_height = input.dims()[2];
     const int input_width = input.dims()[3];
@@ -68,7 +68,8 @@ class Pool2dFunctor<platform::CPUDeviceContext, PoolProcess, T> {
                 pool_process.compute(input_data[h * input_width + w], &ele);
               }
             }
-            int pool_size = (hend - hstart) * (wend - wstart);
+            int pool_size = exclusive ? (hend - hstart) * (wend - wstart)
+                                      : ksize_height * ksize_width;
             pool_process.finalize(static_cast<T>(pool_size), &ele);
             output_data[ph * output_width + pw] = ele;
           }
@@ -93,7 +94,7 @@ class Pool2dGradFunctor<platform::CPUDeviceContext, PoolProcess, T> {
       const framework::Tensor& output, const framework::Tensor& output_grad,
       const std::vector<int>& ksize, const std::vector<int>& strides,
       const std::vector<int>& paddings, PoolProcess pool_grad_process,
-      framework::Tensor* input_grad) {
+      bool exclusive, framework::Tensor* input_grad) {
     const int batch_size = input.dims()[0];
     const int input_height = input.dims()[2];
     const int input_width = input.dims()[3];
@@ -124,7 +125,8 @@ class Pool2dGradFunctor<platform::CPUDeviceContext, PoolProcess, T> {
             int wstart = pw * stride_width - padding_width;
             int wend = std::min(wstart + ksize_width, input_width);
             wstart = std::max(wstart, 0);
-            int pool_size = (hend - hstart) * (wend - wstart);
+            int pool_size = exclusive ? (hend - hstart) * (wend - wstart)
+                                      : ksize_height * ksize_width;
             float scale = 1.0 / pool_size;
             for (int h = hstart; h < hend; ++h) {
               for (int w = wstart; w < wend; ++w) {
@@ -249,7 +251,7 @@ class Pool3dFunctor<platform::CPUDeviceContext, PoolProcess, T> {
                   const framework::Tensor& input, const std::vector<int>& ksize,
                   const std::vector<int>& strides,
                   const std::vector<int>& paddings, PoolProcess pool_process,
-                  framework::Tensor* output) {
+                  bool exclusive, framework::Tensor* output) {
     const int batch_size = input.dims()[0];
     const int input_depth = input.dims()[2];
     const int input_height = input.dims()[3];
@@ -300,7 +302,9 @@ class Pool3dFunctor<platform::CPUDeviceContext, PoolProcess, T> {
                 }
               }
               int pool_size =
-                  (dend - dstart) * (hend - hstart) * (wend - wstart);
+                  exclusive
+                      ? (dend - dstart) * (hend - hstart) * (wend - wstart)
+                      : ksize_depth * ksize_height * ksize_width;
               pool_process.finalize(static_cast<T>(pool_size), &ele);
               output_data[output_idx] = ele;
             }
@@ -326,7 +330,7 @@ class Pool3dGradFunctor<platform::CPUDeviceContext, PoolProcess, T> {
       const framework::Tensor& output, const framework::Tensor& output_grad,
       const std::vector<int>& ksize, const std::vector<int>& strides,
       const std::vector<int>& paddings, PoolProcess pool_grad_process,
-      framework::Tensor* input_grad) {
+      bool exclusive, framework::Tensor* input_grad) {
     const int batch_size = input.dims()[0];
     const int input_depth = input.dims()[2];
     const int input_height = input.dims()[3];
@@ -369,7 +373,9 @@ class Pool3dGradFunctor<platform::CPUDeviceContext, PoolProcess, T> {
               wstart = std::max(wstart, 0);
 
               int pool_size =
-                  (dend - dstart) * (hend - hstart) * (wend - wstart);
+                  exclusive
+                      ? (dend - dstart) * (hend - hstart) * (wend - wstart)
+                      : ksize_depth * ksize_height * ksize_width;
               float scale = 1.0 / pool_size;
               for (int d = dstart; d < dend; ++d) {
                 for (int h = hstart; h < hend; ++h) {

paddle/fluid/operators/math/pooling.cu

@@ -29,7 +29,7 @@ __global__ void KernelPool2D(const int nthreads, const T* input_data,
                              const int ksize_width, const int stride_height,
                              const int stride_width, const int padding_height,
                              const int padding_width, PoolProcess pool_process,
-                             T* output_data) {
+                             bool exclusive, T* output_data) {
   for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads;
        index += blockDim.x * gridDim.x) {
     int pw = index % output_width;
@@ -52,7 +52,8 @@ __global__ void KernelPool2D(const int nthreads, const T* input_data,
         pool_process.compute(input_data[h * input_width + w], &ele);
       }
     }
-    int pool_size = (hend - hstart) * (wend - wstart);
+    int pool_size = exclusive ? (hend - hstart) * (wend - wstart)
+                              : ksize_height * ksize_width;
     pool_process.finalize(static_cast<T>(pool_size), &ele);
     output_data[index] = ele;
   }
@@ -65,7 +66,7 @@ __global__ void KernelPool2DGrad(
     const int input_width, const int output_height, const int output_width,
     const int ksize_height, const int ksize_width, const int stride_height,
     const int stride_width, const int padding_height, const int padding_width,
-    PoolProcess pool_process, T* input_grad) {
+    PoolProcess pool_process, bool exclusive, T* input_grad) {
   for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads;
        index += blockDim.x * gridDim.x) {
     int offsetW = index % input_width + padding_width;
@@ -95,7 +96,8 @@ __global__ void KernelPool2DGrad(
         int wend = min(wstart + ksize_width, input_width);
         hstart = max(hstart, 0);
         wstart = max(wstart, 0);
-        int pool_size = (hend - hstart) * (wend - wstart);
+        int pool_size = exclusive ? (hend - hstart) * (wend - wstart)
+                                  : ksize_height * ksize_width;
         int output_sub_idx = ph * output_width + pw;
         pool_process.compute(input, output_data[output_sub_idx],
                              output_grad[output_sub_idx],
@@ -163,7 +165,7 @@ class Pool2dFunctor<platform::CUDADeviceContext, PoolProcess, T> {
                   const framework::Tensor& input, const std::vector<int>& ksize,
                   const std::vector<int>& strides,
                   const std::vector<int>& paddings, PoolProcess pool_process,
-                  framework::Tensor* output) {
+                  bool exclusive, framework::Tensor* output) {
     const int batch_size = input.dims()[0];
     const int input_channels = input.dims()[1];
     const int input_height = input.dims()[2];
@@ -189,7 +191,8 @@ class Pool2dFunctor<platform::CUDADeviceContext, PoolProcess, T> {
     KernelPool2D<PoolProcess, T><<<grid, threads, 0, context.stream()>>>(
         nthreads, input_data, input_channels, input_height, input_width,
         output_height, output_width, ksize_height, ksize_width, stride_height,
-        stride_width, padding_height, padding_width, pool_process, output_data);
+        stride_width, padding_height, padding_width, pool_process, exclusive,
+        output_data);
   }
 };
 
@@ -208,7 +211,7 @@ class Pool2dGradFunctor<platform::CUDADeviceContext, PoolProcess, T> {
                   const std::vector<int>& ksize,
                   const std::vector<int>& strides,
                   const std::vector<int>& paddings, PoolProcess pool_process,
-                  framework::Tensor* input_grad) {
+                  bool exclusive, framework::Tensor* input_grad) {
     const int batch_size = input.dims()[0];
     const int input_channels = input.dims()[1];
     const int input_height = input.dims()[2];
@@ -236,7 +239,7 @@ class Pool2dGradFunctor<platform::CUDADeviceContext, PoolProcess, T> {
         nthreads, input_data, output_data, output_grad_data, input_channels,
         input_height, input_width, output_height, output_width, ksize_height,
         ksize_width, stride_height, stride_width, padding_height, padding_width,
-        pool_process, input_grad_data);
+        pool_process, exclusive, input_grad_data);
   }
 };
 
@@ -313,16 +316,14 @@ template class Pool2dGradFunctor<platform::CUDADeviceContext,
                                  double>;
 
 template <typename PoolProcess, typename T>
-__global__ void KernelPool3D(const int nthreads, const T* input_data,
-                             const int channels, const int input_depth,
-                             const int input_height, const int input_width,
-                             const int output_depth, const int output_height,
-                             const int output_width, const int ksize_depth,
-                             const int ksize_height, const int ksize_width,
-                             const int stride_depth, const int stride_height,
-                             const int stride_width, const int padding_depth,
-                             const int padding_height, const int padding_width,
-                             PoolProcess pool_process, T* output_data) {
+__global__ void KernelPool3D(
+    const int nthreads, const T* input_data, const int channels,
+    const int input_depth, const int input_height, const int input_width,
+    const int output_depth, const int output_height, const int output_width,
+    const int ksize_depth, const int ksize_height, const int ksize_width,
+    const int stride_depth, const int stride_height, const int stride_width,
+    const int padding_depth, const int padding_height, const int padding_width,
+    PoolProcess pool_process, bool exclusive, T* output_data) {
   for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads;
        index += blockDim.x * gridDim.x) {
     int pw = index % output_width;
@@ -351,7 +352,9 @@ __global__ void KernelPool3D(const int nthreads, const T* input_data,
         }
       }
     }
-    int pool_size = (dend - dstart) * (hend - hstart) * (wend - wstart);
+    int pool_size = exclusive
+                        ? (dend - dstart) * (hend - hstart) * (wend - wstart)
+                        : ksize_depth * ksize_height * ksize_width;
     pool_process.finalize(static_cast<T>(pool_size), &ele);
     output_data[index] = ele;
   }
@@ -366,7 +369,7 @@ __global__ void KernelPool3DGrad(
     const int ksize_height, const int ksize_width, const int stride_depth,
     const int stride_height, const int stride_width, const int padding_depth,
     const int padding_height, const int padding_width, PoolProcess pool_process,
-    T* input_grad) {
+    bool exclusive, T* input_grad) {
   for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads;
        index += blockDim.x * gridDim.x) {
     int offsetW = index % input_width + padding_width;
@@ -409,7 +412,9 @@ __global__ void KernelPool3DGrad(
           dstart = max(dstart, 0);
           hstart = max(hstart, 0);
           wstart = max(wstart, 0);
-          int pool_size = (dend - dstart) * (hend - hstart) * (wend - wstart);
+          int pool_size =
+              exclusive ? (dend - dstart) * (hend - hstart) * (wend - wstart)
+                        : ksize_depth * ksize_height * ksize_width;
           int output_sub_idx = (pd * output_height + ph) * output_width + pw;
           pool_process.compute(input, output_data[output_sub_idx],
                                output_grad[output_sub_idx],
@@ -484,7 +489,7 @@ class Pool3dFunctor<platform::CUDADeviceContext, PoolProcess, T> {
                   const framework::Tensor& input, const std::vector<int>& ksize,
                   const std::vector<int>& strides,
                   const std::vector<int>& paddings, PoolProcess pool_process,
-                  framework::Tensor* output) {
+                  bool exclusive, framework::Tensor* output) {
     const int batch_size = input.dims()[0];
     const int input_channels = input.dims()[1];
     const int input_depth = input.dims()[2];
@@ -517,7 +522,7 @@ class Pool3dFunctor<platform::CUDADeviceContext, PoolProcess, T> {
         nthreads, input_data, input_channels, input_depth, input_height,
         input_width, output_depth, output_height, output_width, ksize_depth,
         ksize_height, ksize_width, stride_depth, stride_height, stride_width,
-        padding_depth, padding_height, padding_width, pool_process,
+        padding_depth, padding_height, padding_width, pool_process, exclusive,
         output_data);
   }
 };
@@ -537,7 +542,7 @@ class Pool3dGradFunctor<platform::CUDADeviceContext, PoolProcess, T> {
                   const std::vector<int>& ksize,
                   const std::vector<int>& strides,
                   const std::vector<int>& paddings, PoolProcess pool_process,
-                  framework::Tensor* input_grad) {
+                  bool exclusive, framework::Tensor* input_grad) {
     const int batch_size = input.dims()[0];
     const int input_channels = input.dims()[1];
     const int input_depth = input.dims()[2];
@@ -573,7 +578,7 @@ class Pool3dGradFunctor<platform::CUDADeviceContext, PoolProcess, T> {
         input_depth, input_height, input_width, output_depth, output_height,
         output_width, ksize_depth, ksize_height, ksize_width, stride_depth,
         stride_height, stride_width, padding_depth, padding_height,
-        padding_width, pool_process, input_grad_data);
+        padding_width, pool_process, exclusive, input_grad_data);
   }
 };
 

paddle/fluid/operators/math/pooling.h

@@ -89,7 +89,7 @@ class Pool2dFunctor {
                   const std::vector<int>& ksize,
                   const std::vector<int>& strides,
                   const std::vector<int>& paddings, PoolProcess pool_compute,
-                  framework::Tensor* output);
+                  bool exclusive, framework::Tensor* output);
 };
 
 template <typename DeviceContext, typename PoolProcess, typename T>
@@ -101,7 +101,7 @@ class Pool2dGradFunctor {
                   const std::vector<int>& ksize,
                   const std::vector<int>& strides,
                   const std::vector<int>& paddings, PoolProcess pool_compute,
-                  framework::Tensor* input_grad);
+                  bool exclusive, framework::Tensor* input_grad);
 };
 
 template <typename DeviceContext, class T>
@@ -123,7 +123,7 @@ class Pool3dFunctor {
                   const std::vector<int>& ksize,
                   const std::vector<int>& strides,
                   const std::vector<int>& paddings, PoolProcess pool_compute,
-                  framework::Tensor* output);
+                  bool exclusive, framework::Tensor* output);
 };
 
 template <typename DeviceContext, typename PoolProcess, typename T>
@@ -135,7 +135,7 @@ class Pool3dGradFunctor {
                   const std::vector<int>& ksize,
                   const std::vector<int>& strides,
                   const std::vector<int>& paddings, PoolProcess pool_compute,
-                  framework::Tensor* input_grad);
+                  bool exclusive, framework::Tensor* input_grad);
 };
 
 template <typename DeviceContext, class T>

paddle/fluid/operators/pool_cudnn_op.cu.cc

@@ -41,6 +41,7 @@ class PoolCUDNNOpKernel : public framework::OpKernel<T> {
     T *output_data = output->mutable_data<T>(ctx.GetPlace());
 
     std::string pooling_type = ctx.Attr<std::string>("pooling_type");
+    bool exclusive = ctx.Attr<bool>("exclusive");
     std::vector<int> ksize = ctx.Attr<std::vector<int>>("ksize");
     std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
     std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
@@ -72,7 +73,8 @@ class PoolCUDNNOpKernel : public framework::OpKernel<T> {
     if (pooling_type == "max") {
       pooling_mode = PoolingMode::kMaximum;
     } else {
-      pooling_mode = PoolingMode::kAverage;
+      pooling_mode = exclusive ? PoolingMode::kAverageExclusive
+                               : PoolingMode::kAverageInclusive;
     }
 
     cudnnPoolingDescriptor_t cudnn_pool_desc =
@@ -101,6 +103,7 @@ class PoolCUDNNGradOpKernel : public framework::OpKernel<T> {
     Tensor *input_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
 
     std::string pooling_type = ctx.Attr<std::string>("pooling_type");
+    bool exclusive = ctx.Attr<bool>("exclusive");
     std::vector<int> ksize = ctx.Attr<std::vector<int>>("ksize");
     std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
     std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
@@ -141,7 +144,8 @@ class PoolCUDNNGradOpKernel : public framework::OpKernel<T> {
         pooling_mode = PoolingMode::kMaximum;
       }
     } else {
-      pooling_mode = PoolingMode::kAverage;
+      pooling_mode = exclusive ? PoolingMode::kAverageExclusive
+                               : PoolingMode::kAverageInclusive;
     }
 
     cudnnPoolingDescriptor_t cudnn_pool_desc =

paddle/fluid/operators/pool_op.cc

@@ -180,6 +180,12 @@ void Pool2dOpMaker::Make() {
       "operator."
       "If global_pooling = true, paddings and ksize will be ignored.")
       .SetDefault({0, 0});
+  AddAttr<bool>(
+      "exclusive",
+      "(bool, default True) When true, will exclude the zero-padding in the "
+      "averaging calculating, otherwise, include the zero-padding. Note, it "
+      "is only used when pooling_type is avg. The defalut is True.")
+      .SetDefault(true);
   AddAttr<bool>(
       "use_cudnn",
       "(bool, default false) Only used in cudnn kernel, need install cudnn")
@@ -236,6 +242,23 @@ Example:
        W_{out} = \\frac{(W_{in} - ksize[1] + 2 * paddings[1] + strides[1] - 1)}{strides[1]} + 1
        $$
 
+  For exclusive = true:
+       $$
+       hstart = i * strides[0] - paddings[0]
+       hend = hstart + ksize[0]
+       wstart = j * strides[1] - paddings[1]
+       wend = wstart + ksize[1]
+       Output(i ,j) = \\frac{sum(Input[hstart:hend, wstart:wend])}{ksize[0] * ksize[1]}
+       $$
+  For exclusive = false:
+       $$
+       hstart = max(0, i * strides[0] - paddings[0])
+       hend = min(H, hstart + ksize[0])
+       wstart = max(0, j * strides[1] - paddings[1])
+       wend = min(W, wstart + ksize[1])
+       Output(i ,j) = \\frac{sum(Input[hstart:hend, wstart:wend])}{(hend - hstart) * (wend - wstart)}
+       $$
+
 )DOC");
 }
 
@@ -283,6 +306,12 @@ void Pool3dOpMaker::Make() {
       "If global_pooling = true, ksize and paddings will be ignored.")
       .SetDefault({0, 0, 0});  // TODO(Chengduo): Add checker. (Currently,
                                // TypedAttrChecker don't support vector type.)
+  AddAttr<bool>(
+      "exclusive",
+      "(bool, default True) When true, will exclude the zero-padding in the "
+      "averaging calculating, otherwise, include the zero-padding. Note, it "
+      "is only used when pooling_type is avg. The defalut is True.")
+      .SetDefault(true);
 
   AddAttr<bool>(
       "use_cudnn",

paddle/fluid/operators/pool_op.h

@@ -69,6 +69,7 @@ class PoolKernel : public framework::OpKernel<T> {
     std::vector<int> ksize = context.Attr<std::vector<int>>("ksize");
     std::vector<int> strides = context.Attr<std::vector<int>>("strides");
     std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
+    bool exclusive = context.Attr<bool>("exclusive");
     if (context.Attr<bool>("global_pooling")) {
       for (size_t i = 0; i < ksize.size(); ++i) {
         paddings[i] = 0;
@@ -84,7 +85,7 @@ class PoolKernel : public framework::OpKernel<T> {
               pool2d_forward;
           paddle::operators::math::MaxPool<T> pool_process;
           pool2d_forward(dev_ctx, *in_x, ksize, strides, paddings, pool_process,
-                         out);
+                         true, out);
 
         } else if (pooling_type == "avg") {
           paddle::operators::math::Pool2dFunctor<
@@ -92,7 +93,7 @@ class PoolKernel : public framework::OpKernel<T> {
               pool2d_forward;
           paddle::operators::math::AvgPool<T> pool_process;
           pool2d_forward(dev_ctx, *in_x, ksize, strides, paddings, pool_process,
-                         out);
+                         exclusive, out);
         }
       } break;
       case 3: {
@@ -102,14 +103,14 @@ class PoolKernel : public framework::OpKernel<T> {
               pool3d_forward;
           paddle::operators::math::MaxPool<T> pool_process;
           pool3d_forward(dev_ctx, *in_x, ksize, strides, paddings, pool_process,
-                         out);
+                         true, out);
         } else if (pooling_type == "avg") {
           paddle::operators::math::Pool3dFunctor<
               DeviceContext, paddle::operators::math::AvgPool<T>, T>
               pool3d_forward;
           paddle::operators::math::AvgPool<T> pool_process;
           pool3d_forward(dev_ctx, *in_x, ksize, strides, paddings, pool_process,
-                         out);
+                         exclusive, out);
         }
       } break;
       default: { PADDLE_THROW("Pool op only supports 2D and 3D input."); }
@@ -131,6 +132,7 @@ class PoolGradKernel : public framework::OpKernel<T> {
     std::vector<int> ksize = context.Attr<std::vector<int>>("ksize");
     std::vector<int> strides = context.Attr<std::vector<int>>("strides");
     std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
+    bool exclusive = context.Attr<bool>("exclusive");
 
     if (context.Attr<bool>("global_pooling")) {
       for (size_t i = 0; i < ksize.size(); ++i) {
@@ -157,7 +159,7 @@ class PoolGradKernel : public framework::OpKernel<T> {
                 pool2d_backward;
             paddle::operators::math::AvgPoolGrad<T> pool_process;
             pool2d_backward(dev_ctx, *in_x, *out, *out_grad, ksize, strides,
-                            paddings, pool_process, in_x_grad);
+                            paddings, pool_process, exclusive, in_x_grad);
           }
         } break;
         case 3: {
@@ -172,7 +174,7 @@ class PoolGradKernel : public framework::OpKernel<T> {
                 pool3d_backward;
             paddle::operators::math::AvgPoolGrad<T> pool_process;
             pool3d_backward(dev_ctx, *in_x, *out, *out_grad, ksize, strides,
-                            paddings, pool_process, in_x_grad);
+                            paddings, pool_process, exclusive, in_x_grad);
           }
         } break;
         default: { PADDLE_THROW("Pool op only supports 2D and 3D input."); }

paddle/fluid/operators/prefetch_op.cc

@@ -42,7 +42,8 @@ class PrefetchOp : public framework::OperatorBase {
     auto& ctx = *pool.Get(place);
 
     distributed::RPCClient* rpc_client =
-        distributed::RPCClient::GetInstance<RPCCLIENT_T>();
+        distributed::RPCClient::GetInstance<RPCCLIENT_T>(
+            Attr<int>("trainer_id"));
 
     std::vector<distributed::VarHandlePtr> rets;
     for (size_t i = 0; i < ins.size(); i++) {
@@ -69,6 +70,7 @@ class PrefetchOpMaker : public framework::OpProtoAndCheckerMaker {
               "(LoDTensor) result "
               "to be fetched from parameter server")
         .AsDuplicable();
+    AddAttr<int>("trainer_id", "trainer id from 0 ~ worker_num.").SetDefault(0);
     AddAttr<std::vector<std::string>>(
         "epmap",
         "(string vector, default 127.0.0.1:6164)"

paddle/fluid/operators/read_op.cc

@@ -33,6 +33,19 @@ class ReadInferShape : public framework::InferShapeBase {
         reader_dims.size(), out_names.size(),
         "The reader's dim number doesn't match the output number.");
     ctx->SetOutputsDim("Out", reader_dims);
+    if (!ctx->IsRuntime()) {
+      auto in_desc =
+          boost::get<framework::VarDesc*>(ctx->GetInputVarPtrs("Reader")[0]);
+      auto in_lod_levels = in_desc->GetLoDLevels();
+      auto out_var_ptrs = ctx->GetOutputVarPtrs("Out");
+      PADDLE_ENFORCE_EQ(in_lod_levels.size(), out_var_ptrs.size(),
+                        "LoDLevels of Input(Reader) must be the same as the "
+                        "number of Outputs(Out).");
+      for (size_t i = 0; i < out_var_ptrs.size(); ++i) {
+        auto* out_desc = boost::get<framework::VarDesc*>(out_var_ptrs[i]);
+        out_desc->SetLoDLevel(in_lod_levels[i]);
+      }
+    }
   }
 };
 

paddle/fluid/operators/recv_op.cc

@@ -42,7 +42,8 @@ class RecvOp : public framework::OperatorBase {
     auto& ctx = *pool.Get(place);
 
     distributed::RPCClient* rpc_client =
-        distributed::RPCClient::GetInstance<RPCCLIENT_T>();
+        distributed::RPCClient::GetInstance<RPCCLIENT_T>(
+            Attr<int>("trainer_id"));
 
     std::vector<distributed::VarHandlePtr> rets;
     for (size_t i = 0; i < outs.size(); i++) {
@@ -73,6 +74,7 @@ This operator can get variables from server side.
                                       "Server endpoints in the order of input "
                                       "variables for mapping")
         .SetDefault({});
+    AddAttr<int>("trainer_id", "trainer id from 0 ~ worker_num.").SetDefault(0);
     AddAttr<int>("sync_mode",
                  "(int, default 0)"
                  "sync recv or async recv.")

paddle/fluid/operators/ref_by_trainer_id_op.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/fluid/operators/ref_by_trainer_id_op.h"
+#include <string>
+
+namespace paddle {
+namespace operators {
+
+class RefByTrainerIdOp : public framework::OperatorWithKernel {
+ public:
+  RefByTrainerIdOp(const std::string &type,
+                   const framework::VariableNameMap &inputs,
+                   const framework::VariableNameMap &outputs,
+                   const framework::AttributeMap &attrs)
+      : OperatorWithKernel(type, inputs, outputs, attrs) {}
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInputs("X"),
+                   "Input(X) of RefByTrainerIdOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("TrainerId"),
+                   "Input(TrainerId) of RefByTrainerIdOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output(Out) of RefByTrainerIdOp should not be null.");
+    PADDLE_ENFORCE_EQ(ctx->GetInputDim("TrainerId").size(), 1,
+                      "TrainerId should be a scalar.");
+    // Out's shape is determined at runtime.
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext &ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(
+            ctx.MultiInput<framework::Tensor>("X")[0]->type()),
+        ctx.GetPlace());
+  }
+};
+
+class RefByTrainerIdOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X", "(Tensor) Input tensor list.").AsDuplicable();
+    AddInput("TrainerId", "(Tensor) Scalar int, the trainer id runtime value.");
+    AddOutput("Out", "(Tensor) Return one tensor reference of X[trainer_id]");
+    AddComment(R"DOC(
+**RefByTrainerId operator**
+
+Return a reference of a tensor, using trainer_id as the index to find from the input.
+
+$$Out = X[TrainerId]$$
+)DOC");
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_WITHOUT_GRADIENT(ref_by_trainer_id, ops::RefByTrainerIdOp,
+                             ops::RefByTrainerIdOpMaker);
+REGISTER_OP_CPU_KERNEL(
+    ref_by_trainer_id,
+    ops::RefByTrainerIdKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::RefByTrainerIdKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::RefByTrainerIdKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::RefByTrainerIdKernel<paddle::platform::CPUDeviceContext, int64_t>);

paddle/fluid/operators/ref_by_trainer_id_op.cu.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/fluid/operators/ref_by_trainer_id_op.h"
+
+REGISTER_OP_CUDA_KERNEL(
+    ref_by_trainer_id,
+    paddle::operators::RefByTrainerIdKernel<paddle::platform::CUDADeviceContext,
+                                            float>,
+    paddle::operators::RefByTrainerIdKernel<paddle::platform::CUDADeviceContext,
+                                            double>,
+    paddle::operators::RefByTrainerIdKernel<paddle::platform::CUDADeviceContext,
+                                            int>,
+    paddle::operators::RefByTrainerIdKernel<paddle::platform::CUDADeviceContext,
+                                            int64_t>);

paddle/fluid/operators/ref_by_trainer_id_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 <stdio.h>
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+template <typename DeviceContext, typename T>
+class RefByTrainerIdKernel : public framework::OpKernel<T> {
+ public:
+  virtual void Compute(const framework::ExecutionContext& context) const {
+    auto* out = context.Output<framework::Tensor>("Out");
+    auto in_list = context.MultiInput<framework::Tensor>("X");
+    auto* trainer_id_t = context.Input<framework::Tensor>("TrainerId");
+    int64_t trainer_id;
+    auto* trainer_id_data = trainer_id_t->data<int64_t>();
+    if (platform::is_gpu_place(context.GetPlace())) {
+#ifdef PADDLE_WITH_CUDA
+      auto stream = context.cuda_device_context().stream();
+      memory::Copy<>(platform::CPUPlace(), &trainer_id,
+                     boost::get<platform::CUDAPlace>(context.GetPlace()),
+                     trainer_id_data, sizeof(int64_t), stream);
+#endif
+    } else {
+      trainer_id = *trainer_id_data;
+    }
+    printf("after get trainer_id %lu\n", trainer_id);
+    PADDLE_ENFORCE_LT(trainer_id, in_list.size());
+    out->mutable_data<T>(context.GetPlace());
+    out->ShareDataWith(*(in_list[trainer_id]));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/send_barrier_op.cc

@@ -39,7 +39,8 @@ class SendBarrierOp : public framework::OperatorBase {
     std::vector<std::string> eps = Attr<std::vector<std::string>>("endpoints");
 
     distributed::RPCClient* rpc_client =
-        distributed::RPCClient::GetInstance<RPCCLIENT_T>();
+        distributed::RPCClient::GetInstance<RPCCLIENT_T>(
+            Attr<int>("trainer_id"));
 
     VLOG(3) << "SendBarrierOp sync";
 
@@ -67,6 +68,7 @@ This operator will send a send barrier signal to list_and_serv op, so that
 the Parameter Server would knew all variables have been sent.
 )DOC");
 
+    AddAttr<int>("trainer_id", "trainer id from 0 ~ worker_num.").SetDefault(0);
     AddAttr<std::vector<std::string>>("endpoints",
                                       "(string vector, default 127.0.0.1:6164)"
                                       "Server endpoints to send variables to.")

paddle/fluid/operators/send_op.cc

@@ -44,7 +44,8 @@ class SendOp : public framework::OperatorBase {
     auto& ctx = *pool.Get(place);
 
     distributed::RPCClient* rpc_client =
-        distributed::RPCClient::GetInstance<RPCCLIENT_T>();
+        distributed::RPCClient::GetInstance<RPCCLIENT_T>(
+            Attr<int>("trainer_id"));
 
     std::vector<distributed::VarHandlePtr> rets;
     for (size_t i = 0; i < ins.size(); i++) {
@@ -79,6 +80,7 @@ This operator will send variables to listen_and_serve op at the parameter server
                  "(int, default 0)"
                  "sync send or async send.")
         .SetDefault(0);
+    AddAttr<int>("trainer_id", "trainer id from 0 ~ worker_num.").SetDefault(0);
     AddAttr<std::vector<std::string>>("epmap",
                                       "(string vector, default 127.0.0.1:6164)"
                                       "Server endpoints in the order of input "

paddle/fluid/operators/sign_op.cc

@@ -67,4 +67,5 @@ namespace ops = paddle::operators;
 REGISTER_OPERATOR(sign, ops::SignOp, ops::SignOpMaker<float>,
                   ops::SignGradMaker);
 REGISTER_OP_CPU_KERNEL(
-    sign, ops::SignKernel<paddle::platform::CPUDeviceContext, float>);
+    sign, ops::SignKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::SignKernel<paddle::platform::CPUDeviceContext, double>);

paddle/fluid/operators/sign_op.cu

@@ -13,7 +13,11 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/sign_op.h"
+#include "paddle/fluid/platform/float16.h"
 
 REGISTER_OP_CUDA_KERNEL(
     sign,
-    paddle::operators::SignKernel<paddle::platform::CUDADeviceContext, float>);
+    paddle::operators::SignKernel<paddle::platform::CUDADeviceContext, float>,
+    paddle::operators::SignKernel<paddle::platform::CUDADeviceContext, double>,
+    paddle::operators::SignKernel<paddle::platform::CUDADeviceContext,
+                                  paddle::platform::float16>);

paddle/fluid/operators/softmax_with_cross_entropy_op.cc

@@ -44,6 +44,12 @@ class SoftmaxWithCrossEntropyOpMaker
         "(bool, default: false), A flag to indicate whether to interpretate "
         "the given labels as soft labels.")
         .SetDefault(false);
+    AddAttr<bool>(
+        "numeric_stable_mode",
+        "(bool, default: false), A flag to indicate whether to use more "
+        "numerically stable algorithm. This flag is only valid when "
+        "soft_label is false and GPU is used.")
+        .SetDefault(false);
     AddAttr<int>(
         "ignore_index",
         "(int, default -100), Specifies a target value that is ignored and"

paddle/fluid/operators/softmax_with_cross_entropy_op.cu

@@ -17,6 +17,7 @@ limitations under the License. */
 #include <cub/cub.cuh>
 #include "paddle/fluid/operators/math/cross_entropy.h"
 #include "paddle/fluid/operators/softmax_with_cross_entropy_op.h"
+#include "paddle/fluid/platform/for_range.h"
 
 namespace paddle {
 namespace operators {
@@ -117,7 +118,7 @@ using BlockReduceTempStorage = typename BlockReduce<T, BlockDim>::TempStorage;
 // Make sure that BlockDim <= feature_size
 // This kernel is used to calculate the max element of each row
 template <typename T, int BlockDim>
-__global__ void RowReductionForMax(const T* logits_data, T* max_data,
+static __global__ void RowReductionForMax(const T* logits_data, T* max_data,
                                           int feature_size) {
   __shared__ BlockReduceTempStorage<T, BlockDim> temp_storage;
 
@@ -141,9 +142,10 @@ __global__ void RowReductionForMax(const T* logits_data, T* max_data,
 }
 
 // Make sure that BlockDim <= feature_size
-template <typename T, int BlockDim>
-__global__ void RowReductionForDiffMaxSum(const T* logits_data, T* max_data,
-                                          T* softmax, int feature_size) {
+template <typename T, int BlockDim, bool CalculateLogSoftmax = false>
+static __global__ void RowReductionForDiffMaxSum(const T* logits_data,
+                                                 T* max_data, T* softmax,
+                                                 int feature_size) {
   __shared__ BlockReduceTempStorage<T, BlockDim> temp_storage;
 
   auto beg_idx = feature_size * blockIdx.x + threadIdx.x;
@@ -153,23 +155,33 @@ __global__ void RowReductionForDiffMaxSum(const T* logits_data, T* max_data,
 
   softmax[beg_idx] = logits_data[beg_idx] - block_max;
   T diff_max_sum = real_exp(softmax[beg_idx]);
-  beg_idx += BlockDim;
-  while (beg_idx < end_idx) {
-    softmax[beg_idx] = logits_data[beg_idx] - block_max;
-    diff_max_sum += real_exp(softmax[beg_idx]);
-    beg_idx += BlockDim;
+  auto idx = beg_idx + BlockDim;
+  while (idx < end_idx) {
+    softmax[idx] = logits_data[idx] - block_max;
+    diff_max_sum += real_exp(softmax[idx]);
+    idx += BlockDim;
   }
 
   diff_max_sum =
       BlockReduce<T, BlockDim>(temp_storage).Reduce(diff_max_sum, cub::Sum());
   if (threadIdx.x == 0) max_data[blockIdx.x] = real_log(diff_max_sum);
+
+  if (!CalculateLogSoftmax) return;
+  __syncthreads();
+  diff_max_sum = max_data[blockIdx.x];
+  softmax[beg_idx] -= diff_max_sum;
+  beg_idx += BlockDim;
+  while (beg_idx < end_idx) {
+    softmax[beg_idx] -= diff_max_sum;
+    beg_idx += BlockDim;
+  }
+  if (threadIdx.x == 0) max_data[blockIdx.x] = 0;
 }
 
 // Make sure that BlockDim <= feature_size
 template <typename T, int BlockDim>
-__global__ void RowReductionForSoftmaxAndCrossEntropy(const T* logits_data,
-                                                      const T* labels_data,
-                                                      T* loss_data, T* softmax,
+static __global__ void RowReductionForSoftmaxAndCrossEntropy(
+    const T* logits_data, const T* labels_data, T* loss_data, T* softmax,
     int feature_size) {
   __shared__ BlockReduceTempStorage<T, BlockDim> temp_storage;
 
@@ -194,11 +206,134 @@ __global__ void RowReductionForSoftmaxAndCrossEntropy(const T* logits_data,
 }
 
 template <typename T>
-__global__ void SetSoftmaxToOneWhenFeatureSizeIsOne(T* out, int batch_size) {
+struct HardLabelSoftmaxWithCrossEntropyFunctor {
+ public:
+  HardLabelSoftmaxWithCrossEntropyFunctor(const T* logits,
+                                          const int64_t* labels, T* loss,
+                                          T* log_softmax, int feature_size)
+      : logits_(logits),
+        labels_(labels),
+        loss_(loss),
+        log_softmax_(log_softmax),
+        feature_size_(feature_size) {}
+
+  __device__ void operator()(int idx) const {
+    auto row_idx = idx / feature_size_;
+    auto col_idx = idx % feature_size_;
+    if (col_idx != labels_[row_idx]) {
+      log_softmax_[idx] = real_exp(log_softmax_[idx]);
+    } else {
+      auto softmax = log_softmax_[idx];
+      log_softmax_[idx] = real_exp(softmax);
+      loss_[row_idx] = -softmax;
+    }
+  }
+
+ private:
+  const T* logits_;
+  const int64_t* labels_;
+  T* loss_;
+  T* log_softmax_;
+  int feature_size_;
+};
+
+template <typename T>
+struct HardLabelSoftmaxWithCrossEntropyFunctorWithIgnoreIdx {
+ public:
+  HardLabelSoftmaxWithCrossEntropyFunctorWithIgnoreIdx(const T* logits,
+                                                       const int64_t* labels,
+                                                       T* loss, T* log_softmax,
+                                                       int feature_size,
+                                                       int ignore_idx)
+      : logits_(logits),
+        labels_(labels),
+        loss_(loss),
+        log_softmax_(log_softmax),
+        feature_size_(feature_size),
+        ignore_idx_(ignore_idx) {}
+
+  __device__ void operator()(int idx) const {
+    auto row_idx = idx / feature_size_;
+    auto col_idx = idx % feature_size_;
+    if (col_idx != labels_[row_idx] || col_idx == ignore_idx_) {
+      log_softmax_[idx] = real_exp(log_softmax_[idx]);
+    } else {
+      auto softmax = log_softmax_[idx];
+      log_softmax_[idx] = real_exp(softmax);
+      loss_[row_idx] = -softmax;
+    }
+  }
+
+ private:
+  const T* logits_;
+  const int64_t* labels_;
+  T* loss_;
+  T* log_softmax_;
+  int feature_size_;
+  int ignore_idx_;
+};
+
+template <typename T>
+static __global__ void SetSoftmaxToOneWhenFeatureSizeIsOne(T* out,
+                                                           int batch_size) {
   auto idx = threadIdx.x + blockIdx.x * blockDim.x;
   if (idx < batch_size) out[idx] = static_cast<T>(1);
 }
 
+template <typename T>
+static void HardLabelSoftmaxWithCrossEntropy(
+    const platform::CUDADeviceContext& ctx, const T* logits_data,
+    const int64_t* labels_data, T* loss_data, T* softmax_data, int batch_size,
+    int feature_size, int ignore_idx) {
+  constexpr int kMaxBlockDim = 512;
+  int block_dim = feature_size >= kMaxBlockDim
+                      ? kMaxBlockDim
+                      : (1 << static_cast<int>(std::log2(feature_size)));
+  auto stream = ctx.stream();
+
+#define CALL_HARD_LABEL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(BlockDim)    \
+  case BlockDim: {                                                           \
+    RowReductionForMax<T, BlockDim><<<batch_size, BlockDim, 0, stream>>>(    \
+        logits_data, loss_data, feature_size);                               \
+    RowReductionForDiffMaxSum<T, BlockDim,                                   \
+                              true><<<batch_size, BlockDim, 0, stream>>>(    \
+        logits_data, loss_data, softmax_data, feature_size);                 \
+    platform::ForRange<platform::CUDADeviceContext> for_range(               \
+        ctx, batch_size* feature_size);                                      \
+    if (ignore_idx >= 0 && ignore_idx < feature_size) {                      \
+      for_range(HardLabelSoftmaxWithCrossEntropyFunctorWithIgnoreIdx<T>(     \
+          logits_data, labels_data, loss_data, softmax_data, feature_size,   \
+          ignore_idx));                                                      \
+    } else {                                                                 \
+      for_range(HardLabelSoftmaxWithCrossEntropyFunctor<T>(                  \
+          logits_data, labels_data, loss_data, softmax_data, feature_size)); \
+    }                                                                        \
+  } break
+
+  switch (block_dim) {
+    CALL_HARD_LABEL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(512);
+    CALL_HARD_LABEL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(256);
+    CALL_HARD_LABEL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(128);
+    CALL_HARD_LABEL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(64);
+    CALL_HARD_LABEL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(32);
+    CALL_HARD_LABEL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(16);
+    CALL_HARD_LABEL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(8);
+    CALL_HARD_LABEL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(4);
+    CALL_HARD_LABEL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(2);
+    case 1:
+      SetSoftmaxToOneWhenFeatureSizeIsOne<<<(batch_size + kMaxBlockDim - 1) /
+                                                kMaxBlockDim,
+                                            kMaxBlockDim, 0, stream>>>(
+          softmax_data, batch_size);
+      cudaMemsetAsync(loss_data, 0, batch_size * sizeof(T), stream);
+      break;
+    default:
+      PADDLE_THROW("BlockDim must be 2^n in softmax_with_cross_entropy_op");
+      break;
+  }
+#undef CALL_HARD_LABEL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL
+}
+
 template <typename T>
 static void SoftmaxWithCrossEntropyFusedKernel(const T* logits_data,
                                                const T* labels_data,
@@ -237,7 +372,7 @@ static void SoftmaxWithCrossEntropyFusedKernel(const T* logits_data,
                                                 kMaxBlockDim,
                                             kMaxBlockDim, 0, stream>>>(
           softmax_data, batch_size);
-      cudaMemsetAsync(loss_data, 0, batch_size, stream);
+      cudaMemsetAsync(loss_data, 0, batch_size * sizeof(T), stream);
       break;
     default:
       PADDLE_THROW("BlockDim must be 2^n in softmax_with_cross_entropy_op");
@@ -272,11 +407,21 @@ class SoftmaxWithCrossEntropyCUDAKernel : public framework::OpKernel<T> {
           logits_data, labels_data, softmax_data, loss_data, batch_size,
           feature_size, context.cuda_device_context().stream());
     } else {
+      if (!context.Attr<bool>("numeric_stable_mode")) {
         math::SoftmaxCUDNNFunctor<T>()(context.cuda_device_context(), logits,
                                        softmax);
         math::CrossEntropyFunctor<platform::CUDADeviceContext, T>()(
             context.cuda_device_context(), loss, softmax, labels, false,
             ignore_index);
+      } else {
+        int batch_size = logits->dims()[0];
+        int feature_size = logits->dims()[1];
+        auto* logits_data = logits->data<T>();
+        auto* labels_data = labels->data<int64_t>();
+        HardLabelSoftmaxWithCrossEntropy<T>(
+            context.cuda_device_context(), logits_data, labels_data, loss_data,
+            softmax_data, batch_size, feature_size, ignore_index);
+      }
     }
   }
 };

paddle/fluid/operators/spp_op.h

@@ -56,12 +56,14 @@ class SppKernel : public framework::OpKernel<T> {
         math::Pool2dFunctor<DeviceContext, math::MaxPool<T>, T> pool_forward;
         math::MaxPool<T> max_process;
         pool_forward(context.template device_context<DeviceContext>(), *in_x,
-                     kernel_size, strides, paddings, max_process, &out_level);
+                     kernel_size, strides, paddings, max_process, true,
+                     &out_level);
       } else if (pooling_type == "avg") {
         math::Pool2dFunctor<DeviceContext, math::AvgPool<T>, T> pool_forward;
         math::AvgPool<T> avg_process;
         pool_forward(context.template device_context<DeviceContext>(), *in_x,
-                     kernel_size, strides, paddings, avg_process, &out_level);
+                     kernel_size, strides, paddings, avg_process, true,
+                     &out_level);
       }
       // flatten pooling output shape
       int output_flatten_w = in_x->dims()[1] * bins * bins;
@@ -154,7 +156,7 @@ class SppGradKernel : public framework::OpKernel<T> {
         math::AvgPoolGrad<T> avg_process;
         pool_backward(context.template device_context<DeviceContext>(), *in_x,
                       *&out_level, *&outgrad_level, kernel_size, strides,
-                      paddings, avg_process, in_x_grad);
+                      paddings, avg_process, true, in_x_grad);
       }
     }
   }

paddle/fluid/operators/test_send_nccl_id.cc

@@ -92,7 +92,7 @@ TEST(SendNcclId, RPCServer) {
   std::string ep = string::Sprintf("127.0.0.1:%d", port);
 
   distributed::RPCClient* client =
-      distributed::RPCClient::GetInstance<RPCCLIENT_T>();
+      distributed::RPCClient::GetInstance<RPCCLIENT_T>(0);
 
   LOG(INFO) << "connect to server" << ep;
   client->AsyncSendVar(ep, dev_ctx, scope, NCCL_ID_VARNAME);

paddle/fluid/platform/cudnn_helper.h

@@ -76,8 +76,9 @@ enum class DataLayout {  // Not use
 
 enum class PoolingMode {
   kMaximum,
-  kAverage,
   kMaximumDeterministic,
+  kAverageExclusive,
+  kAverageInclusive,
 };
 
 #if CUDNN_VERSION < 6000
@@ -91,8 +92,10 @@ inline cudnnPoolingMode_t GetPoolingMode(const PoolingMode& mode) {
   switch (mode) {
     case PoolingMode::kMaximumDeterministic:
       return CUDNN_POOLING_MAX;
-    case PoolingMode::kAverage:
+    case PoolingMode::kAverageExclusive:
       return CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING;
+    case PoolingMode::kAverageInclusive:
+      return CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING;
     case PoolingMode::kMaximum:
       return CUDNN_POOLING_MAX;
     default:
@@ -105,8 +108,10 @@ inline cudnnPoolingMode_t GetPoolingMode(const PoolingMode& mode) {
   switch (mode) {
     case PoolingMode::kMaximumDeterministic:
       return CUDNN_POOLING_MAX_DETERMINISTIC;
-    case PoolingMode::kAverage:
+    case PoolingMode::kAverageExclusive:
       return CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING;
+    case PoolingMode::kAverageInclusive:
+      return CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING;
     case PoolingMode::kMaximum:
       return CUDNN_POOLING_MAX;
     default:
@@ -341,6 +346,28 @@ class ScopedPoolingDescriptor {
   DISABLE_COPY_AND_ASSIGN(ScopedPoolingDescriptor);
 };
 
+class ScopedSpatialTransformerDescriptor {
+ public:
+  ScopedSpatialTransformerDescriptor() {
+    PADDLE_ENFORCE(dynload::cudnnCreateSpatialTransformerDescriptor(&desc_));
+  }
+  ~ScopedSpatialTransformerDescriptor() {
+    PADDLE_ENFORCE(dynload::cudnnDestroySpatialTransformerDescriptor(desc_));
+  }
+
+  template <typename T>
+  inline cudnnSpatialTransformerDescriptor_t descriptor(const int nbDims,
+                                                        const int dimA[]) {
+    PADDLE_ENFORCE(dynload::cudnnSetSpatialTransformerNdDescriptor(
+        desc_, CUDNN_SAMPLER_BILINEAR, CudnnDataType<T>::type, nbDims, dimA));
+    return desc_;
+  }
+
+ private:
+  cudnnSpatialTransformerDescriptor_t desc_;
+  DISABLE_COPY_AND_ASSIGN(ScopedSpatialTransformerDescriptor);
+};
+
 inline bool CanCUDNNBeUsed(const framework::ExecutionContext& ctx) {
   bool use_cudnn = ctx.Attr<bool>("use_cudnn");
   use_cudnn &= paddle::platform::is_gpu_place(ctx.GetPlace());

paddle/fluid/platform/dynload/cudnn.h

@@ -90,6 +90,13 @@ extern void EnforceCUDNNLoaded(const char* fn_name);
   __macro(cudnnSetConvolutionNdDescriptor);          \
   __macro(cudnnGetConvolutionNdDescriptor);          \
   __macro(cudnnDeriveBNTensorDescriptor);            \
+  __macro(cudnnCreateSpatialTransformerDescriptor);  \
+  __macro(cudnnSetSpatialTransformerNdDescriptor);   \
+  __macro(cudnnDestroySpatialTransformerDescriptor); \
+  __macro(cudnnSpatialTfGridGeneratorForward);       \
+  __macro(cudnnSpatialTfGridGeneratorBackward);      \
+  __macro(cudnnSpatialTfSamplerForward);             \
+  __macro(cudnnSpatialTfSamplerBackward);            \
   __macro(cudnnCreate);                              \
   __macro(cudnnDestroy);                             \
   __macro(cudnnSetStream);                           \

paddle/fluid/platform/init.cc

@@ -116,21 +116,49 @@ void InitDevices(bool init_p2p, const std::vector<int> devices) {
   platform::SetNumThreads(FLAGS_paddle_num_threads);
 #endif
 
-  if (platform::jit::MayIUse(platform::jit::avx512f)) {
-#ifndef __AVX512F__
-    LOG(WARNING) << "AVX512F is available, Please re-compile on local machine";
+  if (platform::jit::MayIUse(platform::jit::avx)) {
+#ifndef __AVX__
+    LOG(WARNING) << "AVX is available, Please re-compile on local machine";
 #endif
   }
+
+// Throw some informations when CPU instructions mismatch.
+#define AVX_GUIDE(compiletime, runtime)                                     \
+  LOG(FATAL)                                                                \
+      << "This version is compiled on higher instruction(" #compiletime     \
+         ") system, you may encounter illegal instruction error running on" \
+         " your local CPU machine. Please reinstall the " #runtime          \
+         " version or compile from source code."
+
+#ifdef __AVX512F__
+  if (!platform::jit::MayIUse(platform::jit::avx512f)) {
     if (platform::jit::MayIUse(platform::jit::avx2)) {
-#ifndef __AVX2__
-    LOG(WARNING) << "AVX2 is available, Please re-compile on local machine";
-#endif
+      AVX_GUIDE(AVX512, AVX2);
+    } else if (platform::jit::MayIUse(platform::jit::avx)) {
+      AVX_GUIDE(AVX512, AVX);
+    } else {
+      AVX_GUIDE(AVX512, NonAVX);
     }
+  }
+#endif
+
+#ifdef __AVX2__
+  if (!platform::jit::MayIUse(platform::jit::avx2)) {
     if (platform::jit::MayIUse(platform::jit::avx)) {
-#ifndef __AVX__
-    LOG(WARNING) << "AVX is available, Please re-compile on local machine";
+      AVX_GUIDE(AVX2, AVX);
+    } else {
+      AVX_GUIDE(AVX2, NonAVX);
+    }
+  }
 #endif
+
+#ifdef __AVX__
+  if (!platform::jit::MayIUse(platform::jit::avx)) {
+    AVX_GUIDE(AVX, NonAVX);
   }
+#endif
+
+#undef AVX_GUIDE
 }
 
 void InitGLOG(const std::string &prog_name) {

paddle/fluid/platform/mkldnn_helper.h

@@ -187,6 +187,29 @@ class MKLDNNHandler {
     return mem_p;
   }
 
+  std::shared_ptr<mkldnn::memory> AcquireMemory(
+      const std::shared_ptr<mkldnn::memory>& user_memory_p,
+      const std::shared_ptr<mkldnn::memory>& target_memory_p,
+      const std::string& suffix,
+      std::vector<mkldnn::primitive>& pipeline) {  // NOLINT
+    auto local_key = key_ + suffix;
+    auto key_reorder_p = key_ + suffix + "reorder_p";
+
+    auto stored_reorder_p = std::static_pointer_cast<mkldnn::reorder>(
+        dev_ctx_.GetBlob(key_reorder_p));
+
+    if (stored_reorder_p) {
+      pipeline.push_back(*stored_reorder_p);
+    } else {
+      auto reorder_p =
+          std::make_shared<mkldnn::reorder>(*user_memory_p, *target_memory_p);
+      dev_ctx_.SetBlob(key_reorder_p, reorder_p);
+      pipeline.push_back(*reorder_p);
+    }
+
+    return target_memory_p;
+  }
+
   std::shared_ptr<mkldnn::memory> AcquireMemory(
       mkldnn::memory::primitive_desc& mpd,       // NOLINT
       mkldnn::memory::primitive_desc& user_mpd,  // NOLINT

paddle/fluid/pybind/pybind.cc

@@ -821,6 +821,13 @@ All parameter, weight, gradient are variables in Paddle.
           [](BuildStrategy &self, bool b) {
             self.enable_data_balance_ = b;
           })  // FIXME(chengudo): enable_data_balance seems not important
+      .def_property("enable_sequential_execution",
+                    [](const BuildStrategy &self) {
+                      return self.enable_sequential_execution_;
+                    },
+                    [](BuildStrategy &self, bool b) {
+                      self.enable_sequential_execution_ = b;
+                    })
       .def_property(
           "fuse_elewise_add_act_ops",
           [](const BuildStrategy &self) {

paddle/scripts/paddle_build.sh

@@ -147,7 +147,6 @@ function cmake_gen() {
         -DWITH_SWIG_PY=${WITH_SWIG_PY:-ON}
         -DCUDNN_ROOT=/usr/
         -DWITH_TESTING=${WITH_TESTING:-ON}
-        -DWITH_FAST_BUNDLE_TEST=ON
         -DCMAKE_MODULE_PATH=/opt/rocm/hip/cmake
         -DCMAKE_EXPORT_COMPILE_COMMANDS=ON
         -DWITH_FLUID_ONLY=${WITH_FLUID_ONLY:-OFF}
@@ -180,7 +179,6 @@ EOF
         -DWITH_PYTHON=${WITH_PYTHON:-ON} \
         -DCUDNN_ROOT=/usr/ \
         -DWITH_TESTING=${WITH_TESTING:-ON} \
-        -DWITH_FAST_BUNDLE_TEST=ON \
         -DCMAKE_MODULE_PATH=/opt/rocm/hip/cmake \
         -DWITH_FLUID_ONLY=${WITH_FLUID_ONLY:-OFF} \
         -DCMAKE_EXPORT_COMPILE_COMMANDS=ON \

python/paddle/fluid/io.py

@@ -884,12 +884,13 @@ def _load_slice_up_vars(executor, dirname, slice_vars_and_attrs):
 
     load_prog = Program()
     load_block = load_prog.global_block()
+    need_delete_vars = []
 
     for var_tuple in slice_vars_and_attrs:
         orig_var = var_tuple[0]
         start = var_tuple[1]
         slice_var = var_tuple[2]
-        end = start + reduce(lambda x, y: x * y, slice_var.shape)
+        end = start + slice_var.shape[0]
 
         clone_orig_var = load_block.create_var(
             name=orig_var.name,
@@ -917,5 +918,8 @@ def _load_slice_up_vars(executor, dirname, slice_vars_and_attrs):
             attrs={'axes': [0],
                    'starts': [start],
                    'ends': [end]})
-
+        need_delete_vars.append(clone_orig_var)
+    load_block.append_op(
+        type='delete_var',
+        inputs={'X': need_delete_vars}, )
     executor.run(load_prog)

python/paddle/fluid/layers/control_flow.py

@@ -1586,8 +1586,7 @@ class DynamicRNN(object):
         self.lod_rank_table = None
         self.max_seq_len = None
         self.step_idx = None
-        self.zero_idx = fill_constant(
-            shape=[1], value=0, dtype='int64', force_cpu=True)
+        self.zero_idx = None
         self.mem_dict = dict()
         self.output_array = []
         self.outputs = []
@@ -1792,6 +1791,7 @@ class DynamicRNN(object):
 
         """
         self._assert_in_rnn_block_('memory')
+        self._init_zero_idx_()
         if init is not None:
             if not isinstance(init, Variable):
                 raise TypeError(
@@ -1905,6 +1905,22 @@ class DynamicRNN(object):
             array_write(x=each, i=self.step_idx, array=outside_array)
             self.output_array.append(outside_array)
 
+    def _init_zero_idx_(self):
+        if self.zero_idx is None:
+            parent_block = self._parent_block_()
+            self.zero_idx = parent_block.create_var(
+                name=unique_name.generate('zero_idx'), dtype='int64')
+            parent_block.append_op(
+                type='fill_constant',
+                inputs={},
+                outputs={'Out': [self.zero_idx]},
+                attrs={
+                    'shape': [1],
+                    'dtype': self.zero_idx.dtype,
+                    'value': float(0),
+                    'force_cpu': True
+                })
+
     def _parent_block_(self):
         prog = self.helper.main_program
         parent_idx = prog.current_block().parent_idx

python/paddle/fluid/layers/io.py

@@ -315,6 +315,7 @@ def _copy_reader_var_(block, var):
     new_var = block.create_var(name=var.name, type=core.VarDesc.VarType.READER)
     new_var.desc.set_shapes(var.desc.shapes())
     new_var.desc.set_dtypes(var.desc.dtypes())
+    new_var.desc.set_lod_levels(var.desc.lod_levels())
     new_var.persistable = True
     return new_var
 

python/paddle/fluid/layers/nn.py

@@ -154,9 +154,11 @@ __all__ = [
     'mul',
     'sigmoid_cross_entropy_with_logits',
     'maxout',
+    'affine_grid',
     'sequence_reverse',
     'affine_channel',
     'hash',
+    'grid_sampler',
     'log_loss',
     'add_position_encoding',
 ]
@@ -710,8 +712,18 @@ def dynamic_gru(input,
               The first part are weights of the update gate and reset gate with
               shape :math:`(D \\times 2D)`, and the second part are weights for
               candidate hidden state with shape :math:`(D \\times D)`.
-        bias_attr(ParamAttr): The parameter attribute for learnable the
-            hidden-hidden bias.
+
+            If it is set to None or one attribute of ParamAttr, dynamic_gru 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 GRU. Note that the bias with :math:`(1 \\times 3D)` concatenates 
+            the bias in the update gate, reset gate and candidate calculations.
+            If it is set to False, no bias will be applied to the update gate, 
+            reset gate and candidate calculations. If it is set to None or one 
+            attribute of ParamAttr, dynamic_gru will create ParamAttr as 
+            bias_attr. If the Initializer of the bias_attr is not set, the bias
+            is initialized zero. Default: None.
         is_reverse(bool): Whether to compute reversed GRU, default
             :attr:`False`.
         gate_activation(str): The activation for update gate and reset gate.
@@ -810,10 +822,29 @@ def gru_unit(input,
 
     Args:
         input (Variable): The fc transformed input value of current step.
-        hidden (Variable): The hidden value of lstm unit from previous step.
+        hidden (Variable): The hidden value of gru unit from previous step.
         size (integer): The input dimension value.
-        param_attr (ParamAttr): The weight parameters for gru unit. Default: None
-        bias_attr (ParamAttr): The bias parameters for gru unit. Default: None
+        param_attr(ParamAttr|None): The parameter attribute for the learnable
+            hidden-hidden weight matrix. Note:
+
+            - The shape of the weight matrix is :math:`(T \\times 3D)`, where
+              :math:`D` is the hidden size.
+            - All elements in the weight matrix can be divided into two parts.
+              The first part are weights of the update gate and reset gate with
+              shape :math:`(D \\times 2D)`, and the second part are weights for
+              candidate hidden state with shape :math:`(D \\times D)`.
+
+            If it is set to None or one attribute of ParamAttr, gru_unit 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 GRU. Note that the bias with :math:`(1 \\times 3D)` concatenates 
+            the bias in the update gate, reset gate and candidate calculations.
+            If it is set to False, no bias will be applied to the update gate, 
+            reset gate and candidate calculations. If it is set to None or one 
+            attribute of ParamAttr, gru_unit will create ParamAttr as 
+            bias_attr. If the Initializer of the bias_attr is not set, the bias
+            is initialized zero. Default: None.
         activation (string): The activation type for cell (actNode).
                              Default: 'tanh'
         gate_activation (string): The activation type for gates (actGate).
@@ -2071,7 +2102,8 @@ def pool2d(input,
            global_pooling=False,
            use_cudnn=True,
            ceil_mode=False,
-           name=None):
+           name=None,
+           exclusive=True):
     """
     ${comment}
 
@@ -2085,11 +2117,13 @@ def pool2d(input,
         pool_type: ${pooling_type_comment}
         pool_stride (int): stride of the pooling layer.
         pool_padding (int): padding size.
-        global_pooling: ${global_pooling_comment}
-        use_cudnn: ${use_cudnn_comment}
-        ceil_mode: ${ceil_mode_comment}
+        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.
@@ -2147,7 +2181,8 @@ def pool2d(input,
             "paddings": pool_padding,
             "use_cudnn": use_cudnn,
             "ceil_mode": ceil_mode,
-            "use_mkldnn": False
+            "use_mkldnn": False,
+            "exclusive": exclusive,
         })
 
     return pool_out
@@ -2161,7 +2196,8 @@ def pool3d(input,
            global_pooling=False,
            use_cudnn=True,
            ceil_mode=False,
-           name=None):
+           name=None,
+           exclusive=True):
     """
     This function adds the operator for pooling in 3-dimensions, using the
     pooling configurations mentioned in input parameters.
@@ -2177,6 +2213,8 @@ def pool3d(input,
         ceil_mode (bool): ${ceil_mode_comment}
         name (str): 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: output of pool3d layer.
@@ -2215,7 +2253,8 @@ def pool3d(input,
             "paddings": pool_padding,
             "use_cudnn": use_cudnn,
             "ceil_mode": ceil_mode,
-            "use_mkldnn": False
+            "use_mkldnn": False,
+            "exclusive": exclusive,
         })
 
     return pool_out
@@ -4443,7 +4482,10 @@ def transpose(x, perm, name=None):
     Examples:
         .. code-block:: python
 
-            x = fluid.layers.data(name='x', shape=[5, 10, 15], dtype='float32')
+            # use append_batch_size=False to avoid prepending extra 
+            # batch size in shape
+            x = fluid.layers.data(name='x', shape=[5, 10, 15], 
+                            dtype='float32', append_batch_size=False)
             x_transposed = layers.transpose(x, perm=[1, 0, 2])
     """
 
@@ -4680,7 +4722,8 @@ def multiplex(inputs, index):
 def softmax_with_cross_entropy(logits,
                                label,
                                soft_label=False,
-                               ignore_index=-100):
+                               ignore_index=-100,
+                               numeric_stable_mode=False):
     """
     **Softmax With Cross Entropy Operator.**
 
@@ -4714,6 +4757,18 @@ def softmax_with_cross_entropy(logits,
         \\left(\\text{logit}_i - \\log\\left(\\sum_{i=0}^{K}
         \\exp(\\text{logit}_i)\\right)\\right), j = 1,...,K
 
+    3) If numeric_stable_mode is True, softmax is calculated first by:
+
+    .. math::
+        
+        max_j = \\max_{i=0}^{K}{\\text{logit}_i}
+
+        log\\_max\\_sum_j = \\log\\sum_{i=0}^{K}\\exp(logit_i - max_j)
+
+        softmax_j = \\exp(logit_j - max_j - {log\\_max\\_sum}_j)
+
+    and then cross entropy loss is calculated by softmax and label.
+
     Args:
         logits (Variable): The unscaled log probabilities, which is a 2-D tensor
             with shape [N x K]. N is the batch_size, and K is the class number.
@@ -4725,6 +4780,13 @@ def softmax_with_cross_entropy(logits,
         ignore_index (int): Specifies a target value that is ignored and does
                             not contribute to the input gradient. Only valid
                             if soft_label is set to False. Default: -100
+        numeric_stable_mode (bool): A flag to indicate whether to use a more
+                                    numerically stable algorithm. Only valid
+                                    when soft_label is False and GPU is used.
+                                    When soft_label is True or CPU is used, 
+                                    the algorithm is always numerically stable. 
+                                    Note that the speed may be slower when use 
+                                    stable algorithm. Default: False
 
     Returns:
         Variable: The cross entropy loss is a 2-D tensor with shape [N x 1].
@@ -4747,8 +4809,11 @@ def softmax_with_cross_entropy(logits,
                 'Label': label},
         outputs={'Softmax': softmax,
                  'Loss': loss},
-        attrs={'soft_label': soft_label,
-               'ignore_index': ignore_index})
+        attrs={
+            'soft_label': soft_label,
+            'ignore_index': ignore_index,
+            'numeric_stable_mode': numeric_stable_mode
+        })
     return loss
 
 
@@ -6108,6 +6173,124 @@ def crop(x, shape=None, offsets=None, name=None):
     return out
 
 
+def affine_grid(theta, out_shape, name=None):
+    """
+    It generates a grid of (x,y) coordinates using the parameters of
+    the affine transformation that correspond to a set of points where
+    the input feature map should be sampled to produce the transformed
+    output feature map.
+
+    .. code-block:: text
+
+        * Case 1:
+
+          Given:
+
+              theta = [[[x_11, x_12, x_13]
+                        [x_14, x_15, x_16]]
+                       [[x_21, x_22, x_23]
+                        [x_24, x_25, x_26]]]
+      
+              out_shape = [2, 3, 5, 5]
+      
+          Step 1:
+      
+              Generate normalized coordinates according to out_shape.
+              The values of the normalized coordinates are in the interval between -1 and 1.
+              The shape of the normalized coordinates is [2, H, W] as below:
+      
+              C = [[[-1.  -1.  -1.  -1.  -1. ]
+                    [-0.5 -0.5 -0.5 -0.5 -0.5]
+                    [ 0.   0.   0.   0.   0. ]
+                    [ 0.5  0.5  0.5  0.5  0.5]
+                    [ 1.   1.   1.   1.   1. ]]
+                   [[-1.  -0.5  0.   0.5  1. ]
+                    [-1.  -0.5  0.   0.5  1. ]
+                    [-1.  -0.5  0.   0.5  1. ]
+                    [-1.  -0.5  0.   0.5  1. ]
+                    [-1.  -0.5  0.   0.5  1. ]]]
+              C[0] is the coordinates in height axis and  C[1] is the coordinates in width axis.
+
+          Step2:
+
+              Tanspose and reshape C to shape [H * W, 2] and append ones to last dimension. The we get:
+              C_ = [[-1.  -1.   1. ]
+                    [-0.5 -1.   1. ]
+                    [ 0.  -1.   1. ]
+                    [ 0.5 -1.   1. ]
+                    [ 1.  -1.   1. ]
+                    [-1.  -0.5  1. ]
+                    [-0.5 -0.5  1. ]
+                    [ 0.  -0.5  1. ]
+                    [ 0.5 -0.5  1. ]
+                    [ 1.  -0.5  1. ]
+                    [-1.   0.   1. ]
+                    [-0.5  0.   1. ]
+                    [ 0.   0.   1. ]
+                    [ 0.5  0.   1. ]
+                    [ 1.   0.   1. ]
+                    [-1.   0.5  1. ]
+                    [-0.5  0.5  1. ]
+                    [ 0.   0.5  1. ]
+                    [ 0.5  0.5  1. ]
+                    [ 1.   0.5  1. ]
+                    [-1.   1.   1. ]
+                    [-0.5  1.   1. ]
+                    [ 0.   1.   1. ]
+                    [ 0.5  1.   1. ]
+                    [ 1.   1.   1. ]]
+          Step3:
+              Compute output by equation $$Output[i] = C_ * Theta[i]^T$$
+
+    Args:
+        theta (Variable): A batch of affine transform parameters with shape [N, 2, 3].
+        out_shape (Variable | list | tuple): The shape of target output with format [N, C, H, W].
+        out_shape can be a Variable or a list or tuple.
+        name(str|None): A name for this layer(optional). If set None, the layer
+                        will be named automatically.
+
+    Returns:
+        Variable: The output with shape [N, H, W, 2].
+
+    Raises:
+        ValueError: If the type of arguments is not supported.
+
+    Examples:
+
+        .. code-block:: python
+            theta = fluid.layers.data(name="x", shape=[2, 3], dtype="float32")
+            out_shape = fluid.layers.data(name="y", shape=[-1], dtype="float32")
+            data = fluid.layers.affine_grid(theta, out_shape)
+
+            # or
+            data = fluid.layers.affine_grid(theta, [5, 3, 28, 28])
+
+    """
+    helper = LayerHelper('affine_grid')
+
+    if not (isinstance(out_shape, list) or isinstance(out_shape, tuple) or \
+        isinstance(out_shape, Variable)):
+        raise ValueError("The out_shape should be a list, tuple or Variable.")
+
+    if not isinstance(theta, Variable):
+        raise ValueError("The theta should be a Variable.")
+
+    out = helper.create_variable_for_type_inference(theta.dtype)
+    ipts = {'Theta': theta}
+    attrs = {}
+    if isinstance(out_shape, Variable):
+        ipts['OutputShape'] = out_shape
+    else:
+        attrs['output_shape'] = out_shape
+
+    helper.append_op(
+        type='affine_grid',
+        inputs=ipts,
+        outputs={'Output': out},
+        attrs=None if len(attrs) == 0 else attrs)
+    return out
+
+
 def rank_loss(label, left, right, name=None):
     """
     **Rank loss layer for RankNet**
@@ -7322,10 +7505,10 @@ def clip(x, min, max, name=None):
     helper = LayerHelper("clip", **locals())
 
     if name is None:
-        out = helper.create_variable_for_type_inference(dtype=x.dtype)
-    else:
+        name = unique_name.generate(".".join([helper.name, 'tmp']))
+
     out = helper.create_variable(
-            name=name, dtype=x.dtype, persistable=False)
+        type=x.type, name=name, dtype=x.dtype, persistable=False)
 
     helper.append_op(
         type="clip",
@@ -7354,10 +7537,10 @@ def clip_by_norm(x, max_norm, name=None):
     helper = LayerHelper("clip_by_norm", **locals())
 
     if name is None:
-        out = helper.create_variable_for_type_inference(dtype=x.dtype)
-    else:
+        name = unique_name.generate(".".join([helper.name, 'tmp']))
+
     out = helper.create_variable(
-            name=name, dtype=x.dtype, persistable=False)
+        type=x.type, name=name, dtype=x.dtype, persistable=False)
 
     helper.append_op(
         type="clip_by_norm",
@@ -7561,19 +7744,59 @@ def affine_channel(x, scale=None, bias=None, data_layout='NCHW', name=None):
 
 def hash(input, hash_size, num_hash=1, name=None):
     """
-    hash the input
+    Hash the input to an integer whose value is less than the given hash size.
+
+    The hash algorithm we used was xxHash - Extremely fast hash algorithm
+    (https://github.com/Cyan4973/xxHash/tree/v0.6.5)
+
+    A simple example as below:
+
+    .. code-block:: text
+
+        Given:
+
+        # shape [2, 2]
+        input.data = [
+            [[1], [2]],
+            [[3], [4]],
+        ]
+
+        input.lod = [[0, 2]]
+
+        hash_size = 10000
+
+        num_hash = 4
+
+        Then:
+
+        Hash op will take all number in input's 2nd dimension as hash algorithm's
+        input for each time. Each input will be hashed for 4 times, and get an
+        array whose length is 4. Each value in the array ranges from 0 to 9999.
+
+        # shape [2, 4]
+        output.data = [
+            [[9662], [9217], [1129], [8487]],
+            [[8310], [1327], [1654], [4567]],
+        ]
+
+        output.lod = [[0, 2]]
+
     Args:
-        input (Variable): The input variable which is a one-hot word.
-        hash_size (int): The space size for hash algorithm.
+        input (Variable): The input variable which is a one-hot word. The
+            dimensions of the input variable must be 2.
+        hash_size (int): The space size for hash algorithm. The output value
+            will keep in the range:math:`[0, hash_size - 1]`.
         num_hash (int): The times of hash, default 1.
         name (str, default None): The name of this layer.
+
     Returns:
        Variable: The hash result variable which is a LoDTensor.
+
     Examples:
        .. code-block:: python
            word_dict = paddle.dataset.imdb.word_dict()
            x = fluid.layers.data(shape[1], dtype='int32', lod_level=1)
-            out = fluid.layers.hash(input=x, len(word_dict))
+           out = fluid.layers.hash(input=x, num_hash=4, hash_size=1000)
     """
     helper = LayerHelper('hash', **locals())
     out = helper.create_variable_for_type_inference(
@@ -7587,6 +7810,87 @@ def hash(input, hash_size, num_hash=1, name=None):
     return out
 
 
+@templatedoc()
+def grid_sampler(x, grid, name=None):
+    """
+    This operation samples input X by using bilinear interpolation based on 
+    flow field grid, which is usually gennerated by affine_grid. The grid of
+    shape [N, H, W, 2] is the concatenation of (grid_x, grid_y) coordinates 
+    with shape [N, H, W] each, where grid_x is indexing the 4th dimension 
+    (in width dimension) of input data x and grid_y is indexng the 3rd 
+    dimention (in height dimension), finally results is the bilinear 
+    interpolation value of 4 nearest corner points.
+
+    Step 1:
+    Get (x, y) grid coordinates and scale to [0, H-1/W-1].
+
+    grid_x = 0.5 * (grid[:, :, :, 0] + 1) * (W - 1)
+    grid_y = 0.5 * (grid[:, :, :, 1] + 1) * (H - 1)
+
+    Step 2:
+    Indices input data X with grid (x, y) in each [H, W] area, and bilinear 
+    interpolate point value by 4 nearest points.
+
+      wn ------- y_n ------- en
+      |           |           |
+      |          d_n          |
+      |           |           |
+     x_w --d_w-- grid--d_e-- x_e
+      |           |           |
+      |          d_s          |
+      |           |           |
+      ws ------- y_s ------- wn
+
+    x_w = floor(x)              // west side x coord
+    x_e = x_w + 1               // east side x coord
+    y_n = floor(y)              // north side y coord
+    y_s = y_s + 1               // south side y coord
+
+    d_w = grid_x - x_w          // distance to west side
+    d_e = x_e - grid_x          // distance to east side
+    d_n = grid_y - y_n          // distance to north side
+    d_s = y_s - grid_y          // distance to south side
+
+    wn = X[:, :, y_n, x_w]      // north-west point value
+    en = X[:, :, y_n, x_e]      // north-east point value
+    ws = X[:, :, y_s, x_w]      // south-east point value
+    es = X[:, :, y_s, x_w]      // north-east point value
+
+    output = wn * d_e * d_s + en * d_w * d_s
+           + ws * d_e * d_n + es * d_w * d_n
+
+    Args:
+        x(Variable): Input data of shape [N, C, H, W].
+        grid(Variable): Input grid tensor of shape [N, H, W, 2].
+        name (str, default None): The name of this layer.
+
+    Returns:
+        out(Variable): Output of shape [N, C, H, W] data samples input X 
+        using bilnear interpolation based on input grid.
+
+    Exmples:
+    .. code-block:: python
+
+        x = fluid.layers.data(name='x', shape=[3, 10, 32, 32], dtype='float32')
+        theta = fluid.layers.data(name='theta', shape=[3, 2, 3], dtype='float32')
+        grid = fluid.layers.affine_grid(input=theta, size=[3, 10, 32, 32]})
+        out = fluid.layers.grid_sampler(x=x, grid=grid)
+    """
+    helper = LayerHelper("grid_sampler", **locals())
+
+    if not isinstance(x, Variable):
+        return ValueError("The x should be a Variable")
+
+    if not isinstance(grid, Variable):
+        return ValueError("The grid should be a Variable")
+
+    out = helper.create_variable_for_type_inference(x.dtype)
+    ipts = {'X': x, 'Grid': grid}
+
+    helper.append_op(type='grid_sampler', inputs=ipts, outputs={'Output': out})
+    return out
+
+
 def log_loss(input, label, epsilon=1e-4, name=None):
     """
     **Negative Log Loss Layer**

python/paddle/fluid/tests/unittests/dist_save_load.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 os
+import sys
+import signal
+import subprocess
+import argparse
+import time
+import math
+import random
+from multiprocessing import Process
+from functools import reduce
+
+import numpy as np
+import unittest
+import six
+
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid import core
+from paddle.fluid import io
+
+from test_dist_base import TestDistRunnerBase, runtime_main, RUN_STEP
+from dist_simnet_bow import TestDistSimnetBow2x2, DATA_URL, DATA_MD5
+
+
+class TestDistSaveLoad2x2(TestDistSimnetBow2x2):
+    def _load_persistable_vars(self, executor, dirname, program):
+        def _is_checkpoint_var(var):
+            """
+            the checkpoint will not save or load all the variables.
+            var type is FEED_MINIBATCH/FETCH_LIST/RAW or var name ends with @GRAD are discarded.
+
+            : param var(Variable)
+            """
+            if var.desc.type() == core.VarDesc.VarType.FEED_MINIBATCH or \
+                    var.desc.type() == core.VarDesc.VarType.FETCH_LIST or \
+                    var.desc.type() == core.VarDesc.VarType.RAW:
+                return False
+            # @GRAD are named for gradient variables, checkpoint will not save it.
+            if "@GRAD" in var.name:
+                return False
+            # .trainer_ are named for distribute train variables, checkpoint will not save it.
+            if ".trainer_" in var.name:
+                return False
+
+            # .block is named for distribute train variables, checkpoint will not save it.
+            if ".block" in var.name:
+                return False
+
+            if "tmp_" in var.name:
+                return False
+
+            return var.persistable
+
+        io.load_vars(
+            executor,
+            dirname=dirname,
+            main_program=program,
+            predicate=_is_checkpoint_var,
+            filename=None)
+
+    def run_pserver(self, args):
+        self.get_model(batch_size=2)
+        # NOTE: pserver should not call memory optimize
+        t = self.get_transpiler(args.trainer_id,
+                                fluid.default_main_program(), args.endpoints,
+                                args.trainers, args.sync_mode)
+        pserver_prog = t.get_pserver_program(args.current_endpoint)
+        startup_prog = t.get_startup_program(args.current_endpoint,
+                                             pserver_prog)
+
+        need_load = bool(int(os.getenv("LOAD", "0")))
+        model_dir = os.getenv("MODEL_DIR", "")
+
+        place = fluid.CPUPlace()
+        exe = fluid.Executor(place)
+        exe.run(startup_prog)
+
+        if need_load and model_dir:
+            self._load_persistable_vars(exe, model_dir, startup_prog)
+        exe.run(pserver_prog)
+
+    def run_trainer(self, args):
+        test_program, avg_cost, train_reader, test_reader, batch_acc, predict = \
+            self.get_model(batch_size=2)
+
+        if args.mem_opt:
+            fluid.memory_optimize(fluid.default_main_program(), skip_grads=True)
+        if args.is_dist:
+            t = self.get_transpiler(args.trainer_id,
+                                    fluid.default_main_program(),
+                                    args.endpoints, args.trainers,
+                                    args.sync_mode)
+
+            trainer_prog = t.get_trainer_program()
+        else:
+            trainer_prog = fluid.default_main_program()
+
+        if args.use_cuda:
+            place = fluid.CUDAPlace(0)
+        else:
+            place = fluid.CPUPlace()
+
+        startup_exe = fluid.Executor(place)
+        startup_exe.run(fluid.default_startup_program())
+
+        strategy = fluid.ExecutionStrategy()
+        strategy.num_threads = 1
+        strategy.allow_op_delay = False
+
+        build_stra = fluid.BuildStrategy()
+
+        if args.use_reduce:
+            build_stra.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce
+        else:
+            build_stra.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.AllReduce
+
+        exe = fluid.ParallelExecutor(
+            args.use_cuda,
+            loss_name=avg_cost.name,
+            exec_strategy=strategy,
+            build_strategy=build_stra)
+
+        feed_var_list = [
+            var for var in trainer_prog.global_block().vars.values()
+            if var.is_data
+        ]
+
+        feeder = fluid.DataFeeder(feed_var_list, place)
+        reader_generator = train_reader()
+
+        def get_data():
+            origin_batch = next(reader_generator)
+            if args.is_dist and args.use_reader_alloc:
+                new_batch = []
+                for offset, item in enumerate(origin_batch):
+                    if offset % 2 == args.trainer_id:
+                        new_batch.append(item)
+                return new_batch
+            else:
+                return origin_batch
+
+        need_save = bool(int(os.getenv("SAVE", "0")))
+        model_dir = os.getenv("MODEL_DIR", "")
+
+        if need_save:
+            for _ in six.moves.xrange(RUN_STEP):
+                loss, = exe.run(fetch_list=[avg_cost.name],
+                                feed=feeder.feed(get_data()))
+            if need_save and model_dir:
+                io.save_persistables(startup_exe, model_dir, trainer_prog)
+
+        var = np.array(fluid.global_scope().find_var('__fc_b__').get_tensor())
+        print(np.ravel(var).tolist())
+
+
+if __name__ == "__main__":
+    paddle.dataset.common.download(DATA_URL, 'simnet', DATA_MD5, "train")
+    runtime_main(TestDistSaveLoad2x2)

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

@@ -40,7 +40,8 @@ class TestParallelExecutorBase(unittest.TestCase):
                                   use_reduce=False,
                                   fuse_elewise_add_act_ops=False,
                                   optimizer=fluid.optimizer.Adam,
-                                  use_fast_executor=False):
+                                  use_fast_executor=False,
+                                  enable_sequential_execution=False):
         def run_executor(exe, feed, fetch_list, program=None):
             if isinstance(exe, fluid.ParallelExecutor):
                 res = exe.run(fetch_list=fetch_list, feed=feed)
@@ -80,6 +81,7 @@ class TestParallelExecutorBase(unittest.TestCase):
             build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce \
                 if use_reduce else fluid.BuildStrategy.ReduceStrategy.AllReduce
             build_strategy.fuse_elewise_add_act_ops = fuse_elewise_add_act_ops
+            build_strategy.enable_sequential_execution = enable_sequential_execution
 
             if use_parallel_executor:
                 exe = fluid.ParallelExecutor(

python/paddle/fluid/tests/unittests/test_affine_grid_op.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.
+
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+def AffineGrid(theta, size):
+    n = size[0]
+    w = size[3]
+    h = size[2]
+    h_idx = np.repeat(
+        np.linspace(-1, 1, h)[np.newaxis, :], w, axis=0).T[:, :, np.newaxis]
+    w_idx = np.repeat(
+        np.linspace(-1, 1, w)[np.newaxis, :], h, axis=0)[:, :, np.newaxis]
+    grid = np.concatenate(
+        [w_idx, h_idx, np.ones([h, w, 1])], axis=2)  # h * w * 3
+    grid = np.repeat(grid[np.newaxis, :], size[0], axis=0)  # n * h * w *3
+
+    ret = np.zeros([n, h * w, 2])
+    theta = theta.transpose([0, 2, 1])
+    for i in range(len(theta)):
+        ret[i] = np.dot(grid[i].reshape([h * w, 3]), theta[i])
+
+#    print ret.reshape([h * w, 2]).astype("float32")    
+    return ret.reshape([n, h, w, 2]).astype("float32")
+
+
+class TestAffineGridOp(OpTest):
+    def setUp(self):
+        self.initTestCase()
+        self.op_type = "affine_grid"
+        theta = np.random.randint(1, 3, self.theta_shape).astype("float32")
+        theta = np.ones(self.theta_shape).astype("float32")
+        self.inputs = {'Theta': theta}
+        self.attrs = {"use_cudnn": True}
+        if self.dynamic_shape:
+            self.inputs['OutputShape'] = self.output_shape
+        else:
+            self.attrs['output_shape'] = self.output_shape
+        self.outputs = {'Output': AffineGrid(theta, self.output_shape)}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(
+            ['Theta'],
+            'Output',
+            no_grad_set=['OutputShape'],
+            max_relative_error=0.006)
+
+    def initTestCase(self):
+        self.theta_shape = (3, 2, 3)
+        self.output_shape = np.array([3, 2, 5, 7]).astype("int32")
+        self.dynamic_shape = False
+
+
+class TestAffineGridOpCase1(TestAffineGridOp):
+    def initTestCase(self):
+        self.theta_shape = (3, 2, 3)
+        self.output_shape = np.array([3, 2, 5, 7]).astype("int32")
+        self.dynamic_shape = True
+
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -37,10 +37,15 @@ class TestDistRunnerBase(object):
             "get_model should be implemented by child classes.")
 
     @staticmethod
-    def get_transpiler(trainer_id, main_program, pserver_endpoints, trainers,
-                       sync_mode):
+    def get_transpiler(trainer_id,
+                       main_program,
+                       pserver_endpoints,
+                       trainers,
+                       sync_mode,
+                       dc_asgd=False):
         # NOTE: import fluid until runtime, or else forking processes will cause error.
         config = fluid.DistributeTranspilerConfig()
+        config.enable_dc_asgd = dc_asgd
         t = fluid.DistributeTranspiler(config=config)
         t.transpile(
             trainer_id=trainer_id,
@@ -55,7 +60,7 @@ class TestDistRunnerBase(object):
         # NOTE: pserver should not call memory optimize
         t = self.get_transpiler(args.trainer_id,
                                 fluid.default_main_program(), args.endpoints,
-                                args.trainers, args.sync_mode)
+                                args.trainers, args.sync_mode, args.dc_asgd)
         pserver_prog = t.get_pserver_program(args.current_endpoint)
         startup_prog = t.get_startup_program(args.current_endpoint,
                                              pserver_prog)
@@ -75,8 +80,7 @@ class TestDistRunnerBase(object):
             t = self.get_transpiler(args.trainer_id,
                                     fluid.default_main_program(),
                                     args.endpoints, args.trainers,
-                                    args.sync_mode)
-
+                                    args.sync_mode, args.dc_asgd)
             trainer_prog = t.get_trainer_program()
         else:
             trainer_prog = fluid.default_main_program()
@@ -155,6 +159,7 @@ def runtime_main(test_class):
     parser.add_argument('--mem_opt', action='store_true')
     parser.add_argument('--use_cuda', action='store_true')
     parser.add_argument('--use_reduce', action='store_true')
+    parser.add_argument('--dc_asgd', action='store_true')
     parser.add_argument(
         '--use_reader_alloc', action='store_true', required=False)
     parser.add_argument('--batch_size', required=False, type=int, default=2)
@@ -200,6 +205,7 @@ class TestDistBase(unittest.TestCase):
         self._enforce_place = None
         self._mem_opt = False
         self._use_reduce = False
+        self._dc_asgd = False  # must use with async mode
         self._use_reader_alloc = True
         self._setup_config()
         self._after_setup_config()

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

@@ -53,6 +53,15 @@ class TestDistMnistAsync(TestDistBase):
         self.check_with_place("dist_mnist.py", delta=200)
 
 
+class TestDistMnistDcAsgd(TestDistBase):
+    def _setup_config(self):
+        self._sync_mode = False
+        self._dc_asgd = True
+
+    def test_se_resnext(self):
+        self.check_with_place("dist_mnist.py", delta=200)
+
+
 # FIXME(typhoonzero): enable these tests once we have 4
 # 4 GPUs on CI machine, and the base class should be updated.
 #

python/paddle/fluid/tests/unittests/test_dist_save_load.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 os
+import shutil
+import unittest
+import tempfile
+
+import numpy as np
+
+from test_dist_base import TestDistBase, RUN_STEP
+
+
+class TestDistSaveLoadDense2x2(TestDistBase):
+    def _setup_config(self):
+        self._sync_mode = True
+        self._enforce_place = "CPU"
+
+    def check_with_place(self,
+                         model_file,
+                         delta=1e-3,
+                         check_error_log=False,
+                         need_envs={}):
+
+        required_envs = {
+            "PATH": os.getenv("PATH", ""),
+            "PYTHONPATH": os.getenv("PYTHONPATH", ""),
+            "LD_LIBRARY_PATH": os.getenv("LD_LIBRARY_PATH", ""),
+            "http_proxy": ""
+        }
+
+        required_envs.update(need_envs)
+
+        if check_error_log:
+            required_envs["GLOG_v"] = "7"
+            required_envs["GLOG_logtostderr"] = "1"
+
+        model_dir = tempfile.mkdtemp()
+
+        local_env = {}
+        local_env["SAVE"] = "1"
+        local_env["MODEL_DIR"] = model_dir
+        local_env.update(required_envs)
+
+        cluster_env = {}
+        cluster_env["LOAD"] = "1"
+        cluster_env["MODEL_DIR"] = model_dir
+        cluster_env.update(required_envs)
+
+        local_var = self._run_local(model_file, local_env, check_error_log)
+        tr0_var, tr1_var = self._run_cluster(model_file, cluster_env,
+                                             check_error_log)
+
+        shutil.rmtree(model_dir)
+
+        local_np = np.array(eval(local_var[0]))
+        train0_np = np.array(eval(tr0_var[0]))
+        train1_np = np.array(eval(tr1_var[0]))
+        self.assertAlmostEqual(local_np.all(), train0_np.all(), delta=delta)
+        self.assertAlmostEqual(local_np.all(), train1_np.all(), delta=delta)
+        self.assertAlmostEqual(train0_np.all(), train1_np.all(), delta=delta)
+
+    @unittest.skip(reason="CI fail")
+    def test_dist(self):
+        need_envs = {
+            "IS_DISTRIBUTED": '0',
+            "IS_SPARSE": '0',
+            'IS_SELF_CONTAINED_LR': '1'
+        }
+        self.check_with_place(
+            "dist_save_load.py",
+            delta=0,
+            check_error_log=False,
+            need_envs=need_envs)
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/test_grid_sampler_op.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.
+
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+def AffineGrid(theta, size):
+    n = size[0]
+    h = size[2]
+    w = size[3]
+    h_idx = np.repeat(
+        np.linspace(-1, 1, h)[np.newaxis, :], w, axis=0).T[:, :, np.newaxis]
+    w_idx = np.repeat(
+        np.linspace(-1, 1, w)[np.newaxis, :], h, axis=0)[:, :, np.newaxis]
+    grid = np.concatenate(
+        [w_idx, h_idx, np.ones([h, w, 1])], axis=2)  # h * w * 3
+    grid = np.repeat(grid[np.newaxis, :], size[0], axis=0)  # n * h * w *3
+
+    ret = np.zeros([n, h * w, 2])
+    theta = theta.transpose([0, 2, 1])
+    for i in range(len(theta)):
+        ret[i] = np.dot(grid[i].reshape([h * w, 3]), theta[i])
+
+    return ret.reshape([n, h, w, 2]).astype("float32")
+
+
+def getGridPointValue(data, x, y):
+    data_shape = data.shape
+    N = data_shape[0]
+    H = data_shape[2]
+    W = data_shape[3]
+
+    out = np.zeros(data_shape, dtype='float')
+    for i in range(N):
+        for j in range(H):
+            for k in range(W):
+                if y[i, j, k] < 0 or y[i, j, k] > H - 1 or x[i, j, k] < 0 or x[
+                        i, j, k] > W - 1:
+                    out[i, :, j, k] = 0
+                else:
+                    out[i, :, j, k] = data[i, :, y[i, j, k], x[i, j, k]]
+
+    return out
+
+
+def GridSampler(data, grid):
+    dims = data.shape
+    N = dims[0]
+    C = dims[1]
+    H = dims[2]
+    W = dims[3]
+
+    x = grid[:, :, :, 0]
+    y = grid[:, :, :, 1]
+    y_max = H - 1
+    x_max = W - 1
+
+    x = 0.5 * ((x.astype('float32') + 1.0) * x_max)
+    y = 0.5 * ((y.astype('float32') + 1.0) * y_max)
+
+    x0 = np.floor(x).astype('int32')
+    x1 = x0 + 1
+    y0 = np.floor(y).astype('int32')
+    y1 = y0 + 1
+
+    wa = np.tile(((x1 - x) * (y1 - y)).reshape((N, 1, H, W)), (1, C, 1, 1))
+    wb = np.tile(((x1 - x) * (y - y0)).reshape((N, 1, H, W)), (1, C, 1, 1))
+    wc = np.tile(((x - x0) * (y1 - y)).reshape((N, 1, H, W)), (1, C, 1, 1))
+    wd = np.tile(((x - x0) * (y - y0)).reshape((N, 1, H, W)), (1, C, 1, 1))
+
+    va = getGridPointValue(data, x0, y0)
+    vb = getGridPointValue(data, x0, y1)
+    vc = getGridPointValue(data, x1, y0)
+    vd = getGridPointValue(data, x1, y1)
+
+    out = (wa * va + wb * vb + wc * vc + wd * vd).astype('float32')
+    return out
+
+
+class TestGridSamplerOp(OpTest):
+    def setUp(self):
+        self.initTestCase()
+        self.op_type = 'grid_sampler'
+        x = np.random.randint(0, 255, self.x_shape).astype('float32')
+
+        theta = np.zeros(self.theta_shape).astype('float32')
+        for i in range(self.theta_shape[0]):
+            for j in range(2):
+                for k in range(3):
+                    theta[i, j, k] = np.random.rand(1)[0]
+        grid = AffineGrid(theta, self.x_shape)
+
+        self.inputs = {'X': x, 'Grid': grid}
+        self.attrs = {'use_cudnn': True}
+        self.outputs = {'Output': GridSampler(x, grid)}
+
+    def test_check_output(self):
+        self.check_output(atol=1e-3)
+
+    def test_check_grad_normal(self):
+        self.check_grad(['X', 'Grid'], 'Output', max_relative_error=0.61)
+
+    def initTestCase(self):
+        self.x_shape = (2, 5, 7, 3)
+        self.grid_shape = (2, 7, 3, 2)
+        self.theta_shape = (2, 2, 3)
+
+
+if __name__ == "__main__":
+    unittest.main()

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

@@ -865,6 +865,31 @@ class TestBook(unittest.TestCase):
             self.assertIsNotNone(out)
         print(str(program))
 
+    def test_grid_sampler(self):
+        program = Program()
+        with program_guard(program):
+            x = layers.data(name='x', shape=[3, 5, 7], dtype='float32')
+            grid = layers.data(name='grid', shape=[5, 7, 2], dtype='float32')
+            out = layers.grid_sampler(x, grid)
+            self.assertIsNotNone(out)
+        print(str(program))
+
+    def test_affine_grid(self):
+        program = Program()
+        with program_guard(program):
+            data = layers.data(name='data', shape=[2, 3, 3], dtype="float32")
+            out, ids = layers.argsort(input=data, axis=1)
+
+            theta = layers.data(name="theta", shape=[2, 3], dtype="float32")
+            out_shape = layers.data(
+                name="out_shape", shape=[-1], dtype="float32")
+            data_0 = layers.affine_grid(theta, out_shape)
+            data_1 = layers.affine_grid(theta, [5, 3, 28, 28])
+
+            self.assertIsNotNone(data_0)
+            self.assertIsNotNone(data_1)
+        print(str(program))
+
 
 if __name__ == '__main__':
     unittest.main()

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

@@ -232,6 +232,46 @@ class TestResnet(TestParallelExecutorBase):
         for loss in zip(all_reduce_last_loss, reduce_last_loss):
             self.assertAlmostEquals(loss[0], loss[1], delta=delta2)
 
+        if not use_cuda:
+            return
+
+        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,
+            use_cuda=use_cuda,
+            use_reduce=False,
+            optimizer=optimizer,
+            enable_sequential_execution=True)
+
+        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,
+            use_cuda=use_cuda,
+            use_reduce=True,
+            optimizer=optimizer,
+            enable_sequential_execution=True)
+
+        for loss in zip(all_reduce_first_loss, all_reduce_first_loss_seq):
+            self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
+        for loss in zip(all_reduce_last_loss, all_reduce_last_loss_seq):
+            self.assertAlmostEquals(loss[0], loss[1], delta=delta2)
+
+        for loss in zip(reduce_first_loss, reduce_first_loss_seq):
+            self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
+        for loss in zip(reduce_last_loss, reduce_last_loss_seq):
+            self.assertAlmostEquals(loss[0], loss[1], delta=delta2)
+
+        for loss in zip(all_reduce_first_loss_seq, reduce_first_loss_seq):
+            self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
+        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,
                                   use_cuda=True,

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

@@ -173,6 +173,8 @@ class TestTransformer(TestParallelExecutorBase):
     def test_main(self):
         if core.is_compiled_with_cuda():
             self.check_network_convergence(transformer, use_cuda=True)
+            self.check_network_convergence(
+                transformer, use_cuda=True, enable_sequential_execution=True)
         self.check_network_convergence(transformer, use_cuda=False, iter=5)
 
 

python/paddle/fluid/tests/unittests/test_pool2d_op.py

@@ -26,7 +26,8 @@ def max_pool2D_forward_naive(x,
                              strides,
                              paddings,
                              global_pool=0,
-                             ceil_mode=False):
+                             ceil_mode=False,
+                             exclusive=True):
     N, C, H, W = x.shape
     if global_pool == 1:
         ksize = [H, W]
@@ -54,7 +55,8 @@ def avg_pool2D_forward_naive(x,
                              strides,
                              paddings,
                              global_pool=0,
-                             ceil_mode=False):
+                             ceil_mode=False,
+                             exclusive=True):
     N, C, H, W = x.shape
     if global_pool == 1:
         ksize = [H, W]
@@ -73,8 +75,9 @@ def avg_pool2D_forward_naive(x,
             c_end = np.min((j * strides[1] + ksize[1] - paddings[1], W))
             x_masked = x[:, :, r_start:r_end, c_start:c_end]
 
-            out[:, :, i, j] = np.sum(x_masked, axis=(2, 3)) / (
-                (r_end - r_start) * (c_end - c_start))
+            field_size = ((r_end - r_start) * (c_end - c_start)) if exclusive \
+                            else (ksize[0] * ksize[1])
+            out[:, :, i, j] = np.sum(x_masked, axis=(2, 3)) / field_size
     return out
 
 
@@ -89,12 +92,13 @@ class TestPool2d_Op(OpTest):
         self.init_kernel_type()
         self.init_pool_type()
         self.init_ceil_mode()
+        self.init_exclusive()
         if self.global_pool:
             self.paddings = [0 for _ in range(len(self.paddings))]
         input = np.random.random(self.shape).astype(self.dtype)
-        output = self.pool2D_forward_naive(input, self.ksize, self.strides,
-                                           self.paddings, self.global_pool,
-                                           self.ceil_mode).astype(self.dtype)
+        output = self.pool2D_forward_naive(
+            input, self.ksize, self.strides, self.paddings, self.global_pool,
+            self.ceil_mode, self.exclusive).astype(self.dtype)
         self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(input)}
 
         self.attrs = {
@@ -106,7 +110,9 @@ class TestPool2d_Op(OpTest):
             'use_cudnn': self.use_cudnn,
             'use_mkldnn': self.use_mkldnn,
             'ceil_mode': self.ceil_mode,
-            'data_format': 'AnyLayout'  # TODO(dzhwinter) : should be fix latter
+            'data_format':
+            'AnyLayout',  # TODO(dzhwinter) : should be fix latter
+            'exclusive': self.exclusive
         }
 
         self.outputs = {'Out': output}
@@ -150,6 +156,9 @@ class TestPool2d_Op(OpTest):
     def init_ceil_mode(self):
         self.ceil_mode = False
 
+    def init_exclusive(self):
+        self.exclusive = True
+
 
 class TestCase1(TestPool2d_Op):
     def init_test_case(self):
@@ -322,5 +331,15 @@ class TestCeilModeCase4(TestCase2):
         self.ceil_mode = True
 
 
+class TestAvgInclude(TestCase2):
+    def init_exclusive(self):
+        self.exclusive = False
+
+
+class TestCUDNNAvgInclude(TestCUDNNCase3):
+    def init_exclusive(self):
+        self.exclusive = False
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/test_pool3d_op.py

@@ -26,7 +26,8 @@ def max_pool3D_forward_naive(x,
                              strides,
                              paddings,
                              global_pool=0,
-                             ceil_mode=False):
+                             ceil_mode=False,
+                             exclusive=True):
     N, C, D, H, W = x.shape
     if global_pool == 1:
         ksize = [D, H, W]
@@ -60,7 +61,8 @@ def avg_pool3D_forward_naive(x,
                              strides,
                              paddings,
                              global_pool=0,
-                             ceil_mode=False):
+                             ceil_mode=False,
+                             exclusive=True):
     N, C, D, H, W = x.shape
     if global_pool == 1:
         ksize = [D, H, W]
@@ -85,8 +87,10 @@ def avg_pool3D_forward_naive(x,
                 w_end = np.min((j * strides[1] + ksize[1] - paddings[1], W))
                 x_masked = x[:, :, d_start:d_end, h_start:h_end, w_start:w_end]
 
-                out[:, :, k, i, j] = np.sum(x_masked, axis=(2, 3, 4)) / (
-                    (d_end - d_start) * (h_end - h_start) * (w_end - w_start))
+                field_size = (d_end - d_start) * (h_end - h_start) * (w_end - w_start) \
+                             if exclusive else ksize[0] * ksize[1] * ksize[2]
+                out[:, :, k, i, j] = np.sum(x_masked, axis=(2, 3,
+                                                            4)) / field_size
     return out
 
 
@@ -100,13 +104,14 @@ class TestPool3d_Op(OpTest):
         self.init_kernel_type()
         self.init_pool_type()
         self.init_ceil_mode()
+        self.init_exclusive()
 
         if self.global_pool:
             self.paddings = [0 for _ in range(len(self.paddings))]
         input = np.random.random(self.shape).astype(self.dtype)
-        output = self.pool3D_forward_naive(input, self.ksize, self.strides,
-                                           self.paddings, self.global_pool,
-                                           self.ceil_mode).astype(self.dtype)
+        output = self.pool3D_forward_naive(
+            input, self.ksize, self.strides, self.paddings, self.global_pool,
+            self.ceil_mode, self.exclusive).astype(self.dtype)
         self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(input)}
 
         self.attrs = {
@@ -117,7 +122,9 @@ class TestPool3d_Op(OpTest):
             'global_pooling': self.global_pool,
             'use_cudnn': self.use_cudnn,
             'ceil_mode': self.ceil_mode,
-            'data_format': 'AnyLayout'  # TODO(dzhwinter) : should be fix latter
+            'data_format':
+            'AnyLayout',  # TODO(dzhwinter) : should be fix latter
+            'exclusive': self.exclusive
         }
 
         self.outputs = {'Out': output}
@@ -161,6 +168,9 @@ class TestPool3d_Op(OpTest):
     def init_ceil_mode(self):
         self.ceil_mode = False
 
+    def init_exclusive(self):
+        self.exclusive = True
+
 
 class TestCase1(TestPool3d_Op):
     def init_test_case(self):
@@ -333,5 +343,15 @@ class TestCeilModeCase4(TestCase2):
         self.ceil_mode = True
 
 
+class TestAvgInclude(TestCase2):
+    def init_exclusive(self):
+        self.exclusive = False
+
+
+class TestCUDNNAvgInclude(TestCUDNNCase3):
+    def init_exclusive(self):
+        self.exclusive = False
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/test_py_reader_lod_level_share.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.
+
+import paddle.fluid as fluid
+import unittest
+
+
+class TestLoDLevelShare(unittest.TestCase):
+    def setUp(self):
+        self.use_double_buffer = False
+
+    def test_lod_level_share(self):
+        reader = fluid.layers.py_reader(
+            capacity=16,
+            shapes=([-1, 256], [-1, 512], [-1, 100]),
+            dtypes=('float32', 'int64', 'double'),
+            lod_levels=(1, 2, 0),
+            use_double_buffer=self.use_double_buffer)
+
+        x, y, z = fluid.layers.read_file(reader)
+        self.assertEqual(x.lod_level, 1)
+        self.assertEqual(y.lod_level, 2)
+        self.assertEqual(z.lod_level, 0)
+
+
+class TestLoDLevelShare2(TestLoDLevelShare):
+    def setUp(self):
+        self.use_double_buffer = True
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/test_py_reader_pin_memory.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 paddle
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+import numpy as np
+from threading import Thread
+
+
+def user_reader(inputs):
+    def _reader():
+        for d in inputs:
+            yield d
+
+    return _reader
+
+
+def batch_feeder(batch_reader, pin_memory=False, img_dtype="float32"):
+    def _feeder():
+        for batch_data in batch_reader():
+            sample_batch = []
+            label_batch = []
+            for sample, label in batch_data:
+                sample_batch.append(sample)
+                label_batch.append([label])
+            tensor = core.LoDTensor()
+            label = core.LoDTensor()
+            place = core.CUDAPinnedPlace() if pin_memory else core.CPUPlace()
+            tensor.set(np.array(sample_batch, dtype=img_dtype), place)
+            label.set(np.array(label_batch, dtype="int64"), place)
+            yield [tensor, label]
+
+    return _feeder
+
+
+class TestPyReader(unittest.TestCase):
+    def setUp(self):
+        self.capacity = 10
+        self.shapes = [(-1, 3, 2, 1), (-1, 1)]
+        self.lod_levels = [0, 0]
+        self.dtypes = ['float32', 'int64']
+
+    def test_pin_memory_pyreader(self):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
+            ) else fluid.CPUPlace()
+            executor = fluid.Executor(place)
+
+            data_file = fluid.layers.py_reader(
+                capacity=self.capacity,
+                dtypes=self.dtypes,
+                lod_levels=self.lod_levels,
+                shapes=self.shapes)
+            # feed_queue = data_file.queue
+            read_out_data = fluid.layers.read_file(data_file)
+
+            self.inputs = []
+            for _ in range(10):
+                sample = np.random.uniform(
+                    low=0, high=1, size=[3, 2, 1]).astype("float32")
+                label = np.random.uniform(
+                    low=0, high=10, size=[1]).astype("int64")
+                self.inputs.append((sample, label))
+
+            self.input_tensors = []
+            for d, l in batch_feeder(
+                    paddle.batch(
+                        user_reader(self.inputs), batch_size=2),
+                    pin_memory=True
+                    if fluid.core.is_compiled_with_cuda() else False)():
+                ta = fluid.LoDTensorArray()
+                ta.append(d)
+                ta.append(l)
+                self.input_tensors.append(ta)
+
+            self.batched_inputs = []
+            for batch in paddle.batch(user_reader(self.inputs), batch_size=2)():
+                feed_d = []
+                feed_l = []
+                for d, l in batch:
+                    feed_d.append(d)
+                    feed_l.append([l])
+                self.batched_inputs.append([feed_d, feed_l])
+
+            data_file.decorate_tensor_provider(
+                batch_feeder(
+                    paddle.batch(
+                        user_reader(self.inputs), batch_size=2),
+                    pin_memory=True
+                    if fluid.core.is_compiled_with_cuda() else False))
+
+            executor.run(fluid.default_startup_program())
+            self.outputs = []
+
+            data_file.start()
+            for _ in self.input_tensors:
+                self.outputs.append(
+                    executor.run(fetch_list=list(read_out_data)))
+            data_file.reset()
+            self.validate()
+
+    def validate(self):
+        self.assertEqual(len(self.batched_inputs), len(self.outputs))
+        for in_data_list, out_data_list in zip(self.batched_inputs,
+                                               self.outputs):
+            self.assertEqual(len(in_data_list), len(out_data_list))
+            in_data_list_np = [
+                np.array(in_lod_tensor) for in_lod_tensor in in_data_list
+            ]
+            for in_data, out_data in zip(in_data_list_np, out_data_list):
+                self.assertTrue((in_data == out_data).all())
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/test_ref_by_trainer_id_op.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.
+
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestRefByTrainerIdOp(OpTest):
+    def setUp(self):
+        self.op_type = "ref_by_trainer_id"
+        param_baks = [("x%d" % x, np.random.random((10, 10)).astype("float32"))
+                      for x in range(10)]
+        self.inputs = {
+            'X': param_baks,
+            'TrainerId': np.array([8]).astype("int64")
+        }
+        self.outputs = {'Out': param_baks[8][1]}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+if __name__ == "__main__":
+    unittest.main()

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

@@ -26,7 +26,11 @@ class TestSoftmaxWithCrossEntropyOp(OpTest):
     Test softmax with cross entropy operator with discreate one-hot labels.
     """
 
+    def initParams(self):
+        self.numeric_stable_mode = False
+
     def setUp(self):
+        self.initParams()
         self.op_type = "softmax_with_cross_entropy"
         batch_size = 41
         class_num = 37
@@ -46,6 +50,7 @@ class TestSoftmaxWithCrossEntropyOp(OpTest):
             "Softmax": softmax.astype("float64"),
             "Loss": cross_entropy.astype("float64")
         }
+        self.attrs = {"numeric_stable_mode": self.numeric_stable_mode}
 
     def test_check_output(self):
         self.check_output()
@@ -54,6 +59,11 @@ class TestSoftmaxWithCrossEntropyOp(OpTest):
         self.check_grad(["Logits"], "Loss")
 
 
+class TestSoftmaxWithCrossEntropyOpNoCudnn(TestSoftmaxWithCrossEntropyOp):
+    def initParams(self):
+        self.numeric_stable_mode = True
+
+
 class TestSoftmaxWithCrossEntropyOp2(OpTest):
     """
     Test softmax with cross entropy operator with soft labels.
@@ -93,7 +103,11 @@ class TestSoftmaxWithCrossEntropyOp3(OpTest):
     Test softmax with cross entropy operator with ignore_index.
     """
 
+    def initParams(self):
+        self.numeric_stable_mode = False
+
     def setUp(self):
+        self.initParams()
         self.op_type = "softmax_with_cross_entropy"
         batch_size = 41
         class_num = 37
@@ -114,7 +128,10 @@ class TestSoftmaxWithCrossEntropyOp3(OpTest):
             "Softmax": softmax.astype("float64"),
             "Loss": cross_entropy.astype("float64")
         }
-        self.attrs = {"ignore_index": ignore_index}
+        self.attrs = {
+            "ignore_index": ignore_index,
+            "numeric_stable_mode": self.numeric_stable_mode
+        }
 
     def test_check_output(self):
         self.check_output()
@@ -123,5 +140,10 @@ class TestSoftmaxWithCrossEntropyOp3(OpTest):
         self.check_grad(["Logits"], "Loss")
 
 
+class TestSoftmaxWithCrossEntropyOp3NoCudnn(TestSoftmaxWithCrossEntropyOp3):
+    def initParams(self):
+        self.numeric_stable_mode = True
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/transpiler/distribute_transpiler.py

@@ -38,7 +38,7 @@ import six
 import logging
 
 from .ps_dispatcher import RoundRobin, HashName, PSDispatcher
-from .. import core, framework
+from .. import core, framework, unique_name
 from ..framework import Program, default_main_program, \
     default_startup_program, Block, \
     Parameter, grad_var_name
@@ -138,6 +138,7 @@ class DistributeTranspilerConfig(object):
     slice_var_up = True
     split_method = None
     min_block_size = 8192
+    enable_dc_asgd = False
     # supported modes: pserver, nccl2
     mode = "pserver"
     print_log = False
@@ -252,6 +253,8 @@ class DistributeTranspiler(object):
                 n workers, the id may range from 0 ~ n-1
             program (Program|None): program to transpile,
                 default is fluid.default_main_program().
+            startup_program (Program|None): startup_program to transpile,
+                default is fluid.default_startup_program().
             pservers (str): comma separated ip:port string for the pserver
                 list.
             trainers (int|str): in pserver mode this is the number of
@@ -383,6 +386,8 @@ class DistributeTranspiler(object):
                 outputs={"Out": send_barrier_out},
                 attrs={
                     "endpoints": pserver_endpoints,
+                    "sync_mode": self.sync_mode,
+                    "trainer_id": self.trainer_id,
                     RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
                 })
 
@@ -426,6 +431,7 @@ class DistributeTranspiler(object):
                 outputs={"Out": splited_var},
                 attrs={
                     "epmap": eps,
+                    "trainer_id": self.trainer_id,
                     RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE,
                     OP_ROLE_VAR_ATTR_NAME:
                     [param_varname, recv_op_role_var_name],
@@ -440,6 +446,7 @@ class DistributeTranspiler(object):
                 outputs={"Out": all_recv_outputs},
                 attrs={
                     "endpoints": pserver_endpoints,
+                    "trainer_id": self.trainer_id,
                     RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
                 })
 
@@ -651,6 +658,24 @@ in a single call.")
                     endpoint, op):
                 opt_op_on_pserver.append(op)
         # step 3.3
+        # prepare if dc asgd is enabled
+        if self.config.enable_dc_asgd == True:
+            assert (self.sync_mode == False)
+            self.param_bak_list = []
+            # add param_bak for each trainer
+            for p in self.param_grad_ep_mapping[endpoint]["params"]:
+                # each parameter should have w_bak for each trainer id
+                for i in range(self.trainer_num):
+                    param_bak_name = "%s.trainer_%d_bak" % (p.name, i)
+                    tmpvar = pserver_program.global_block().create_var(
+                        # NOTE: this var name format is used in `request_get_handler`
+                        name=param_bak_name,
+                        type=p.type,
+                        shape=p.shape,
+                        dtype=p.dtype)
+                    self.param_bak_list.append((p, tmpvar))
+
+        # step 3.4
         # Iterate through the ops, and if an op and the optimize ops
         # which located on current pserver are in one set, then
         # append it into the sub program.
@@ -741,7 +766,7 @@ in a single call.")
                                                grad_to_block_id, merged_var,
                                                lr_ops)
 
-        # dedup grad to ids list
+# dedup grad to ids list
         grad_to_block_id = list(set(grad_to_block_id))
         # append global ops
         if global_ops:
@@ -787,6 +812,8 @@ in a single call.")
 
         if self.has_distributed_lookup_table:
             attrs['checkpint_block_id'] = checkpoint_block_id
+        if self.config.enable_dc_asgd:
+            attrs['dc_asgd'] = True
 
         if len(prefetch_var_name_to_block_id) > 0:
             attrs[
@@ -903,6 +930,15 @@ to transpile() call.")
                     inputs=new_inputs,
                     outputs=new_outputs,
                     attrs=op.all_attrs())
+        if self.config.enable_dc_asgd:
+            for p, p_bak in self.param_bak_list:
+                startup_param_var = s_prog.global_block().vars[p.name]
+                startup_tmpvar = s_prog.global_block().vars[p_bak.name]
+                # copy init random value to param_bak
+                s_prog.global_block().append_op(
+                    type="assign",
+                    inputs={"X": startup_param_var},
+                    outputs={"Out": startup_tmpvar})
 
         # add slice vars
         s_prog._slice_vars_and_attrs = self._get_slice_vars_and_attrs(endpoint)
@@ -920,11 +956,11 @@ to transpile() call.")
             block_idx = int(block_name.split(block_suffix)[1])
             orig_var = self.origin_program.global_block().vars[orig_var_name]
 
-            skip_numel = 0
+            skip_dim0 = 0
             slice_vars = self.param_var_mapping[orig_var_name]
             for slice_var in slice_vars[:block_idx]:
-                skip_numel += reduce(lambda x, y: x * y, slice_var.shape)
-            slice_vars_and_attrs.append([orig_var, skip_numel, param])
+                skip_dim0 += slice_var.shape[0]
+            slice_vars_and_attrs.append([orig_var, skip_dim0, param])
 
         return slice_vars_and_attrs
 
@@ -1175,6 +1211,7 @@ to transpile() call.")
                     attrs={
                         "sync_mode": not self.sync_mode,
                         "epmap": pserver_endpoints,
+                        "trainer_id": self.trainer_id,
                         RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE,
                         OP_ROLE_VAR_ATTR_NAME: [
                             self.grad_name_to_param_name[table_grad_name],
@@ -1531,6 +1568,69 @@ to transpile() call.")
                 attrs={"scale": 1.0 / float(self.trainer_num)})
         return merged_var
 
+    def _append_dc_asgd_ops(self, block, param_var, grad_var):
+        # NOTE: can not use grammar candy here, should put ops in specific block
+        local_param_bak = block.create_var(
+            name="%s.local_bak" % param_var.name,
+            shape=param_var.shape,
+            type=param_var.type,
+            dtype=param_var.dtype,
+            persistable=False)
+        # trainer_id_var is block local
+        trainer_id_var = block.create_var(
+            name="@TRAINER_ID@",
+            type=core.VarDesc.VarType.LOD_TENSOR,
+            dtype=core.VarDesc.VarType.INT64,
+            shape=[1],
+            persistable=False)
+
+        # ref_inputs = [x[1] for x in self.param_bak_list]
+        ref_inputs = []
+        for p, p_bak in self.param_bak_list:
+            if p.name == param_var.name:
+                print("#### ref inputs: ", param_var.name, p_bak.name)
+                ref_inputs.append(p_bak)
+        block.append_op(
+            type="ref_by_trainer_id",
+            inputs={"X": ref_inputs,
+                    "TrainerId": trainer_id_var},
+            outputs={"Out": local_param_bak})
+
+        def __create_temp_var__():
+            return block.create_var(
+                name=unique_name.generate("tmp_dc_output"),
+                shape=param_var.shape,
+                type=param_var.type,
+                dtype=param_var.dtype,
+                persistable=False)
+
+        o1 = __create_temp_var__()
+        block.append_op(
+            type="elementwise_sub",
+            inputs={"X": param_var,
+                    "Y": local_param_bak},
+            outputs={"Out": o1})
+        o2 = __create_temp_var__()
+        block.append_op(
+            type="elementwise_mul",
+            inputs={"X": o1,
+                    "Y": grad_var},
+            outputs={"Out": o2})
+        o3 = __create_temp_var__()
+        block.append_op(
+            type="elementwise_mul",
+            inputs={"X": o2,
+                    "Y": grad_var},
+            outputs={"Out": o3})
+        # TODO(typhoonzero): append scale
+        o4 = __create_temp_var__()
+        block.append_op(
+            type="elementwise_add",
+            inputs={"X": grad_var,
+                    "Y": o3},
+            outputs={"Out": o4})
+        return o4
+
     def _append_pserver_ops(self, optimize_block, opt_op, endpoint,
                             grad_to_block_id, origin_program, merged_var):
         program = optimize_block.program
@@ -1546,8 +1646,15 @@ to transpile() call.")
                     break
             return param_block
 
+        if self.config.enable_dc_asgd:
+            param_var = _get_param_block(opt_op)
+            dc = self._append_dc_asgd_ops(optimize_block, param_var, merged_var)
+
         for key in opt_op.input_names:
             if key == "Grad":
+                if self.config.enable_dc_asgd:
+                    new_inputs[key] = dc
+                else:
                     new_inputs[key] = merged_var
             elif key == "Param":
                 param_block = _get_param_block(opt_op)

python/paddle/fluid/transpiler/inference_transpiler.py

@@ -61,6 +61,9 @@ class InferenceTranspiler(object):
             raise TypeError("scope should be as Scope type or None")
         use_mkldnn = bool(os.getenv("FLAGS_use_mkldnn", False))
 
+        if use_mkldnn:
+            self._depthwise_conv_mkldnn(program)
+
         self._fuse_batch_norm(program, place, scope)
         if use_mkldnn:
             self._fuse_conv_bias_mkldnn(program)
@@ -70,6 +73,31 @@ class InferenceTranspiler(object):
                 program)  # ResNet residual block merging
             self._fuse_bn_relu_mkldnn(program)
 
+    def _depthwise_conv_mkldnn(self, program):
+        '''
+        Transpile the program by replacing depthwise_conv2d to conv2d for MKLDNN program.
+        The result is:
+            - before:
+                - any_other_op->depthwise_conv->any_other_op
+            - after:
+                - any_other_op->conv->any_other_op
+        :param program: program to transpile
+        :type program: Program
+        '''
+        self.block = program.block(0)
+
+        i = 0
+        while i < len(self.block.ops):
+            current_op = self.block.ops[i]
+            if current_op.type == 'depthwise_conv2d':
+                current_op.desc.set_type("conv2d")
+            i = i + 1
+
+        # TODO(luotao): use clone() method to flush the program.desc in force,
+        # since some large program.desc will not be flushed immediately.
+        # And a better solution will be considered later.
+        program = program.clone()
+
     def _fuse_conv_eltwise_mkldnn(self, program):
         '''
         Transpile the program fusing elementwise_add into conv for MKLDNN

python/setup.py.in

@@ -27,7 +27,7 @@ def _get_version_detail(idx):
     if re.match('@TAG_VERSION_REGEX@', '@PADDLE_VERSION@'):
         version_details = '@PADDLE_VERSION@'.split('.')
 
-        if len(version_details) == 3:
+        if len(version_details) >= 3:
             return version_details[idx]
 
     return 0