Merge https://github.com/PaddlePaddle/Paddle into doublegrad

cmake/generic.cmake

@@ -386,7 +386,7 @@ function(cc_test_run TARGET_NAME)
     set_property(TEST ${TARGET_NAME} PROPERTY ENVIRONMENT FLAGS_cudnn_deterministic=true)
     # No unit test should exceed 2 minutes.
     if (APPLE OR WIN32)
-        set_tests_properties(${TARGET_NAME} PROPERTIES TIMEOUT 600)
+        set_tests_properties(${TARGET_NAME} PROPERTIES TIMEOUT 150)
     else()
         set_tests_properties(${TARGET_NAME} PROPERTIES TIMEOUT 120)
     endif()
@@ -748,7 +748,7 @@ function(py_test TARGET_NAME)
     endif()
     
     if (APPLE OR WIN32)
-        set_tests_properties(${TARGET_NAME} PROPERTIES TIMEOUT 600)
+        set_tests_properties(${TARGET_NAME} PROPERTIES TIMEOUT 150)
     else()
         # No unit test should exceed 2 minutes in Linux.
         set_tests_properties(${TARGET_NAME} PROPERTIES TIMEOUT 120)

cmake/operators.cmake

@@ -138,12 +138,17 @@ function(op_library TARGET)
     # And for detail pybind information, please see generated paddle/pybind/pybind.h.
     file(READ ${TARGET}.cc TARGET_CONTENT)
     string(REGEX MATCH "REGISTER_OPERATOR\\(.*REGISTER_OPERATOR\\(" multi_register "${TARGET_CONTENT}")
-    string(REGEX MATCH "REGISTER_OPERATOR\\([a-z0-9_]*," one_register "${multi_register}")
+    # [ \t\r\n]* is used for blank characters
+    string(REGEX MATCH "REGISTER_OPERATOR\\([ \t\r\n]*[a-z0-9_]*," one_register "${multi_register}")
+
     if (one_register STREQUAL "")
         string(REPLACE "_op" "" TARGET "${TARGET}")
     else ()
         string(REPLACE "REGISTER_OPERATOR(" "" TARGET "${one_register}")
         string(REPLACE "," "" TARGET "${TARGET}")
+        # [ \t\r\n]+ is used for blank characters.
+        # Here we use '+' instead of '*' since it is a REPLACE operation.
+        string(REGEX REPLACE "[ \t\r\n]+" "" TARGET "${TARGET}")
     endif()
 
     # pybind USE_NO_KERNEL_OP

paddle/fluid/framework/ir/CMakeLists.txt

@@ -102,6 +102,8 @@ if(WITH_MKLDNN)
     pass_library(conv_concat_relu_mkldnn_fuse_pass inference DIR mkldnn)
     pass_library(conv_elementwise_add_mkldnn_fuse_pass inference DIR mkldnn)
     pass_library(scale_matmul_fuse_pass inference DIR mkldnn)
+    pass_library(cpu_bfloat16_placement_pass inference DIR mkldnn)
+    pass_library(cpu_bfloat16_pass inference DIR mkldnn)
     pass_library(fc_mkldnn_pass inference DIR mkldnn)
     pass_library(cpu_quantize_placement_pass base DIR mkldnn)
     pass_library(cpu_quantize_pass inference DIR mkldnn)
@@ -162,4 +164,6 @@ endif()
     cc_test(test_cpu_quantize_squash_pass SRCS mkldnn/cpu_quantize_squash_pass_tester.cc DEPS cpu_quantize_squash_pass naive_executor)
     cc_test(test_reshape_transpose_matmul_mkldnn_fuse_pass SRCS mkldnn/reshape_transpose_matmul_mkldnn_fuse_pass_tester.cc DEPS reshape_transpose_matmul_mkldnn_fuse_pass)
     cc_test(test_matmul_transpose_reshape_fuse_pass SRCS mkldnn/matmul_transpose_reshape_fuse_pass_tester.cc DEPS matmul_transpose_reshape_fuse_pass)
+    cc_test(test_cpu_bfloat16_placement_pass SRCS mkldnn/cpu_bfloat16_placement_pass_tester.cc DEPS cpu_bfloat16_placement_pass)
+    cc_test(test_cpu_bfloat16_pass SRCS mkldnn/cpu_bfloat16_pass_tester.cc DEPS cpu_bfloat16_pass)
 endif ()

paddle/fluid/framework/ir/graph_pattern_detector.cc

@@ -1892,6 +1892,82 @@ PDNode *patterns::QuantizePlacement::operator()(
   return op;
 }
 
+PDNode *patterns::Bfloat16Placement::operator()(
+    const std::unordered_set<std::string> &bfloat16_enabled_op_types) {
+  std::unordered_set<std::string> supported_op_types =
+      std::unordered_set<std::string>();
+  if (!bfloat16_enabled_op_types.empty()) {
+    supported_op_types = bfloat16_enabled_op_types;
+  }
+  auto *op = pattern->NewNode(op_repr())->assert_is_ops(supported_op_types);
+  return op;
+}
+
+PDNode *patterns::OrphanedBfloat16::operator()() {
+  auto *prev_op = pattern->NewNode(prev_op_repr())->assert_is_op();
+  prev_op->assert_more([&](Node *node) {
+    return node->Op()->GetAttrIfExists<std::string>("mkldnn_data_type") ==
+           "float32";
+  });
+  auto *prev_out = pattern->NewNode(prev_out_repr())->AsOutput();
+
+  auto *op = pattern->NewNode(op_repr())->assert_is_op();
+  op->assert_more([&](Node *node) {
+    return node->Op()->GetAttrIfExists<std::string>("mkldnn_data_type") ==
+           "bfloat16";
+  });
+  auto *op_out = pattern->NewNode(op_out_repr())->AsOutput();
+
+  auto *next_op = pattern->NewNode(next_op_repr())->assert_is_op();
+  next_op->assert_more([&](Node *node) {
+    return node->Op()->GetAttrIfExists<std::string>("mkldnn_data_type") ==
+           "float32";
+  });
+
+  prev_op->LinksTo({prev_out});
+  op->LinksFrom({prev_out}).LinksTo({op_out});
+  next_op->LinksFrom({op_out});
+  return next_op;
+}
+
+PDNode *patterns::LastBfloat16Ops::operator()() {
+  auto *op = pattern->NewNode(op_repr())->assert_is_op();
+  op->assert_more([&](Node *node) {
+    return node->Op()->GetAttrIfExists<std::string>("mkldnn_data_type") ==
+           "bfloat16";
+  });
+  auto *op_out = pattern->NewNode(op_out_repr())->AsOutput();
+
+  auto *next_op = pattern->NewNode(next_op_repr())->assert_is_op();
+  next_op->assert_more([&](Node *node) {
+    return node->Op()->GetAttrIfExists<std::string>("mkldnn_data_type") !=
+           "bfloat16";
+  });
+
+  op->LinksTo({op_out});
+  next_op->LinksFrom({op_out});
+  return next_op;
+}
+
+PDNode *patterns::FirstBfloat16Ops::operator()() {
+  auto *prev_op = pattern->NewNode(prev_op_repr())->assert_is_op();
+  prev_op->assert_more([&](Node *node) {
+    return node->Op()->GetAttrIfExists<std::string>("mkldnn_data_type") !=
+           "bfloat16";
+  });
+  auto *op_in = pattern->NewNode(op_in_repr())->AsOutput();
+
+  auto *op = pattern->NewNode(op_repr())->assert_is_op();
+  op->assert_more([&](Node *node) {
+    return node->Op()->GetAttrIfExists<std::string>("mkldnn_data_type") ==
+           "bfloat16";
+  });
+
+  prev_op->LinksTo({op_in});
+  op->LinksFrom({op_in});
+  return op;
+}
+
 PDNode *patterns::MKLDNNInPlace::operator()() {
   const std::unordered_set<std::string> &supported_op_types = {
       "abs",

paddle/fluid/framework/ir/graph_pattern_detector.h

@@ -1129,6 +1129,47 @@ struct QuantizePlacement : public PatternBase {
   PATTERN_DECL_NODE(op);
 };
 
+struct Bfloat16Placement : public PatternBase {
+  Bfloat16Placement(PDPattern* pattern, const std::string& name_scope)
+      : PatternBase(pattern, name_scope, "bfloat16_placement") {}
+  PDNode* operator()(
+      const std::unordered_set<std::string>& bfloat16_enabled_op_types);
+
+  PATTERN_DECL_NODE(op);
+};
+
+struct OrphanedBfloat16 : public PatternBase {
+  OrphanedBfloat16(PDPattern* pattern, const std::string& name_scope)
+      : PatternBase(pattern, name_scope, "orphaned_bfloat16") {}
+  PDNode* operator()();
+
+  PATTERN_DECL_NODE(prev_op);
+  PATTERN_DECL_NODE(prev_out);
+  PATTERN_DECL_NODE(op);
+  PATTERN_DECL_NODE(op_out);
+  PATTERN_DECL_NODE(next_op);
+};
+
+struct LastBfloat16Ops : public PatternBase {
+  LastBfloat16Ops(PDPattern* pattern, const std::string& name_scope)
+      : PatternBase(pattern, name_scope, "last_bfloat16_ops") {}
+  PDNode* operator()();
+
+  PATTERN_DECL_NODE(op);
+  PATTERN_DECL_NODE(op_out);
+  PATTERN_DECL_NODE(next_op);
+};
+
+struct FirstBfloat16Ops : public PatternBase {
+  FirstBfloat16Ops(PDPattern* pattern, const std::string& name_scope)
+      : PatternBase(pattern, name_scope, "first_bfloat16_ops") {}
+  PDNode* operator()();
+
+  PATTERN_DECL_NODE(prev_op);
+  PATTERN_DECL_NODE(op_in);
+  PATTERN_DECL_NODE(op);
+};
+
 // Pattern used for enforcing inplace computation for in-place computation
 // supporting DNNL ops. softmax, batch_norm and layer_norm
 struct MKLDNNInPlace : public PatternBase {

paddle/fluid/framework/ir/mkldnn/cpu_bfloat16_pass.cc

+/* Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+    http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/fluid/framework/ir/mkldnn/cpu_bfloat16_pass.h"
+
+#include <string>
+#include <unordered_set>
+#include <vector>
+
+#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
+#include "paddle/fluid/platform/mkldnn_helper.h"
+#include "paddle/fluid/string/pretty_log.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+using string::PrettyLogDetail;
+
+void UnlinkNodes(ir::Node* a, ir::Node* b) {
+  a->outputs.erase(std::remove(a->outputs.begin(), a->outputs.end(), b),
+                   a->outputs.end());
+  b->inputs.erase(std::remove(b->inputs.begin(), b->inputs.end(), a),
+                  b->inputs.end());
+}
+
+void CPUBFloat16Pass::SetInputDataType(ir::Graph* graph) const {
+  GraphPatternDetector gpd;
+  patterns::FirstBfloat16Ops bfloat16_ops{gpd.mutable_pattern(),
+                                          "first_bfloat16_ops"};
+  bfloat16_ops();
+  int quantize_counter = 0;
+  auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
+                     Graph* g) {
+    GET_IR_NODE_FROM_SUBGRAPH(prev_op, prev_op, bfloat16_ops);
+    GET_IR_NODE_FROM_SUBGRAPH(op_in, op_in, bfloat16_ops);
+    GET_IR_NODE_FROM_SUBGRAPH(op, op, bfloat16_ops);
+
+    if (op->Op()->Type() != "conv2d" && prev_op->Op()->Type() != "quantize") {
+      VarDesc quantize_out_desc(patterns::PDNodeName("quantize", "out"));
+      auto* quantize_out_node = g->CreateVarNode(&quantize_out_desc);
+
+      // create a quantize op node
+      OpDesc q_desc;
+      q_desc.SetType("quantize");
+      q_desc.SetInput("Input", std::vector<std::string>({op_in->Name()}));
+      q_desc.SetOutput("Output",
+                       std::vector<std::string>({quantize_out_node->Name()}));
+      q_desc.SetAttr("Scale", 1.f);
+      q_desc.SetAttr("bfloat16", true);
+      q_desc.SetAttr("output_format", Has("data_layout")
+                                          ? Get<std::string>("data_layout")
+                                          : "NCHW");
+      auto quantize_op = g->CreateOpNode(&q_desc);  // OpDesc will be copied.
+
+      std::string op_input_name;
+      for (auto name : op->Op()->InputNames()) {
+        for (auto input_name : op->Op()->Input(name)) {
+          if (input_name == op_in->Name()) op_input_name = name;
+        }
+      }
+
+      PADDLE_ENFORCE_NE(
+          op_input_name.empty(), true,
+          platform::errors::NotFound(
+              "Operator before operator should have input as op output"));
+
+      op->Op()->SetInput(op_input_name,
+                         std::vector<std::string>({quantize_out_node->Name()}));
+
+      UnlinkNodes(op_in, op);
+      IR_NODE_LINK_TO(op_in, quantize_op);
+      IR_NODE_LINK_TO(quantize_op, quantize_out_node);
+      IR_NODE_LINK_TO(quantize_out_node, op);
+      quantize_counter++;
+    }
+  };
+  gpd(graph, handler);
+  PrettyLogDetail("---    added %d quantize op before bfloat16 op",
+                  quantize_counter);
+}
+
+void CPUBFloat16Pass::SetOutputDataType(ir::Graph* graph) const {
+  GraphPatternDetector gpd;
+  patterns::LastBfloat16Ops bfloat16_ops{gpd.mutable_pattern(),
+                                         "last_bfloat16_ops"};
+  bfloat16_ops();
+  int force_fp32_counter = 0, dequantize_counter = 0;
+
+  auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
+                     Graph* g) {
+    GET_IR_NODE_FROM_SUBGRAPH(op, op, bfloat16_ops);
+    GET_IR_NODE_FROM_SUBGRAPH(op_out, op_out, bfloat16_ops);
+    GET_IR_NODE_FROM_SUBGRAPH(next_op, next_op, bfloat16_ops);
+
+    if ((op->Op()->HasAttr("force_fp32_output") ||
+         op->Op()->HasProtoAttr("force_fp32_output")) &&
+        !op->Op()->GetAttrIfExists<bool>("fuse_residual_connection")) {
+      op->Op()->SetAttr("force_fp32_output", true);
+      force_fp32_counter++;
+    } else if (op->Op()->Type() != "prior_box") {
+      // Create dequantize input variable
+      VarDesc dequantize_in_desc(patterns::PDNodeName("dequantize", "in"));
+      auto* dequantize_in_node = g->CreateVarNode(&dequantize_in_desc);
+
+      // create a dequantize op node for output.
+      OpDesc deq_desc;
+      deq_desc.SetType("dequantize");
+      deq_desc.SetInput("Input",
+                        std::vector<std::string>({dequantize_in_node->Name()}));
+      deq_desc.SetOutput("Output", std::vector<std::string>({op_out->Name()}));
+      deq_desc.SetAttr("Scale", 1.0f);
+      auto dequantize_op = g->CreateOpNode(&deq_desc);
+
+      std::string op_output_name;
+      for (auto name : op->Op()->OutputNames()) {
+        for (auto output_name : op->Op()->Output(name)) {
+          if (output_name == op_out->Name()) op_output_name = name;
+        }
+      }
+
+      PADDLE_ENFORCE_NE(
+          op_output_name.empty(), true,
+          platform::errors::NotFound(
+              "Operator after operator should have input as op output"));
+
+      op->Op()->SetOutput(op_output_name, std::vector<std::string>(
+                                              {dequantize_in_node->Name()}));
+
+      UnlinkNodes(op, op_out);
+      IR_NODE_LINK_TO(op, dequantize_in_node);
+      IR_NODE_LINK_TO(dequantize_in_node, dequantize_op);
+      IR_NODE_LINK_TO(dequantize_op, op_out);
+      dequantize_counter++;
+    }
+  };
+  gpd(graph, handler);
+  PrettyLogDetail("---    added %d dequantize op and used %d force_fp32_output",
+                  dequantize_counter, force_fp32_counter);
+}
+
+void CPUBFloat16Pass::ApplyImpl(ir::Graph* graph) const {
+  SetInputDataType(graph);
+  SetOutputDataType(graph);
+}
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+REGISTER_PASS(cpu_bfloat16_pass, paddle::framework::ir::CPUBFloat16Pass);

paddle/fluid/framework/ir/mkldnn/cpu_bfloat16_pass.h

+/* Copyright (c) 2020 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 <memory>
+
+#include "paddle/fluid/framework/ir/pass.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+class CPUBFloat16Pass : public Pass {
+ protected:
+  void SetInputDataType(ir::Graph* graph) const;
+  void SetOutputDataType(ir::Graph* graph) const;
+  void ApplyImpl(ir::Graph* graph) const override;
+};
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/ir/mkldnn/cpu_bfloat16_pass_tester.cc

+// Copyright (c) 2020 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 <gtest/gtest.h>
+
+#include "paddle/fluid/framework/ir/mkldnn/cpu_bfloat16_pass.h"
+#include "paddle/fluid/framework/naive_executor.h"
+#include "paddle/fluid/imperative/type_defs.h"
+#include "paddle/fluid/platform/place.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,
+           const std::string& mkldnn_data_type = "float32",
+           const bool force_fp32_output = false) {
+  auto* op = prog->MutableBlock(0)->AppendOp();
+  op->SetType(type);
+  op->SetAttr("use_mkldnn", use_mkldnn);
+  op->SetAttr("name", name);
+
+  if (type == "conv2d") {
+    op->SetInput("Input", {inputs[0]});
+    op->SetOutput("Output", {outputs[0]});
+    op->SetAttr("mkldnn_data_type", mkldnn_data_type);
+    op->SetAttr("force_fp32_output", force_fp32_output);
+  } else if (type == "pool2d" || type == "transpose2" || type == "reshape2" ||
+             type == "dropout") {
+    op->SetInput("X", {inputs[0]});
+    op->SetOutput("Out", {outputs[0]});
+    op->SetAttr("mkldnn_data_type", mkldnn_data_type);
+  } else if (type == "fc") {
+    op->SetInput("Input", {inputs[0]});
+    op->SetOutput("Out", {outputs[0]});
+    op->SetAttr("mkldnn_data_type", mkldnn_data_type);
+  } else if (type == "concat") {
+    op->SetInput("X", inputs);
+    op->SetOutput("Out", outputs);
+    op->SetAttr("mkldnn_data_type", mkldnn_data_type);
+  } else if (type == "matmul" || type == "elementwise_add") {
+    op->SetInput("X", {inputs[0]});
+    if (inputs.size() > 1) op->SetInput("Y", {inputs[1]});
+    op->SetOutput("Out", {outputs[0]});
+    op->SetAttr("mkldnn_data_type", mkldnn_data_type);
+  }
+}
+
+void PreparePass(std::unique_ptr<ir::Graph>* graph, const ProgramDesc& prog,
+                 const std::initializer_list<std::string> variable_names,
+                 int* original_nodes_num, int* current_nodes_num) {
+  auto pass = PassRegistry::Instance().Get("cpu_bfloat16_pass");
+
+  graph->reset(pass->Apply(graph->release()));
+
+  *original_nodes_num = (*graph)->Nodes().size();
+  (*graph).reset(pass->Apply((*graph).release()));
+  *current_nodes_num = (*graph)->Nodes().size();
+}
+
+static const std::initializer_list<std::string> variable_names{
+    "z", "a", "b", "c", "d", "e", "f", "g", "h", "i"};
+
+ProgramDesc BuildProgramDesc(bool use_mkldnn) {
+  ProgramDesc prog;
+  for (auto& v : variable_names) {
+    prog.MutableBlock(0)->Var(v);
+  }
+  SetOp(&prog, "dropout", "Dropout1", {"z"}, {"a"}, use_mkldnn, "float32");
+  SetOp(&prog, "conv2d", "Conv1", {"a"}, {"b"}, use_mkldnn, "bfloat16");
+  SetOp(&prog, "pool2d", "Pool1", {"b"}, {"c"}, use_mkldnn, "bfloat16");
+  SetOp(&prog, "conv2d", "Conv1", {"c"}, {"d"}, use_mkldnn, "bfloat16");
+  SetOp(&prog, "dropout", "Dropout2", {"d"}, {"e"}, use_mkldnn, "float32");
+  SetOp(&prog, "transpose2", "Transpose1", {"e"}, {"f"}, use_mkldnn,
+        "bfloat16");
+  SetOp(&prog, "reshape2", "Reshape1", {"f"}, {"g"}, use_mkldnn, "bfloat16");
+  SetOp(&prog, "concat", "Concat1", {"g"}, {"h"}, use_mkldnn, "bfloat16");
+  SetOp(&prog, "dropout", "Dropout3", {"h"}, {"i"}, use_mkldnn, "float32");
+
+  return prog;
+}
+
+void MainTest(const ProgramDesc& prog, int conv_count, int pool_count,
+              int transpose_count, int quant_count, int dequant_count,
+              int added_nodes_count) {
+  std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
+  int original_nodes_num, current_nodes_num;
+  PreparePass(&graph, prog, variable_names, &original_nodes_num,
+              &current_nodes_num);
+
+  int quantize_nodes_count = 0;
+  int dequantize_nodes_count = 0;
+  int conv2d_nodes_count = 0;
+  int pool2d_nodes_count = 0;
+  int transpose2_nodes_count = 0;
+
+  for (auto* node : graph->Nodes()) {
+    if (node->IsOp()) {
+      auto* op = node->Op();
+      if (op->Type() == "conv2d") {
+        conv2d_nodes_count++;
+      } else if (op->Type() == "pool2d") {
+        pool2d_nodes_count++;
+      } else if (op->Type() == "transpose2") {
+        transpose2_nodes_count++;
+      } else if (op->Type() == "quantize") {
+        quantize_nodes_count++;
+      } else if (op->Type() == "dequantize") {
+        dequantize_nodes_count++;
+      }
+    }
+  }
+  EXPECT_EQ(conv2d_nodes_count, conv_count);
+  EXPECT_EQ(pool2d_nodes_count, pool_count);
+  EXPECT_EQ(transpose2_nodes_count, transpose_count);
+  EXPECT_EQ(quantize_nodes_count, quant_count);
+  EXPECT_EQ(dequantize_nodes_count, dequant_count);
+  EXPECT_EQ(original_nodes_num + added_nodes_count, current_nodes_num);
+}
+
+TEST(CpuQuantizePass, quantize) {
+  bool use_mkldnn = true;
+  // 1 quantize + 1 dequantize
+  int added_nodes = 2;
+  MainTest(BuildProgramDesc(use_mkldnn), 2, 1, 1, 1, 2, added_nodes);
+}
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+USE_PASS(cpu_bfloat16_pass);

paddle/fluid/framework/ir/mkldnn/cpu_bfloat16_placement_pass.cc

+/* Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/fluid/framework/ir/mkldnn/cpu_bfloat16_placement_pass.h"
+
+#include <string>
+#include <unordered_set>
+
+#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
+#include "paddle/fluid/platform/mkldnn_helper.h"
+#include "paddle/fluid/string/pretty_log.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+using string::PrettyLogDetail;
+
+void CPUBfloat16PlacementPass::SetMkldnnDataType(
+    ir::Graph* graph, int* bfloat16_operators) const {
+  const auto& op_types_list =
+      Get<std::unordered_set<std::string>>("bfloat16_enabled_op_types");
+  // set mkldnn_data_type to bfloat16 to all operators that are in
+  // bfloat16_enabled_op_types vector or they are included to Bfloat16Placement
+  // pattern
+  GraphPatternDetector gpd;
+  patterns::Bfloat16Placement bfloat16_placement_pattern{gpd.mutable_pattern(),
+                                                         "bfloat16_placement"};
+  bfloat16_placement_pattern(op_types_list);
+
+  auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
+                     Graph* g) {
+    GET_IR_NODE_FROM_SUBGRAPH(op, op, bfloat16_placement_pattern);
+
+    if ((op->Op()->HasAttr("mkldnn_data_type") ||
+         op->Op()->HasProtoAttr("mkldnn_data_type")) &&
+        !platform::HasOpINT8DataType(op->Op())) {
+      op->Op()->SetAttr("mkldnn_data_type", std::string("bfloat16"));
+      (*bfloat16_operators)++;
+    }
+  };
+  gpd(graph, handler);
+}
+
+void CPUBfloat16PlacementPass::RemoveOrhanedOperators(
+    ir::Graph* graph, int* bfloat16_operators) const {
+  // find orphaned bfloat16 operator that is between two float32 operators
+  // revert mkldnn_data_type attr to float32
+  GraphPatternDetector gpd;
+  patterns::OrphanedBfloat16 orphaned_bfloat16_pattern{gpd.mutable_pattern(),
+                                                       "orphaned_bfloat16"};
+  orphaned_bfloat16_pattern();
+  auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
+                     Graph* g) {
+    GET_IR_NODE_FROM_SUBGRAPH(op, op, orphaned_bfloat16_pattern);
+
+    op->Op()->SetAttr("mkldnn_data_type", std::string("float32"));
+    bfloat16_operators--;
+  };
+  gpd(graph, handler);
+}
+
+void CPUBfloat16PlacementPass::ApplyImpl(ir::Graph* graph) const {
+  int bfloat16_operators = 0;
+  SetMkldnnDataType(graph, &bfloat16_operators);
+  RemoveOrhanedOperators(graph, &bfloat16_operators);
+  PrettyLogDetail("---    marked %d operators to bfloat16 ",
+                  bfloat16_operators);
+}
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+REGISTER_PASS(cpu_bfloat16_placement_pass,
+              paddle::framework::ir::CPUBfloat16PlacementPass)
+    // a vector of operator type names with bfloat16 support ("conv2d" etc.)
+    // the second param is the default value for this vector
+    .DefaultPassAttr("bfloat16_enabled_op_types",
+                     new std::unordered_set<std::string>());

paddle/fluid/framework/ir/mkldnn/cpu_bfloat16_placement_pass.h

+/* Copyright (c) 2020 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 <memory>
+
+#include "paddle/fluid/framework/ir/pass.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+/*
+ * Specifies which operators should be run on bfloat16.
+ */
+class CPUBfloat16PlacementPass : public Pass {
+ protected:
+  void SetMkldnnDataType(ir::Graph* graph, int* bfloat16_operators) const;
+
+  void RemoveOrhanedOperators(ir::Graph* graph, int* bfloat16_operators) const;
+
+  void ApplyImpl(ir::Graph* graph) const override;
+};
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/ir/mkldnn/cpu_bfloat16_placement_pass_tester.cc

+// Copyright (c) 2020 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 <gtest/gtest.h>
+
+#include "paddle/fluid/framework/ir/mkldnn/cpu_bfloat16_placement_pass.h"
+#include "paddle/fluid/platform/mkldnn_helper.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,
+           const std::string& mkldnn_data_type = "float32") {
+  auto* op = prog->MutableBlock(0)->AppendOp();
+
+  op->SetType(type);
+  op->SetAttr("mkldnn_data_type", mkldnn_data_type);
+
+  if (type == "conv2d") {
+    op->SetAttr("name", name);
+    op->SetInput("Input", {inputs[0]});
+  } else if (type == "relu") {
+    op->SetInput("X", inputs);
+  } else if (type == "concat") {
+    op->SetAttr("axis", 1);
+    op->SetInput("X", {inputs[0], inputs[1]});
+  } else if (type == "pool2d") {
+    op->SetInput("X", {inputs[0]});
+  } else {
+    FAIL() << "Unexpected operator type.";
+  }
+  op->SetOutput("Out", {outputs[0]});
+}
+
+// operator                      mkldnn_data_type
+// ---------------------------------------
+// (a,b)->concat->c              float32
+// c->conv->f                    float32
+// f->relu->g                    float32
+// g->pool->h                    float32
+// h->conv->k                    float32
+// k->pool->l                    float32
+ProgramDesc BuildProgramDesc() {
+  ProgramDesc prog;
+
+  for (auto& v :
+       std::vector<std::string>({"a", "b", "c", "f", "g", "h", "k", "l"})) {
+    prog.MutableBlock(0)->Var(v);
+  }
+
+  SetOp(&prog, "concat", "concat1", {"a", "b"}, {"c"});
+  SetOp(&prog, "conv2d", "conv1", {"c"}, {"f"});
+  SetOp(&prog, "relu", "relu1", {"f"}, {"g"});
+  SetOp(&prog, "pool2d", "pool1", {"g"}, {"h"});
+  SetOp(&prog, "conv2d", "conv2", {"h"}, {"k"});
+  SetOp(&prog, "pool2d", "pool2", {"k"}, {"l"});
+
+  return prog;
+}
+
+void MainTest(std::initializer_list<std::string> bfloat16_enabled_op_types,
+              unsigned expected_bfloat16_data_type_count) {
+  auto prog = BuildProgramDesc();
+
+  std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
+
+  auto pass = PassRegistry::Instance().Get("cpu_bfloat16_placement_pass");
+  pass->Set("bfloat16_enabled_op_types",
+            new std::unordered_set<std::string>(bfloat16_enabled_op_types));
+
+  graph.reset(pass->Apply(graph.release()));
+
+  unsigned bfloat16_data_type_count = 0;
+
+  for (auto* node : graph->Nodes()) {
+    if (node->IsOp()) {
+      if (platform::HasOpBFLOAT16DataType(node->Op())) {
+        ++bfloat16_data_type_count;
+      }
+    }
+  }
+
+  EXPECT_EQ(bfloat16_data_type_count, expected_bfloat16_data_type_count);
+}
+
+void DefaultAttrTest(unsigned expected_bfloat16_data_type_count) {
+  auto prog = BuildProgramDesc();
+  std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
+  auto pass = PassRegistry::Instance().Get("cpu_bfloat16_placement_pass");
+  graph.reset(pass->Apply(graph.release()));
+
+  unsigned bfloat16_data_type_count = 0;
+  for (auto* node : graph->Nodes()) {
+    if (node->IsOp()) {
+      if (platform::HasOpBFLOAT16DataType(node->Op())) {
+        ++bfloat16_data_type_count;
+      }
+    }
+  }
+  EXPECT_EQ(bfloat16_data_type_count, expected_bfloat16_data_type_count);
+}
+
+TEST(Bfloat16PlacementPass, enable_all) {
+  MainTest({"conv2d", "pool2d", "relu", "concat"}, 6);
+}
+
+TEST(Bfloat16PlacementPass, enabled_conv_and_pool) {
+  // 2 conv2d + 2 pool2 - 1 orphaned conv2d
+  MainTest({"conv2d", "pool2d"}, 3);
+}
+
+TEST(Bfloat16PlacementPass, default_attr_value) { DefaultAttrTest(0); }
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+USE_PASS(cpu_bfloat16_placement_pass);

paddle/fluid/framework/ir/transpose_flatten_concat_fuse_pass.cc

@@ -20,6 +20,7 @@
 #include "paddle/fluid/framework/ir/graph_viz_pass.h"
 #include "paddle/fluid/framework/ir/node.h"
 #include "paddle/fluid/framework/ir/transpose_flatten_concat_fuse_pass.h"
+#include "paddle/fluid/framework/op_version_registry.h"
 
 namespace paddle {
 namespace framework {
@@ -145,3 +146,11 @@ void TransposeFlattenConcatFusePass::ApplyImpl(ir::Graph *graph) const {
 
 REGISTER_PASS(transpose_flatten_concat_fuse_pass,
               paddle::framework::ir::TransposeFlattenConcatFusePass);
+REGISTER_PASS_CAPABILITY(transpose_flatten_concat_fuse_pass)
+    .AddCombination(
+        paddle::framework::compatible::OpVersionComparatorCombination()
+            .EQ("transpose", 0)
+            .EQ("transpose2", 0)
+            .EQ("flatten", 0)
+            .EQ("concat", 0)
+            .EQ("fusion_transpose_flatten_concat", 0));

paddle/fluid/framework/op_info.h

@@ -69,7 +69,8 @@ class OpInfo {
 
   const OpCreator& Creator() const {
     PADDLE_ENFORCE_NOT_NULL(creator_,
-                            "Operator's Creator has not been registered");
+                            platform::errors::NotFound(
+                                "Operator's Creator has not been registered."));
     return creator_;
   }
 
@@ -79,11 +80,12 @@ class OpInfo {
     std::string type = proto_ ? proto_->type() : "unknown";
     PADDLE_ENFORCE_NOT_NULL(
         grad_op_maker_,
+        platform::errors::NotFound(
             "Operator %s's GradOpMaker has not been "
-        "registered.\nPlease check whether %s_op has "
-        "grad_op.\nIf not, please set stop_gradient to True "
+            "registered.\nPlease check whether (%s) operator has "
+            "gradient operator.\nIf not, please set stop_gradient to be True "
             "for its input and output variables using var.stop_gradient=True.",
-        type.c_str(), type.c_str());
+            type.c_str(), type.c_str()));
     return grad_op_maker_;
   }
 
@@ -100,11 +102,12 @@ class OpInfo {
     std::string type = proto_ ? proto_->type() : "unknown";
     PADDLE_ENFORCE_NOT_NULL(
         dygraph_grad_op_maker_,
+        platform::errors::NotFound(
             "Operator %s's DygraphGradOpMaker has not been "
-        "registered.\nPlease check whether %s_op has "
-        "grad_op.\nIf not, please set stop_gradient to True "
+            "registered.\nPlease check whether (%s) operator has "
+            "gradient operator.\nIf not, please set stop_gradient to be True "
             "for its input and output variables using var.stop_gradient=True.",
-        type.c_str(), type.c_str());
+            type.c_str(), type.c_str()));
     return dygraph_grad_op_maker_;
   }
 
@@ -130,14 +133,17 @@ class OpInfoMap {
   }
 
   void Insert(const std::string& type, const OpInfo& info) {
-    PADDLE_ENFORCE(!Has(type), "Operator %s has been registered", type);
+    PADDLE_ENFORCE_NE(Has(type), true,
+                      platform::errors::AlreadyExists(
+                          "Operator (%s) has been registered.", type));
     map_.insert({type, info});
   }
 
   const OpInfo& Get(const std::string& type) const {
     auto op_info_ptr = GetNullable(type);
-    PADDLE_ENFORCE_NOT_NULL(op_info_ptr, "Operator %s has not been registered",
-                            type);
+    PADDLE_ENFORCE_NOT_NULL(
+        op_info_ptr,
+        platform::errors::NotFound("Operator (%s) is not registered.", type));
     return *op_info_ptr;
   }
 

paddle/fluid/framework/op_kernel_type.cc

@@ -33,10 +33,18 @@ size_t OpKernelType::Hash::operator()(const OpKernelType& key) const {
   cur_loc += OpKernelType::kLibBits;
 
   int customized_value = key.customized_type_value_;
-  PADDLE_ENFORCE(customized_value < (1 << OpKernelType::kCustomizeBits));
+  PADDLE_ENFORCE_LT(customized_value, (1 << OpKernelType::kCustomizeBits),
+                    platform::errors::Unavailable(
+                        "Too many custom OpKernel attribute values, expected "
+                        "maximum value is %d, received value is %d.",
+                        (1 << OpKernelType::kCustomizeBits), customized_value));
   customized_value = customized_value << cur_loc;
   cur_loc += OpKernelType::kCustomizeBits;
-  PADDLE_ENFORCE(cur_loc < 64);
+  PADDLE_ENFORCE_LT(cur_loc, 64,
+                    platform::errors::Unavailable(
+                        "Too many OpKernel attribute values, expected maximum "
+                        "value is 64, received value is %d.",
+                        cur_loc));
 
   std::hash<int> hasher;
   return hasher(place + data_type + data_layout + library_type +

paddle/fluid/framework/op_proto_maker.cc

@@ -43,7 +43,9 @@ OpProtoAndCheckerMaker::VariableBuilder OpProtoAndCheckerMaker::AddOutput(
 void OpProtoAndCheckerMaker::CheckNoDuplicatedInOutAttrs() {
   std::unordered_set<std::string> names;
   auto checker = [&](const std::string& name) {
-    PADDLE_ENFORCE(!names.count(name), "[%s] is duplicated", name);
+    PADDLE_ENFORCE_EQ(
+        names.count(name), 0,
+        platform::errors::AlreadyExists("Attribute [%s] is duplicated.", name));
     names.insert(name);
   };
   for (auto& attr : proto_->attrs()) {

paddle/fluid/framework/op_registry.h

@@ -54,9 +54,10 @@ class Registrar {
 template <typename... ARGS>
 struct OperatorRegistrar : public Registrar {
   explicit OperatorRegistrar(const char* op_type) {
-    if (OpInfoMap::Instance().Has(op_type)) {
-      PADDLE_THROW("'%s' is registered more than once.", op_type);
-    }
+    PADDLE_ENFORCE_EQ(
+        OpInfoMap::Instance().Has(op_type), false,
+        platform::errors::AlreadyExists(
+            "Operator '%s' is registered more than once.", op_type));
     static_assert(sizeof...(ARGS) != 0,
                   "OperatorRegistrar should be invoked at least by OpClass");
     OpInfo info;

paddle/fluid/framework/op_registry_test.cc

@@ -58,7 +58,8 @@ class MyTestOpProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
     AddInput("input", "input of cosine op").AsDuplicable();
     AddOutput("output", "output of cosine op").AsIntermediate();
     auto my_checker = [](int i) {
-      PADDLE_ENFORCE(i % 2 == 0, "'test_attr' must be even!");
+      PADDLE_ENFORCE_EQ(i % 2, 0, platform::errors::InvalidArgument(
+                                      "'test_attr' must be even!"));
     };
     AddAttr<int>("test_attr", "a simple test attribute")
         .AddCustomChecker(my_checker);

paddle/fluid/framework/op_version_registry.h

@@ -152,10 +152,10 @@ class OpVersionRegistrar {
     return instance;
   }
   OpVersion& Register(const std::string& op_type) {
-    if (op_version_map_.find(op_type) != op_version_map_.end()) {
-      PADDLE_THROW("'%s' is registered in operator version more than once.",
-                   op_type);
-    }
+    PADDLE_ENFORCE_EQ(
+        op_version_map_.find(op_type), op_version_map_.end(),
+        platform::errors::AlreadyExists(
+            "'%s' is registered in operator version more than once.", op_type));
     op_version_map_.insert({op_type, OpVersion()});
     return op_version_map_[op_type];
   }

paddle/fluid/framework/operator.cc

@@ -164,15 +164,20 @@ void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
     VLOG(4) << place << " " << DebugStringEx(&scope);
     if (platform::is_gpu_place(place)) {
 #ifndef PADDLE_WITH_CUDA
-      PADDLE_THROW("Cannot run operator on place %s", place);
+      PADDLE_THROW(platform::errors::Unavailable(
+          "Cannot run operator on place %s, please recompile paddle or "
+          "reinstall Paddle with CUDA support.",
+          place));
 #else
       auto dev_id = BOOST_GET_CONST(platform::CUDAPlace, place).device;
       platform::SetDeviceId(dev_id);
 #endif
     } else if (platform::is_xpu_place(place)) {
 #ifndef PADDLE_WITH_XPU
-      PADDLE_THROW(platform::errors::Unimplemented(
-          "Cannot run operator on place %s", place));
+      PADDLE_THROW(platform::errors::Unavailable(
+          "Cannot run operator on place %s, please recompile paddle or "
+          "reinstall Paddle with XPU support.",
+          place));
 #else
       auto dev_id = BOOST_GET_CONST(platform::XPUPlace, place).device;
       platform::SetXPUDeviceId(dev_id);
@@ -214,7 +219,7 @@ std::string OperatorBase::Input(const std::string& name) const {
   auto& ins = Inputs(name);
   PADDLE_ENFORCE_LE(
       ins.size(), 1UL,
-      platform::errors::AlreadyExists(
+      platform::errors::InvalidArgument(
           "Operator %s's input %s should contain only one variable.", type_,
           name));
   return ins.empty() ? kEmptyVarName : ins[0];
@@ -223,8 +228,10 @@ std::string OperatorBase::Input(const std::string& name) const {
 const std::vector<std::string>& OperatorBase::Inputs(
     const std::string& name) const {
   auto it = inputs_.find(name);
-  PADDLE_ENFORCE(it != inputs_.end(), "Operator %s does not have the input %s.",
-                 type_, name);
+  PADDLE_ENFORCE_NE(
+      it, inputs_.end(),
+      platform::errors::NotFound("Operator %s does not have the input %s.",
+                                 type_, name));
   return it->second;
 }
 
@@ -238,17 +245,21 @@ bool OperatorBase::HasOutputs(const std::string& name) const {
 
 std::string OperatorBase::Output(const std::string& name) const {
   auto& outs = Outputs(name);
-  PADDLE_ENFORCE_LE(outs.size(), 1UL,
-                    "Operator %s's output %s should contain only one variable.",
-                    type_, name);
+  PADDLE_ENFORCE_LE(
+      outs.size(), 1UL,
+      platform::errors::InvalidArgument(
+          "Operator %s's output %s should contain only one variable.", type_,
+          name));
   return outs.empty() ? kEmptyVarName : outs[0];
 }
 
 const std::vector<std::string>& OperatorBase::Outputs(
     const std::string& name) const {
   auto it = outputs_.find(name);
-  PADDLE_ENFORCE(it != outputs_.end(),
-                 "Operator %s does not have an output called %s.", type_, name);
+  PADDLE_ENFORCE_NE(
+      it, outputs_.end(),
+      platform::errors::NotFound(
+          "Operator %s does not have an output called %s.", type_, name));
   return it->second;
 }
 
@@ -391,16 +402,19 @@ void OperatorBase::CheckAllInputOutputSet() const {
 
   for (auto& in : info_->Proto().inputs()) {
     if (!in.dispensable()) {
-      PADDLE_ENFORCE(inputs_.find(in.name()) != inputs_.end(),
-                     "Operator %s's input, %s, is not set", Type(), in.name());
+      PADDLE_ENFORCE_NE(
+          inputs_.find(in.name()), inputs_.end(),
+          platform::errors::NotFound("Operator %s's input (%s) is not set.",
+                                     Type(), in.name()));
     }
   }
 
   for (auto& out : info_->Proto().outputs()) {
     if (!out.dispensable()) {
-      PADDLE_ENFORCE(outputs_.find(out.name()) != outputs_.end(),
-                     "Operator %s's output, %s, is not set", Type(),
-                     out.name());
+      PADDLE_ENFORCE_NE(
+          outputs_.find(out.name()), outputs_.end(),
+          platform::errors::NotFound("Operator %s's output (%s) is not set.",
+                                     Type(), out.name()));
     }
   }
 }
@@ -428,8 +442,9 @@ const Tensor* GetLoDTensorOrSelectedRowsValueFromVar(const Variable& var) {
   } else if (var.IsType<SelectedRows>()) {
     return &(var.Get<SelectedRows>().value());
   } else {
-    PADDLE_THROW("Variable type_id %s, expect LoDTensor/SelectedRows.",
-                 ToTypeName(var.Type()));
+    PADDLE_THROW(platform::errors::InvalidArgument(
+        "Variable type is %s, expect LoDTensor or SelectedRows.",
+        ToTypeName(var.Type())));
   }
 }
 
@@ -439,8 +454,9 @@ Tensor* GetMutableLoDTensorOrSelectedRowsValueFromVar(Variable* var) {
   } else if (var->IsType<SelectedRows>()) {
     return var->GetMutable<SelectedRows>()->mutable_value();
   } else {
-    PADDLE_THROW("Variable type_id %s, expect LoDTensor/SelectedRows.",
-                 ToTypeName(var->Type()));
+    PADDLE_THROW(platform::errors::InvalidArgument(
+        "Variable type is %s, expect LoDTensor or SelectedRows.",
+        ToTypeName(var->Type())));
   }
 }
 
@@ -462,7 +478,7 @@ const Variable* ExecutionContext::InputVar(const std::string& name) const {
 
   PADDLE_ENFORCE_LE(
       it->second.size(), 1UL,
-      platform::errors::AlreadyExists(
+      platform::errors::InvalidArgument(
           "Operator %s's input %s should contain only one variable.",
           op_.Type(), name));
   return it->second.empty() ? nullptr : it->second[0];
@@ -472,9 +488,11 @@ Variable* ExecutionContext::OutputVar(const std::string& name) const {
   auto it = ctx_.outputs.find(name);
   if (it == ctx_.outputs.end()) return nullptr;
 
-  PADDLE_ENFORCE_LE(it->second.size(), 1UL,
+  PADDLE_ENFORCE_LE(
+      it->second.size(), 1UL,
+      platform::errors::InvalidArgument(
           "Operator %s's output %s should contain only one variable.",
-                    op_.Type(), name);
+          op_.Type(), name));
   return it->second.empty() ? nullptr : it->second[0];
 }
 
@@ -497,10 +515,11 @@ const std::vector<const Tensor*> ExecutionContext::MultiInput<Tensor>(
   std::transform(vars.begin(), vars.end(), std::back_inserter(res),
                  [&](const Variable* var) -> const Tensor* {
                    if (var == nullptr) return nullptr;
-                   PADDLE_ENFORCE(
-                       var->IsType<LoDTensor>(),
-                       "should be LoDTensor, but the received type is %s",
-                       ToTypeName(var->Type()));
+                   PADDLE_ENFORCE_EQ(var->IsType<LoDTensor>(), true,
+                                     platform::errors::InvalidArgument(
+                                         "Input variable should be LoDTensor, "
+                                         "but the received type is %s.",
+                                         ToTypeName(var->Type())));
                    return &(var->Get<LoDTensor>());
                  });
   return res;
@@ -558,8 +577,10 @@ class RuntimeInferShapeContext : public InferShapeContext {
     }
     const auto& in = it->second;
     if (in.size() == 0) return false;
-    PADDLE_ENFORCE_EQ(in.size(), 1UL,
-                      "Input %s should not have more than one inputs", name);
+    PADDLE_ENFORCE_EQ(
+        in.size(), 1UL,
+        platform::errors::InvalidArgument(
+            "Input %s should not contain more than one inputs.", name));
     return in[0] != nullptr;
   }
 
@@ -574,8 +595,10 @@ class RuntimeInferShapeContext : public InferShapeContext {
     if (out.size() == 0) {
       return false;
     }
-    PADDLE_ENFORCE_EQ(out.size(), 1UL,
-                      "Output %s should not have more than one outputs", name);
+    PADDLE_ENFORCE_EQ(
+        out.size(), 1UL,
+        platform::errors::InvalidArgument(
+            "Output %s should not contain more than one outputs.", name));
     return out[0] != nullptr;
   }
 
@@ -644,16 +667,31 @@ class RuntimeInferShapeContext : public InferShapeContext {
                 size_t j = 0) override {
     auto in_it = ctx_.inputs.find(in);
     auto out_it = ctx_.outputs.find(out);
-    PADDLE_ENFORCE(in_it != ctx_.inputs.end() && in_it->second.size() > i,
-                   "Inputs %s should have %llu argument", in, i);
-    PADDLE_ENFORCE(out_it != ctx_.outputs.end() && out_it->second.size() > j,
-                   "Outputs %s should have %llu argument", out, j);
+    PADDLE_ENFORCE_NE(
+        in_it, ctx_.inputs.end(),
+        platform::errors::NotFound("Input %s does not exist.", in));
+    PADDLE_ENFORCE_NE(
+        out_it, ctx_.outputs.end(),
+        platform::errors::NotFound("Output %s does not exist.", out));
+    PADDLE_ENFORCE_LT(i, in_it->second.size(),
+                      platform::errors::InvalidArgument(
+                          "The index of input dimension is out of range, "
+                          "excepted index less than %zu, but received %zu.",
+                          in_it->second.size(), i));
+    PADDLE_ENFORCE_LT(j, out_it->second.size(),
+                      platform::errors::InvalidArgument(
+                          "The index of output dimension is out of range, "
+                          "excepted index less than %zu, but received %zu.",
+                          out_it->second.size(), j));
 
     Variable* in_var = in_it->second[i];
     Variable* out_var = out_it->second[j];
 
-    PADDLE_ENFORCE(in_var->Type() == out_var->Type(),
-                   "The type of %s and %s is not the same.", in, out);
+    PADDLE_ENFORCE_EQ(
+        in_var->Type(), out_var->Type(),
+        platform::errors::InvalidArgument(
+            "The type of input (%s) and output (%s) are inconsistent.", in,
+            out));
 
     if (in_var->IsType<framework::SelectedRows>()) {
       auto& in_sele_rows = in_var->Get<framework::SelectedRows>();
@@ -666,9 +704,9 @@ class RuntimeInferShapeContext : public InferShapeContext {
       auto* out_lod_tensor = out_var->GetMutable<framework::LoDTensor>();
       out_lod_tensor->Resize(in_lod_tensor.dims());
     } else {
-      PADDLE_THROW(
+      PADDLE_THROW(platform::errors::Unimplemented(
           "Currently, the input type of ShareDim only can be LoDTensor "
-          "or SelectedRows.");
+          "or SelectedRows."));
     }
   }
 
@@ -721,16 +759,30 @@ class RuntimeInferShapeContext : public InferShapeContext {
                 size_t j = 0) const override {
     auto in_it = ctx_.inputs.find(in);
     auto out_it = ctx_.outputs.find(out);
-    PADDLE_ENFORCE(in_it != ctx_.inputs.end() && in_it->second.size() > i,
-                   "Inputs %s should have %llu argument", in, i);
-    PADDLE_ENFORCE(out_it != ctx_.outputs.end() && out_it->second.size() > j,
-                   "Outputs %s should have %llu argument", out, j);
+    PADDLE_ENFORCE_NE(
+        in_it, ctx_.inputs.end(),
+        platform::errors::NotFound("Input %s does not exist.", in));
+    PADDLE_ENFORCE_NE(
+        out_it, ctx_.outputs.end(),
+        platform::errors::NotFound("Output %s does not exist.", out));
+    PADDLE_ENFORCE_LT(i, in_it->second.size(),
+                      platform::errors::InvalidArgument(
+                          "The index of input dimension is out of range, "
+                          "excepted index less than %zu, but received %zu.",
+                          in_it->second.size(), i));
+    PADDLE_ENFORCE_LT(j, out_it->second.size(),
+                      platform::errors::InvalidArgument(
+                          "The index of output dimension is out of range, "
+                          "excepted index less than %zu, but received %zu.",
+                          out_it->second.size(), j));
 
     Variable* in_var = in_it->second.at(i);
     if (!in_var->IsType<LoDTensor>()) return;
     Variable* out_var = out_it->second.at(j);
-    PADDLE_ENFORCE(out_var->IsType<LoDTensor>(),
-                   "The %d-th output of Output(%s) must be LoDTensor.", j, out);
+    PADDLE_ENFORCE_EQ(
+        out_var->IsType<LoDTensor>(), true,
+        platform::errors::InvalidArgument(
+            "The %zu-th output of Output(%s) must be LoDTensor.", j, out));
     auto& in_tensor = in_var->Get<LoDTensor>();
     auto* out_tensor = out_var->GetMutable<LoDTensor>();
     out_tensor->set_lod(in_tensor.lod());
@@ -757,18 +809,18 @@ class RuntimeInferShapeContext : public InferShapeContext {
   }
 
   int32_t GetLoDLevel(const std::string& in, size_t i = 0) const override {
-    PADDLE_THROW(
+    PADDLE_THROW(platform::errors::PreconditionNotMet(
         "GetLoDLevel is only used in compile time. The calculation of "
         "output's actual lod is different among operators so that should be "
-        "set in the runtime kernel.");
+        "set in the runtime kernel."));
   }
 
   void SetLoDLevel(const std::string& out, int32_t lod_level,
                    size_t j = 0) const override {
-    PADDLE_THROW(
+    PADDLE_THROW(platform::errors::PreconditionNotMet(
         "SetLoDLevel is only used in compile time. The calculation of "
         "output's actual lod is different among operators so that should be "
-        "set in the runtime kernel.");
+        "set in the runtime kernel."));
   }
 
   bool IsRuntime() const override { return true; }
@@ -794,9 +846,11 @@ class RuntimeInferShapeContext : public InferShapeContext {
 
   DDim GetInputDim(const std::string& name) const override {
     const std::vector<Variable*>& vars = InputVars(name);
-    PADDLE_ENFORCE_EQ(vars.size(), 1UL,
-                      "Input(%s) should hold one element, but now it holds %d",
-                      name, vars.size());
+    PADDLE_ENFORCE_EQ(
+        vars.size(), 1UL,
+        platform::errors::InvalidArgument(
+            "Input(%s) should hold one element, but now it holds %zu elements.",
+            name, vars.size()));
     return this->GetDim(vars[0]);
   }
 
@@ -817,9 +871,11 @@ class RuntimeInferShapeContext : public InferShapeContext {
 
   void SetOutputDim(const std::string& name, const DDim& dim) override {
     auto& vars = OutputVars(name);
-    PADDLE_ENFORCE_EQ(vars.size(), 1UL,
-                      "Output(%s) should hold one element, but now it holds %d",
-                      name, vars.size());
+    PADDLE_ENFORCE_EQ(
+        vars.size(), 1UL,
+        platform::errors::InvalidArgument("Output(%s) should hold one element, "
+                                          "but now it holds %zu elements.",
+                                          name, vars.size()));
     SetDim(vars[0], dim);
   }
 
@@ -831,16 +887,17 @@ class RuntimeInferShapeContext : public InferShapeContext {
 
  protected:
   DDim GetDim(Variable* var) const {
-    PADDLE_ENFORCE_NOT_NULL(var);
+    PADDLE_ENFORCE_NOT_NULL(
+        var, platform::errors::InvalidArgument("Input variable is nullptr."));
     if (var->IsType<LoDTensor>()) {
       return var->Get<LoDTensor>().dims();
     } else if (var->IsType<SelectedRows>()) {
       return var->Get<SelectedRows>().GetCompleteDims();
     } else {
-      PADDLE_THROW(
-          "Only LoDTensor/SelectedRows support 'GetDim', but Variables "
-          "type_id is %s.",
-          ToTypeName(var->Type()));
+      PADDLE_THROW(platform::errors::InvalidArgument(
+          "Only LoDTensor or SelectedRows support 'GetDim', but input "
+          "Variable's type is %s.",
+          ToTypeName(var->Type())));
     }
   }
 
@@ -853,7 +910,8 @@ class RuntimeInferShapeContext : public InferShapeContext {
   }
 
   std::vector<DDim> GetRepeatedDims(const std::string& name) const override {
-    PADDLE_THROW("Only compile time support this method");
+    PADDLE_THROW(platform::errors::PreconditionNotMet(
+        "GetRepeatedDims method only ban be used in compile time."));
   }
 
   void SetDim(Variable* var, const DDim& dim) {
@@ -862,15 +920,22 @@ class RuntimeInferShapeContext : public InferShapeContext {
     } else if (var->IsType<SelectedRows>()) {
       var->GetMutable<SelectedRows>()->set_height(dim[0]);
     } else {
-      PADDLE_THROW("Variable type_id %s, expect LoDTensor/SelectedRows.",
-                   ToTypeName(var->Type()));
+      PADDLE_THROW(platform::errors::Unimplemented(
+          "Variable type error, expect LoDTensor or SelectedRows, but received "
+          "(%s).",
+          ToTypeName(var->Type())));
     }
   }
 
   void SetDims(const std::vector<Variable*>& vars,
                const std::vector<DDim>& dims) {
     size_t length = vars.size();
-    PADDLE_ENFORCE_EQ(length, dims.size());
+    PADDLE_ENFORCE_EQ(length, dims.size(),
+                      platform::errors::InvalidArgument(
+                          "The number of input variables do not match the "
+                          "number of input dimensions, the number of variables "
+                          "is %zu, the number of dimensions is %zu.",
+                          length, dims.size()));
     for (size_t i = 0; i < length; ++i) {
       if (vars[i] == nullptr) {
         continue;
@@ -881,7 +946,8 @@ class RuntimeInferShapeContext : public InferShapeContext {
 
   void SetRepeatedDims(const std::string& name,
                        const std::vector<DDim>& dims) override {
-    PADDLE_THROW("Only compile time support this method");
+    PADDLE_THROW(platform::errors::PreconditionNotMet(
+        "SetRepeatedDims method only can be used in compile time."));
   }
 
   std::vector<proto::VarType::Type> GetVarTypes(
@@ -901,16 +967,19 @@ class RuntimeInferShapeContext : public InferShapeContext {
  private:
   const std::vector<Variable*>& InputVars(const std::string& name) const {
     auto it = ctx_.inputs.find(name);
-    PADDLE_ENFORCE(it != ctx_.inputs.end(),
-                   "Operator %s does not have the input %s.", op_.Type(), name);
+    PADDLE_ENFORCE_NE(
+        it, ctx_.inputs.end(),
+        platform::errors::NotFound(
+            "Operator (%s) does not have the input (%s).", op_.Type(), name));
     return it->second;
   }
 
   const std::vector<Variable*>& OutputVars(const std::string& name) const {
     auto it = ctx_.outputs.find(name);
-    PADDLE_ENFORCE(it != ctx_.outputs.end(),
-                   "Operator %s does not have the outputs %s.", op_.Type(),
-                   name);
+    PADDLE_ENFORCE_NE(
+        it, ctx_.outputs.end(),
+        platform::errors::NotFound(
+            "Operator (%s) does not have the outputs (%s).", op_.Type(), name));
     return it->second;
   }
 
@@ -928,10 +997,14 @@ static void CheckTensorNANOrInf(const std::string& op_type,
       tensor.type() != proto::VarType::FP64) {
     return;
   }
-  PADDLE_ENFORCE(!framework::TensorContainsInf(tensor),
-                 "Operator %s output Tensor %s contains Inf", op_type, name);
-  PADDLE_ENFORCE(!framework::TensorContainsNAN(tensor),
-                 "Operator %s output Tensor %s contains NAN", op_type, name);
+  PADDLE_ENFORCE_NE(
+      framework::TensorContainsInf(tensor), true,
+      platform::errors::Fatal("Operator %s output Tensor %s contains Inf.",
+                              op_type, name));
+  PADDLE_ENFORCE_NE(
+      framework::TensorContainsNAN(tensor), true,
+      platform::errors::Fatal("Operator %s output Tensor %s contains NAN.",
+                              op_type, name));
 }
 
 void OperatorWithKernel::RuntimeInferShape(const Scope& scope,
@@ -1074,10 +1147,11 @@ void OperatorWithKernel::ChooseKernel(const RuntimeContext& ctx,
   // check if op[type] has kernel registered.
   auto& all_op_kernels = AllOpKernels();
   auto kernels_iter = all_op_kernels.find(type_);
-  if (kernels_iter == all_op_kernels.end()) {
-    PADDLE_THROW(
-        "There are no kernels which are registered in the %s operator.", type_);
-  }
+  PADDLE_ENFORCE_NE(
+      kernels_iter, all_op_kernels.end(),
+      platform::errors::Unavailable(
+          "There are no kernels which are registered in the %s operator.",
+          type_));
 
   OpKernelMap& kernels = kernels_iter->second;
 
@@ -1131,10 +1205,10 @@ void OperatorWithKernel::ChooseKernel(const RuntimeContext& ctx,
     kernel_iter = kernels.find(expected_kernel_key);
   }
 #endif
-  if (kernel_iter == kernels.end()) {
-    PADDLE_THROW("op %s does not have kernel for %s", type_,
-                 KernelTypeToString(expected_kernel_key));
-  }
+  PADDLE_ENFORCE_NE(kernel_iter, kernels.end(),
+                    platform::errors::NotFound(
+                        "Operator (%s) does not have kernel for %s.", type_,
+                        KernelTypeToString(expected_kernel_key)));
 
   std::lock_guard<std::mutex> lock(cache_update_mutex_);
   if (kernel_type_.get() == nullptr || kernel_func_.get() == nullptr) {
@@ -1149,13 +1223,14 @@ void OperatorWithKernel::TransferInplaceVarsBack(
   for (auto& var_name : inplace_vars) {
     VLOG(3) << "share inplace var " + var_name + " back to it's original scope";
     auto* origin_var = scope.FindVar(var_name);
-    PADDLE_ENFORCE_NOT_NULL(origin_var, "The var[%s] should not be nullptr.",
-                            var_name);
+    PADDLE_ENFORCE_NOT_NULL(origin_var,
+                            platform::errors::InvalidArgument(
+                                "The variable[%s] is nullptr.", var_name));
     auto* original_tensor =
         GetMutableLoDTensorOrSelectedRowsValueFromVar(origin_var);
     auto* var = transfer_scope.FindVar(var_name);
-    PADDLE_ENFORCE_NOT_NULL(var, "The var[%s] should not be nullptr.",
-                            var_name);
+    PADDLE_ENFORCE_NOT_NULL(var, platform::errors::InvalidArgument(
+                                     "The variable[%s] is nullptr.", var_name));
     auto* transformed_tensor = GetLoDTensorOrSelectedRowsValueFromVar(*var);
     auto original_dims = original_tensor->dims();
     original_tensor->ShareDataWith(*transformed_tensor);
@@ -1380,9 +1455,11 @@ proto::VarType::Type OperatorWithKernel::IndicateVarDataType(
   ParseInputDataType(ctx, name, &data_type);
   PADDLE_ENFORCE_NE(
       data_type, dafault_data_type,
-      "The Input Variable(%s) of %s Op used to determine kernel data type "
-      "is empty or not LoDTensor or SelectedRows or LoDTensorArray.",
-      name, Type());
+      platform::errors::InvalidArgument(
+          "The Input Variable(%s) of (%s) Operator used to determine kernel "
+          "data type is empty or not LoDTensor or SelectedRows or "
+          "LoDTensorArray.",
+          name, Type()));
   return data_type;
 }
 

paddle/fluid/framework/operator_test.cc

@@ -495,9 +495,9 @@ TEST(IndicateVarDataTypeTest, other) {
     EXPECT_TRUE(
         ex_msg.find(
             "The Input Variable(Other) of "
-            "indicate_other_data_type_test Op used to "
+            "(indicate_other_data_type_test) Operator used to "
             "determine kernel data type "
-            "is empty or not LoDTensor or SelectedRows or LoDTensorArray") !=
+            "is empty or not LoDTensor or SelectedRows or LoDTensorArray.") !=
         std::string::npos);
   }
   ASSERT_TRUE(caught);

paddle/fluid/framework/reader.cc

@@ -20,7 +20,10 @@ namespace framework {
 
 void ReaderBase::ReadNext(std::vector<LoDTensor> *out) {
   std::lock_guard<std::mutex> lock(mu_);
-  PADDLE_ENFORCE_EQ(status_, ReaderStatus::kRunning);
+  PADDLE_ENFORCE_EQ(status_, ReaderStatus::kRunning,
+                    platform::errors::Unavailable(
+                        "The current reader has stopped running and cannot "
+                        "continue to read the next batch of data."));
   ReadNextImpl(out);
 }
 

paddle/fluid/framework/rw_lock.h

@@ -32,17 +32,21 @@ struct RWLock {
   ~RWLock() { pthread_rwlock_destroy(&lock_); }
 
   inline void RDLock() {
-    PADDLE_ENFORCE_EQ(pthread_rwlock_rdlock(&lock_), 0,
-                      "acquire read lock failed");
+    PADDLE_ENFORCE_EQ(
+        pthread_rwlock_rdlock(&lock_), 0,
+        platform::errors::External("The pthread failed to acquire read lock."));
   }
 
   inline void WRLock() {
     PADDLE_ENFORCE_EQ(pthread_rwlock_wrlock(&lock_), 0,
-                      "acquire write lock failed");
+                      platform::errors::External(
+                          "The pthread failed to acquire write lock."));
   }
 
   inline void UNLock() {
-    PADDLE_ENFORCE_EQ(pthread_rwlock_unlock(&lock_), 0, "unlock failed");
+    PADDLE_ENFORCE_EQ(
+        pthread_rwlock_unlock(&lock_), 0,
+        platform::errors::External("The pthread failed to unlock."));
   }
 
  private:

paddle/fluid/framework/save_load_util.cc

@@ -33,7 +33,8 @@ void CheckInStreamState(std::istream& istre, size_t length) {
     VLOG(5) << "Can't read [" << length << "] from file"
             << "file seems breakem";
 
-    PADDLE_THROW("Model load error, file seems breaken");
+    PADDLE_THROW(platform::errors::Unavailable(
+        "Model load failed, istream state error."));
   }
 }
 
@@ -58,10 +59,11 @@ size_t ReadTensorNumber(std::istream& istre) {
              sizeof(char) * tensor_number_mark.size());
   std::string str_read_tensor_number_mark(tensor_number_mark_buffer,
                                           tensor_number_mark.size());
-  PADDLE_ENFORCE_EQ(
-      tensor_number_mark, str_read_tensor_number_mark,
-      "Tensor number mark not match, expect [%s], but read from file is [%]",
-      tensor_number_mark, str_read_tensor_number_mark);
+  PADDLE_ENFORCE_EQ(tensor_number_mark, str_read_tensor_number_mark,
+                    platform::errors::InvalidArgument(
+                        "Tensor number mark does not match, expect mark is "
+                        "[%s], but the mark read from file is [%s].",
+                        tensor_number_mark, str_read_tensor_number_mark));
 
   size_t tensor_number = 0;
   istre.read(reinterpret_cast<char*>(&tensor_number), sizeof(tensor_number));
@@ -79,10 +81,11 @@ std::string ReadTensorName(std::istream& istre) {
 
   std::string str_read_tensor_name_mark(name_mark_buffer,
                                         tensor_name_mark.size());
-  PADDLE_ENFORCE_EQ(
-      tensor_name_mark, str_read_tensor_name_mark,
-      "Tensor name mark not match, expect [%s], but read from file is [%]",
-      tensor_name_mark, str_read_tensor_name_mark);
+  PADDLE_ENFORCE_EQ(tensor_name_mark, str_read_tensor_name_mark,
+                    platform::errors::InvalidArgument(
+                        "Tensor name mark does not match, expect mark is [%s], "
+                        "but the mark read from file is [%s].",
+                        tensor_name_mark, str_read_tensor_name_mark));
 
   size_t tensor_name_length = 0;
   istre.read(reinterpret_cast<char*>(&tensor_name_length),
@@ -117,16 +120,18 @@ bool SaveStaticNameListToDisk(
 
   for (size_t i = 0; i < vec_tensor_name_list.size(); ++i) {
     auto var_ptr = scope.FindVar(vec_tensor_name_list[i]);
-    PADDLE_ENFORCE_NE(
-        var_ptr, nullptr,
-        "Variable find error, when save model, can't not find vairable [%s], "
-        "Please make sure you have run StartUpProgram",
-        vec_tensor_name_list[i]);
+    PADDLE_ENFORCE_NOT_NULL(
+        var_ptr, platform::errors::NotFound("Variable (%s) is not found when "
+                                            "saving model, please make sure "
+                                            "that exe.run(startup_program) has "
+                                            "been executed.",
+                                            vec_tensor_name_list[i]));
     Tensor* tensor = var_ptr->GetMutable<LoDTensor>();
     PADDLE_ENFORCE_EQ(tensor->IsInitialized(), true,
-                      "Paramter [%s] not initialzed,"
-                      "Please make sure you have run StartUpProgram",
-                      vec_tensor_name_list[i]);
+                      platform::errors::PreconditionNotMet(
+                          "Paramter [%s] is not initialzed, please make sure "
+                          "that exe.run(startup_program) has been executed.",
+                          vec_tensor_name_list[i]));
 
     map_tensor[vec_tensor_name_list[i]] = tensor;
   }
@@ -145,9 +150,10 @@ bool SaveDygraphVarBaseListToDisk(
     Tensor* tensor = var_ptr->GetMutable<LoDTensor>();
 
     PADDLE_ENFORCE_EQ(tensor->IsInitialized(), true,
-                      "Paramter [%s] not initialzed,"
-                      "Please make sure you have run StartUpProgram",
-                      vec_var_base_list[i]->Name());
+                      platform::errors::PreconditionNotMet(
+                          "Paramter [%s] is not initialzed, please make sure "
+                          "that exe.run(startup_program) has been executed.",
+                          vec_var_base_list[i]->Name()));
 
     map_tensor[vec_var_base_list[i]->Name()] = tensor;
   }
@@ -185,34 +191,41 @@ bool LoadStaticNameListFromDisk(
 
   for (size_t i = 0; i < vec_tensor_name_list.size(); ++i) {
     auto it = map_load_tensor.find(vec_tensor_name_list[i]);
-    PADDLE_ENFORCE(it != map_load_tensor.end(),
-                   "Paramete not found in Model file, "
-                   "Can not find [%s] in model file [%s]",
-                   vec_tensor_name_list[i], file_name);
+    PADDLE_ENFORCE_NE(it, map_load_tensor.end(),
+                      platform::errors::NotFound(
+                          "Parameter (%s) not found in model file (%s).",
+                          vec_tensor_name_list[i], file_name));
     auto var_ptr = scope.FindVar(vec_tensor_name_list[i]);
 
-    PADDLE_ENFORCE_NE(
-        var_ptr, nullptr,
-        "Parameter not created, when load model, can't not find parameter [%s] "
-        "please make sure you have run StartUpProgram",
-        vec_tensor_name_list[i]);
+    PADDLE_ENFORCE_NOT_NULL(
+        var_ptr,
+        platform::errors::PreconditionNotMet(
+            "Parameter (%s) is not created when loading model, "
+            "please make sure that exe.run(startup_program) has been executed.",
+            vec_tensor_name_list[i]));
 
     Tensor* tensor = var_ptr->GetMutable<LoDTensor>();
-    PADDLE_ENFORCE_NE(tensor, nullptr,
-                      "Paramter [%s] not initialzed "
-                      "please make sure you have run startUpProgram",
-                      vec_tensor_name_list[i]);
+    PADDLE_ENFORCE_NOT_NULL(
+        tensor,
+        platform::errors::PreconditionNotMet(
+            "Paramter [%s] is not initialzed, "
+            "please make sure that exe.run(startup_program) has been executed.",
+            vec_tensor_name_list[i]));
 
     PADDLE_ENFORCE_EQ(tensor->IsInitialized(), true,
-                      "Paramter [%s] not initialzed "
-                      "please make sure you have run StartUpProgram",
-                      vec_tensor_name_list[i]);
+                      platform::errors::PreconditionNotMet(
+                          "Paramter [%s] is not initialzed, "
+                          "please make sure that exe.run(startup_program) has "
+                          "been executed.v",
+                          vec_tensor_name_list[i]));
     PADDLE_ENFORCE_EQ(
         tensor->dims(), it->second->dims(),
-        "Shape not matching: the Program requires a parameter with a shape of "
-        "(%s), "
-        "while the loaded parameter (namely [ %s ]) has a shape of  (%s).",
-        tensor->dims(), vec_tensor_name_list[i], it->second->dims());
+        platform::errors::InvalidArgument(
+            "Shape does not match, the program requires a parameter with a "
+            "shape of "
+            "(%s), while the loaded parameter (namely [ %s ]) has a shape of "
+            "(%s).",
+            tensor->dims(), vec_tensor_name_list[i], it->second->dims()));
 
     TensorCopySync(*(it->second.get()), tensor->place(), tensor);
 
@@ -239,9 +252,9 @@ bool SaveTensorToDisk(const std::string& file_name,
   MkDirRecursively(DirName(file_name).c_str());
 
   std::ofstream fout(file_name, std::ios::binary);
-  if (!fout) {
-    PADDLE_THROW("File open error. Can not open file [%s]", file_name);
-  }
+  PADDLE_ENFORCE_EQ(
+      fout.is_open(), true,
+      platform::errors::Unavailable("File (%s) open failed.", file_name));
 
   // first 256 byte for reserve for fulture upgrade
   char* kReserveBuffer = new char[model_file_reserve_size];
@@ -292,9 +305,8 @@ bool SaveTensorToDisk(const std::string& file_name,
       TensorCopySync(*tensor, platform::CPUPlace(), &temp);
       data_ptr = temp.data<void>();
 #else
-      PADDLE_THROW(
-          "Tensor is in CUDA device, but paddle not compile with CUDA, this "
-          "should not happen");
+      PADDLE_THROW(platform::errors::Unavailable(
+          "Tensor is in CUDA device, but paddle not compiled with CUDA."));
 #endif
     }
     fout.write(static_cast<const char*>(data_ptr),
@@ -302,8 +314,9 @@ bool SaveTensorToDisk(const std::string& file_name,
   }
 
   if (!fout) {
-    PADDLE_THROW("Model save failed, data write to model file [%s] error",
-                 file_name);
+    PADDLE_THROW(platform::errors::Unavailable(
+        "Model save failed, error when writing data into model file [%s].",
+        file_name));
   }
 
   fout.close();
@@ -316,9 +329,9 @@ bool LoadTensorFromDisk(
     std::map<std::string, std::shared_ptr<Tensor>>* map_tensor) {
   std::ifstream fin(file_name, std::ios::binary);
 
-  if (!fin) {
-    PADDLE_THROW("File open error. Can not open model file [%s]", file_name);
-  }
+  PADDLE_ENFORCE_EQ(
+      fin.is_open(), true,
+      platform::errors::Unavailable("File (%s) open failed.", file_name));
 
   ReadReserveBuffer(fin);
 
@@ -331,7 +344,8 @@ bool LoadTensorFromDisk(
     uint32_t version;
     fin.read(reinterpret_cast<char*>(&version), sizeof(version));
     CheckInStreamState(fin, sizeof(version));
-    PADDLE_ENFORCE_EQ(version, 0U, "Only version 0 is supported");
+    PADDLE_ENFORCE_EQ(version, 0U, platform::errors::InvalidArgument(
+                                       "Only version 0 tensor is supported."));
     proto::VarType::TensorDesc desc;
     {
       // int32_t size
@@ -344,7 +358,7 @@ bool LoadTensorFromDisk(
       CheckInStreamState(fin, sizeof(size));
       PADDLE_ENFORCE_EQ(
           desc.ParseFromArray(buf.get(), size), true,
-          platform::errors::InvalidArgument("Cannot parse tensor desc"));
+          platform::errors::InvalidArgument("Parse tensor desc failed."));
     }
 
     {  // read tensor

paddle/fluid/framework/selected_rows.cc

@@ -113,7 +113,9 @@ void DeserializeFromStream(std::istream& is, SelectedRows* selected_rows,
     // the 1st field, unit32_t version for SelectedRows
     uint32_t version;
     is.read(reinterpret_cast<char*>(&version), sizeof(version));
-    PADDLE_ENFORCE_EQ(version, 0U, "Only version 0 is supported");
+    PADDLE_ENFORCE_EQ(version, 0U,
+                      platform::errors::InvalidArgument(
+                          "Only version 0 SelectedRows is supported."));
   }
   {
     // the 2st field, rows information
@@ -155,24 +157,27 @@ int64_t SelectedRows::AutoGrownIndex(int64_t key, bool auto_grown,
   auto iter = id_to_index_.find(key);
   if (iter == id_to_index_.end()) {
     rwlock_->UNLock();
-    if (!auto_grown) {
-      PADDLE_THROW("key %d not found", key);
-    }
+    PADDLE_ENFORCE_EQ(
+        auto_grown, true,
+        platform::errors::NotFound("Input key(%lld) is not found.", key));
     rwlock_->WRLock();
     auto map_size = id_to_index_.size();
     auto vector_size = rows_.size();
     if (map_size != vector_size) {
       rwlock_->UNLock();
-      PADDLE_THROW(
-          "id_to_index_ size %d should have the same size with rows_ %d",
-          map_size, vector_size);
+      PADDLE_THROW(platform::errors::InvalidArgument(
+          "Row map size(%zu) should be equal to rows size(%zu).", map_size,
+          vector_size));
     }
     auto write_iter = id_to_index_.find(key);
     if (write_iter == id_to_index_.end()) {
       int row_num = rows_.size();
       if (row_num == value_->dims()[0]) {
         rwlock_->UNLock();
-        PADDLE_THROW("selected rows is full, then length exceed %d", row_num);
+        PADDLE_THROW(platform::errors::InvalidArgument(
+            "Selected rows is full, then length exceed the length of first "
+            "dimension (%d).",
+            row_num));
       }
       // key logic to put a key into id_to_index_
       rows_.push_back(key);
@@ -203,15 +208,20 @@ void SelectedRows::SyncIndex() {
 
 void SelectedRows::Get(const framework::Tensor& ids, framework::Tensor* value,
                        bool auto_grown, bool is_test) {
-  PADDLE_ENFORCE(value->IsInitialized(),
-                 "The value tensor should be initialized.");
+  PADDLE_ENFORCE_EQ(value->IsInitialized(), true,
+                    platform::errors::InvalidArgument(
+                        "The value tensor is not initialized."));
   if (ids.numel() == 0) {
     VLOG(3) << "keys is empty, please check data!";
   } else {
     int64_t value_width = value_->numel() / value_->dims()[0];
-    PADDLE_ENFORCE_EQ(value_width, value->numel() / value->dims()[0],
-                      "output tensor should have the same shape with table "
-                      "except the dims[0].");
+    PADDLE_ENFORCE_EQ(
+        value_width, value->numel() / value->dims()[0],
+        platform::errors::InvalidArgument(
+            "Output tensor should have the same shape with table "
+            "except the first dimmension, excepted value width not counting "
+            "the first dimension is %d, actual value width is %d.",
+            value_width, value->numel() / value->dims()[0]));
     for (int i = 0; i < ids.numel(); ++i) {
       auto id = ids.data<int64_t>()[i];
       int64_t index = AutoGrownIndex(id, auto_grown, is_test);

paddle/fluid/framework/selected_rows.h

@@ -82,7 +82,8 @@ class SelectedRows {
   int64_t Index(int64_t key) const {
     auto it = std::find(rows_.begin(), rows_.end(), key);
     if (it == rows_.end()) {
-      PADDLE_THROW("id %s not in table", key);
+      PADDLE_THROW(platform::errors::NotFound(
+          "Input id (%lld) is not in current rows table.", key));
     }
     return static_cast<int64_t>(std::distance(rows_.begin(), it));
   }

paddle/fluid/framework/shape_inference.cc

@@ -25,20 +25,22 @@ namespace framework {
 std::vector<DDim> InferShapeContext::GetReaderDims(
     const std::string &name) const {
   const std::vector<std::string> &arg_names = Inputs(name);
-  PADDLE_ENFORCE_EQ(
-      arg_names.size(), 1UL,
-      "Reader input '%s' should hold one element, but now it holds %d", name,
-      arg_names.size());
+  PADDLE_ENFORCE_EQ(arg_names.size(), 1UL,
+                    platform::errors::InvalidArgument(
+                        "Reader input '%s' should hold one element, but now it "
+                        "holds %d elements.",
+                        name, arg_names.size()));
   return this->GetRepeatedDims(arg_names[0]);
 }
 
 void InferShapeContext::SetReaderDims(const std::string &name,
                                       const std::vector<DDim> &dims) {
   const std::vector<std::string> &arg_names = Outputs(name);
-  PADDLE_ENFORCE_EQ(
-      arg_names.size(), 1UL,
-      "Reader output '%s' should hold one element, but now it holds %d", name,
-      arg_names.size());
+  PADDLE_ENFORCE_EQ(arg_names.size(), 1UL,
+                    platform::errors::InvalidArgument(
+                        "Reader output '%s' should hold one element, but now "
+                        "it holds %d elements.",
+                        name, arg_names.size()));
   return this->SetRepeatedDims(arg_names[0], dims);
 }
 

paddle/fluid/framework/tensor_util.cc

@@ -94,9 +94,17 @@ void TensorCopy(const Tensor& src, const platform::Place& dst_place,
     auto src_gpu_place = BOOST_GET_CONST(platform::CUDAPlace, src_place);
     auto dst_cpu_place = BOOST_GET_CONST(platform::CPUPlace, dst_place);
     auto ctx_place = ctx.GetPlace();
-    PADDLE_ENFORCE_EQ(platform::is_gpu_place(ctx_place), true);
+    PADDLE_ENFORCE_EQ(
+        platform::is_gpu_place(ctx_place), true,
+        platform::errors::PreconditionNotMet(
+            "Context place error, excepted GPUPlace, but actually %s.",
+            ctx_place));
     auto ctx_gpu_place = BOOST_GET_CONST(platform::CUDAPlace, ctx_place);
-    PADDLE_ENFORCE_EQ(src_gpu_place, ctx_gpu_place);
+    PADDLE_ENFORCE_EQ(src_gpu_place, ctx_gpu_place,
+                      platform::errors::Unavailable(
+                          "Source place and context place do not match, source "
+                          "place is %s, context place is %s.",
+                          src_gpu_place, ctx_gpu_place));
     auto stream =
         reinterpret_cast<const platform::CUDADeviceContext&>(ctx).stream();
     memory::Copy(dst_cpu_place, dst_ptr, src_gpu_place, src_ptr, size, stream);
@@ -106,9 +114,17 @@ void TensorCopy(const Tensor& src, const platform::Place& dst_place,
     auto src_cpu_place = BOOST_GET_CONST(platform::CPUPlace, src_place);
     auto dst_gpu_place = BOOST_GET_CONST(platform::CUDAPlace, dst_place);
     auto ctx_place = ctx.GetPlace();
-    PADDLE_ENFORCE_EQ(platform::is_gpu_place(ctx_place), true);
+    PADDLE_ENFORCE_EQ(
+        platform::is_gpu_place(ctx_place), true,
+        platform::errors::PreconditionNotMet(
+            "Context place error, excepted GPUPlace, but actually %s.",
+            ctx_place));
     auto ctx_gpu_place = BOOST_GET_CONST(platform::CUDAPlace, ctx_place);
-    PADDLE_ENFORCE_EQ(dst_gpu_place, ctx_gpu_place);
+    PADDLE_ENFORCE_EQ(dst_gpu_place, ctx_gpu_place,
+                      platform::errors::Unavailable(
+                          "Destination place and context place do not match, "
+                          "destination place is %s, context place is %s.",
+                          dst_gpu_place, ctx_gpu_place));
     auto stream =
         reinterpret_cast<const platform::CUDADeviceContext&>(ctx).stream();
     memory::Copy(dst_gpu_place, dst_ptr, src_cpu_place, src_ptr, size, stream);
@@ -164,7 +180,11 @@ void TensorCopy(const Tensor& src, const platform::Place& dst_place,
     auto src_gpu_place = BOOST_GET_CONST(platform::CUDAPlace, src_place);
     auto dst_gpu_place = BOOST_GET_CONST(platform::CUDAPlace, dst_place);
     auto ctx_place = ctx.GetPlace();
-    PADDLE_ENFORCE_EQ(platform::is_gpu_place(ctx_place), true);
+    PADDLE_ENFORCE_EQ(
+        platform::is_gpu_place(ctx_place), true,
+        platform::errors::PreconditionNotMet(
+            "Context place error, excepted GPUPlace, but actually %s.",
+            ctx_place));
     auto stream =
         reinterpret_cast<const platform::CUDADeviceContext&>(ctx).stream();
     if (platform::is_same_place(src_place, dst_place)) {
@@ -180,12 +200,14 @@ void TensorCopy(const Tensor& src, const platform::Place& dst_place,
         memory::Copy(dst_gpu_place, dst_ptr, src_gpu_place, src_ptr, size,
                      stream);
       } else {
-        PADDLE_THROW("ctx is not belong to dst_gpu_place or src_gpu_place.");
+        PADDLE_THROW(platform::errors::Unavailable(
+            "Context place dose not match the source and destination place."));
       }
     }
   }
   else {  // NOLINT
-    PADDLE_THROW("Copy from %s to %s is not supported.", src_place, dst_place);
+    PADDLE_THROW(platform::errors::Unimplemented(
+        "Copying from %s to %s is not supported.", src_place, dst_place));
   }
 #endif
 }
@@ -298,7 +320,8 @@ void TensorCopySync(const Tensor& src, const platform::Place& dst_place,
                  nullptr);
   }
   else {  // NOLINT
-    PADDLE_THROW("Copy from %s to %s is not supported.", src_place, dst_place);
+    PADDLE_THROW(platform::errors::Unimplemented(
+        "Copy from %s to %s is not supported.", src_place, dst_place));
   }
 #endif
 }
@@ -832,7 +855,9 @@ void TensorFromStream(std::istream& is, Tensor* tensor,
 void* GetDstPtrByDLDataType(DLDataType type, framework::Tensor* dst,
                             const platform::Place& dst_place) {
   // vector types not currently supported
-  PADDLE_ENFORCE_LE(type.lanes, 1, "vector types not currently supported");
+  PADDLE_ENFORCE_LE(type.lanes, 1,
+                    platform::errors::Unimplemented(
+                        "Vector type is not supported currently."));
 
   switch (type.bits) {
     case 8:
@@ -840,32 +865,37 @@ void* GetDstPtrByDLDataType(DLDataType type, framework::Tensor* dst,
         return static_cast<void*>(dst->mutable_data<int8_t>(dst_place));
       if (type.code == kDLUInt)
         return static_cast<void*>(dst->mutable_data<uint8_t>(dst_place));
-      PADDLE_THROW("There is no this type.code <%d> when type.bits is <%d>.",
-                   type.code, type.bits);
+      PADDLE_THROW(platform::errors::Unimplemented(
+          "DLDataType code <%d> is illegal when DLDataType.bits is <%d>.",
+          type.code, type.bits));
     case 16:
       if (type.code == kDLInt)
         return static_cast<void*>(dst->mutable_data<int16_t>(dst_place));
       if (type.code == kDLFloat)
         return static_cast<void*>(
             dst->mutable_data<paddle::platform::float16>(dst_place));
-      PADDLE_THROW("There is no this type.code <%d> when type.bits is <%d>.",
-                   type.code, type.bits);
+      PADDLE_THROW(platform::errors::Unimplemented(
+          "DLDataType code <%d> is illegal when DLDataType.bits is <%d>.",
+          type.code, type.bits));
     case 32:
       if (type.code == kDLInt)
         return static_cast<void*>(dst->mutable_data<int32_t>(dst_place));
       if (type.code == kDLFloat)
         return static_cast<void*>(dst->mutable_data<float>(dst_place));
-      PADDLE_THROW("There is no this type.code <%d> when type.bits is <%d>.",
-                   type.code, type.bits);
+      PADDLE_THROW(platform::errors::Unimplemented(
+          "DLDataType code <%d> is illegal when DLDataType.bits is <%d>.",
+          type.code, type.bits));
     case 64:
       if (type.code == kDLInt)
         return static_cast<void*>(dst->mutable_data<int64_t>(dst_place));
       if (type.code == kDLFloat)
         return static_cast<void*>(dst->mutable_data<double>(dst_place));
-      PADDLE_THROW("There is no this type.code <%d> when type.bits is <%d>.",
-                   type.code, type.bits);
+      PADDLE_THROW(platform::errors::Unimplemented(
+          "DLDataType code <%d> is illegal when DLDataType.bits is <%d>.",
+          type.code, type.bits));
     default:
-      PADDLE_THROW("Unsupport type.bits %d", type.bits);
+      PADDLE_THROW(platform::errors::Unimplemented(
+          "Unsupported DLDataType.bits %d.", type.bits));
   }
 }
 

paddle/fluid/framework/tensor_util.h

@@ -183,7 +183,11 @@ void TensorToVector(const Tensor& src, std::vector<T>* dst) {
   dst->resize(src.numel());
   auto dst_ptr = static_cast<void*>(dst->data());
 
-  PADDLE_ENFORCE_EQ(platform::is_cpu_place(src.place()), true);
+  PADDLE_ENFORCE_EQ(
+      platform::is_cpu_place(src.place()), true,
+      platform::errors::InvalidArgument(
+          "The input tensor should be CPU device, but actually it is in %s.",
+          src.place()));
 
   memory::Copy(dst_place, dst_ptr,
                BOOST_GET_CONST(platform::CPUPlace, src.place()), src_ptr, size);

paddle/fluid/inference/analysis/analyzer.cc

@@ -27,8 +27,9 @@ Analyzer::Analyzer() {}
 void Analyzer::Run(Argument *argument) { RunAnalysis(argument); }
 
 void Analyzer::RunAnalysis(Argument *argument) {
-  PADDLE_ENFORCE(argument->analysis_passes_valid(),
-                 "analsis_passes is not valid in the argument.");
+  PADDLE_ENFORCE_EQ(argument->analysis_passes_valid(), true,
+                    platform::errors::InvalidArgument(
+                        "analsis_passes is not valid in the argument."));
   const bool disable_logs = argument->disable_logs();
   for (auto &pass : argument->analysis_passes()) {
     if (!disable_logs) {
@@ -38,7 +39,8 @@ void Analyzer::RunAnalysis(Argument *argument) {
       continue;
 
     auto *ptr = PassRegistry::Global().Retreive(pass);
-    PADDLE_ENFORCE_NOT_NULL(ptr, "no analysis pass called %s", pass);
+    PADDLE_ENFORCE_NOT_NULL(ptr, platform::errors::PreconditionNotMet(
+                                     "no analysis pass called %s", pass));
     ptr->Run(argument);
   }
 }

paddle/fluid/inference/analysis/analyzer_tester.cc

@@ -75,9 +75,14 @@ void TestWord2vecPrediction(const std::string& model_path) {
   std::vector<PaddleTensor> outputs;
   CHECK(predictor->Run(slots, &outputs));
 
-  PADDLE_ENFORCE_EQ(outputs.size(), 1UL);
+  PADDLE_ENFORCE_EQ(outputs.size(), 1UL,
+                    platform::errors::PreconditionNotMet(
+                        "Output size should be 1, but got %d", outputs.size()));
   // Check the output buffer size and result of each tid.
-  PADDLE_ENFORCE_EQ(outputs.front().data.length(), 33168UL);
+  PADDLE_ENFORCE_EQ(outputs.front().data.length(), 33168UL,
+                    platform::errors::PreconditionNotMet(
+                        "Output's data length should be 33168 but got %d",
+                        outputs.front().data.length()));
   float result[5] = {0.00129761, 0.00151112, 0.000423564, 0.00108815,
                      0.000932706};
   const size_t num_elements = outputs.front().data.length() / sizeof(float);

paddle/fluid/inference/analysis/argument.h

@@ -79,7 +79,9 @@ struct Argument {
 #define DECL_ARGUMENT_FIELD(field__, Field, type__)                      \
  public:                                                                 \
   type__& field__() {                                                    \
-    PADDLE_ENFORCE(Has(#field__), "There is no such field"); \
+    PADDLE_ENFORCE_EQ(                                                   \
+        Has(#field__), true,                                             \
+        platform::errors::PreconditionNotMet("There is no such field")); \
     return field__##_;                                                   \
   }                                                                      \
   void Set##Field(const type__& x) {                                     \
@@ -98,8 +100,11 @@ struct Argument {
 #define DECL_ARGUMENT_UNIQUE_FIELD(field__, Field, type__)                    \
  public:                                                                      \
   type__& field__() {                                                         \
-    PADDLE_ENFORCE_NOT_NULL(field__##_);                                  \
-    PADDLE_ENFORCE(Has(#field__));                                        \
+    PADDLE_ENFORCE_NOT_NULL(field__##_, platform::errors::PreconditionNotMet( \
+                                            "filed should not be null."));    \
+    PADDLE_ENFORCE_EQ(                                                        \
+        Has(#field__), true,                                                  \
+        platform::errors::PreconditionNotMet("There is no such field"));      \
     return *static_cast<type__*>(field__##_.get());                           \
   }                                                                           \
   void Set##Field(type__* x) {                                                \
@@ -113,11 +118,15 @@ struct Argument {
   }                                                                           \
   DECL_ARGUMENT_FIELD_VALID(field__);                                         \
   type__* field__##_ptr() {                                                   \
-    PADDLE_ENFORCE(Has(#field__));                                        \
+    PADDLE_ENFORCE_EQ(                                                        \
+        Has(#field__), true,                                                  \
+        platform::errors::PreconditionNotMet("There is no such field"));      \
     return static_cast<type__*>(field__##_.get());                            \
   }                                                                           \
   type__* Release##Field() {                                                  \
-    PADDLE_ENFORCE(Has(#field__));                                        \
+    PADDLE_ENFORCE_EQ(                                                        \
+        Has(#field__), true,                                                  \
+        platform::errors::PreconditionNotMet("There is no such field"));      \
     valid_fields_.erase(#field__);                                            \
     return static_cast<type__*>(field__##_.release());                        \
   }                                                                           \
@@ -227,8 +236,10 @@ struct Argument {
 };
 
 #define ARGUMENT_CHECK_FIELD(argument__, fieldname__) \
-  PADDLE_ENFORCE(argument__->Has(#fieldname__),       \
-                 "the argument field [%s] should be set", #fieldname__);
+  PADDLE_ENFORCE_EQ(                                  \
+      argument__->Has(#fieldname__), true,            \
+      platform::errors::PreconditionNotMet(           \
+          "the argument field [%s] should be set", #fieldname__));
 
 }  // namespace analysis
 }  // namespace inference

paddle/fluid/inference/analysis/helper.h

@@ -73,12 +73,15 @@ struct DataTypeNamer {
   template <typename T>
   const std::string &repr() const {
     auto x = std::type_index(typeid(T));
-    PADDLE_ENFORCE(dic_.count(x), "unknown type for representation");
+    PADDLE_ENFORCE_GT(dic_.count(x), 0, platform::errors::PreconditionNotMet(
+                                            "unknown type for representation"));
     return dic_.at(x);
   }
 
   const std::string &repr(const std::type_index &type) const {  // NOLINT
-    PADDLE_ENFORCE(dic_.count(type), "unknown type for representation");
+    PADDLE_ENFORCE_GT(dic_.count(type), 0,
+                      platform::errors::PreconditionNotMet(
+                          "unknown type for representation"));
     return dic_.at(type);
   }
 
@@ -116,7 +119,9 @@ template <typename T>
 class OrderedRegistry {
  public:
   T *Register(const std::string &name, T *x) {
-    PADDLE_ENFORCE(!dic_.count(name), "duplicate key [%s]", name);
+    PADDLE_ENFORCE_EQ(dic_.count(name), 0,
+                      platform::errors::PreconditionNotMet(
+                          "There exists duplicate key [%s]", name));
     dic_[name] = elements_.size();
     elements_.emplace_back(std::unique_ptr<T>(x));
     return elements_.back().get();
@@ -136,14 +141,20 @@ class OrderedRegistry {
 template <typename T>
 T &GetFromScope(const framework::Scope &scope, const std::string &name) {
   framework::Variable *var = scope.FindVar(name);
-  PADDLE_ENFORCE(var != nullptr);
+  PADDLE_ENFORCE_NOT_NULL(
+      var, platform::errors::PreconditionNotMet(
+               "The var which name is %s should not be nullptr.", name));
   return *var->GetMutable<T>();
 }
 
 static framework::proto::ProgramDesc LoadProgramDesc(
     const std::string &model_path) {
   std::ifstream fin(model_path, std::ios::in | std::ios::binary);
-  PADDLE_ENFORCE(fin.is_open(), "Cannot open file %s", model_path);
+  PADDLE_ENFORCE_EQ(
+      fin.is_open(), true,
+      platform::errors::NotFound(
+          "Cannot open file %s, please confirm whether the file exists",
+          model_path));
   fin.seekg(0, std::ios::end);
   std::string buffer(fin.tellg(), ' ');
   fin.seekg(0, std::ios::beg);
@@ -188,10 +199,12 @@ static std::string GetDirRoot(const std::string &path) {
 static std::string GetOrCreateModelOptCacheDir(const std::string &model_root) {
   std::string opt_cache_dir = model_root + "/_opt_cache/";
   if (!PathExists(opt_cache_dir)) {
-    PADDLE_ENFORCE(MKDIR(opt_cache_dir.c_str()) != -1,
+    PADDLE_ENFORCE_NE(
+        MKDIR(opt_cache_dir.c_str()), -1,
+        platform::errors::PreconditionNotMet(
             "Can not create optimize cache directory: %s, Make sure you "
             "have permission to write",
-                   opt_cache_dir);
+            opt_cache_dir));
   }
   return opt_cache_dir;
 }

paddle/fluid/inference/analysis/ir_pass_manager.cc

@@ -38,7 +38,9 @@ IRPassManager::IRPassManager(Argument *argument) {
   graph_ = std::unique_ptr<Graph>(new Graph(argument->main_program()));
   if (argument->Has("scope")) {
     auto *scope_ptr = argument->scope_ptr();
-    PADDLE_ENFORCE(scope_ptr);
+    PADDLE_ENFORCE_NOT_NULL(scope_ptr,
+                            platform::errors::PreconditionNotMet(
+                                "The scope ptr should not be nullptr."));
     graph_->SetNotOwned(framework::ir::kParamScopeAttr, scope_ptr);
   }
 
@@ -101,13 +103,17 @@ void IRPassManager::CreatePasses(Argument *argument,
       std::string optim_cache_dir = argument->optim_cache_dir();
       bool int8_valid =
           !(model_from_memory && optim_cache_dir.empty() && enable_int8);
-      PADDLE_ENFORCE(int8_valid,
+      PADDLE_ENFORCE_EQ(
+          int8_valid, true,
+          platform::errors::PreconditionNotMet(
               "When you are in TRT INT8 mode, and load model from "
               "memory, you should set optim_cache_dir using "
-                     "config.SetOptimCacheDir()");
-      PADDLE_ENFORCE(!(model_from_memory && use_static_engine),
+              "config.SetOptimCacheDir()"));
+      PADDLE_ENFORCE_EQ(
+          !(model_from_memory && use_static_engine), true,
+          platform::errors::PreconditionNotMet(
               "When you are using Paddle-TRT, and also using load model "
-                     "from memory, you should set the use_static to false.");
+              "from memory, you should set the use_static to false."));
 
       if (!optim_cache_dir.empty()) {
         pass->Set("model_opt_cache_dir", new std::string(optim_cache_dir));

paddle/fluid/inference/analysis/ir_passes/subgraph_util.cc

@@ -123,7 +123,9 @@ void RenameAndGetOutputs(
   auto add_block_var = [&](const std::string &graph_arg,
                            const std::string &block_arg) {
     auto arg_var_node = graph_var_map.find(graph_arg);
-    PADDLE_ENFORCE(arg_var_node != graph_var_map.end());
+    PADDLE_ENFORCE_NE(arg_var_node, graph_var_map.end(),
+                      platform::errors::InvalidArgument(
+                          "Can not find %s in graph_var_map", graph_arg));
     auto *var_t = block_desc->Var(block_arg);
     var_t->SetShape(arg_var_node->second->Var()->GetShape());
     var_t->SetDataType(arg_var_node->second->Var()->GetDataType());
@@ -133,7 +135,10 @@ void RenameAndGetOutputs(
     framework::proto::OpDesc *op = block_desc->Op(index)->Proto();
     framework::OpDesc op_desc(*op, nullptr);
     auto correspond_node = subgraph_nodes[index];
-    PADDLE_ENFORCE_EQ(correspond_node->Name(), op->type());
+    PADDLE_ENFORCE_EQ(correspond_node->Name(), op->type(),
+                      platform::errors::PreconditionNotMet(
+                          "We should get %s, but get %s", op->type(),
+                          correspond_node->Name()));
 
     std::unordered_map<std::string, size_t> var2id;
     std::unordered_map<std::string, framework::ir::Node *> in_vars;

paddle/fluid/inference/analysis/ir_passes/tensorrt_subgraph_pass.cc

@@ -97,7 +97,9 @@ void TensorRtSubgraphPass::CreateTensorRTOp(
     std::vector<std::string> *repetitive_params) const {
   auto *op_desc = node->Op();
   auto &subgraph = *framework::ir::Agent(node).subgraph();
-  PADDLE_ENFORCE(!subgraph.empty());
+  PADDLE_ENFORCE_EQ(subgraph.empty(), false,
+                    platform::errors::PreconditionNotMet(
+                        "The subgraph should not be empty."));
 
   framework::ProgramDesc *program_desc =
       Get<framework::ProgramDesc *>("program");
@@ -194,12 +196,17 @@ void TensorRtSubgraphPass::CreateTensorRTOp(
   // to Tensor.
   std::vector<std::string> output_mapping;
   for (auto name : output_names) {
-    PADDLE_ENFORCE(output_name_map.count(name) != 0);
+    PADDLE_ENFORCE_NE(output_name_map.count(name), 0,
+                      platform::errors::PreconditionNotMet(
+                          "The output_name_map should have %s", name));
     output_mapping.push_back(output_name_map[name]);
   }
-  PADDLE_ENFORCE(!output_mapping.empty());
-  PADDLE_ENFORCE(!block_desc.Proto()->vars().empty(),
-                 "the block has no var-desc");
+  PADDLE_ENFORCE_EQ(output_mapping.empty(), false,
+                    platform::errors::PreconditionNotMet(
+                        "The output_mapping should not be empty."));
+  PADDLE_ENFORCE_EQ(
+      !block_desc.Proto()->vars().empty(), true,
+      platform::errors::PreconditionNotMet("the block has no var-desc"));
 
   // Set attrs
   op_desc->SetType("tensorrt_engine");

paddle/fluid/inference/analysis/passes/ir_analysis_pass.cc

@@ -13,6 +13,8 @@
 // limitations under the License.
 
 #include "paddle/fluid/inference/analysis/passes/ir_analysis_pass.h"
+#include <memory>
+#include <utility>
 #include "paddle/fluid/framework/ir/fuse_pass_base.h"
 #include "paddle/fluid/inference/analysis/ir_pass_manager.h"
 
@@ -31,7 +33,10 @@ void IrAnalysisPass::RunImpl(Argument* argument) {
   // Apply passes.
   IRPassManager the_ir_manager(argument);
   graph = the_ir_manager.Apply(std::move(graph));
-  PADDLE_ENFORCE_GT(graph->Nodes().size(), 0);
+  PADDLE_ENFORCE_GT(
+      graph->Nodes().size(), 0,
+      platform::errors::PreconditionNotMet(
+          "The graph nodes size should be greater than 0, but got 0"));
   argument->SetMainGraph(graph.release());
   CollectFusionStatis(argument);
 }

paddle/fluid/inference/analysis/passes/ir_graph_build_pass.cc

@@ -31,7 +31,9 @@ void IrGraphBuildPass::RunImpl(Argument *argument) {
   if (!argument->scope_valid()) {
     argument->SetScope(new framework::Scope);
   }
-  PADDLE_ENFORCE(argument->use_gpu_valid());
+  PADDLE_ENFORCE_EQ(argument->use_gpu_valid(), true,
+                    platform::errors::PreconditionNotMet(
+                        "The use_gpu field should be valid"));
 
   // The load program should run on the same device with the inference program,
   // so that the parameters will on the same device, or they will keep copying
@@ -51,14 +53,17 @@ void IrGraphBuildPass::RunImpl(Argument *argument) {
         argument->model_from_memory_valid() && argument->model_from_memory());
     argument->SetMainProgram(program.release());
   } else {
-    PADDLE_THROW(
-        "either model_dir or (program path and parameter path) should be set.");
+    PADDLE_THROW(platform::errors::PreconditionNotMet(
+        "either model_dir or (program path and parameter path) should be "
+        "set."));
   }
 
   auto graph = std::unique_ptr<Graph>(new Graph(argument->main_program()));
   argument->SetMainGraph(graph.release());
   auto *scope_ptr = argument->scope_ptr();
-  PADDLE_ENFORCE(scope_ptr);
+  PADDLE_ENFORCE_NOT_NULL(scope_ptr,
+                          platform::errors::PreconditionNotMet(
+                              "The scope ptr should not be nullptr."));
   argument->main_graph().SetNotOwned(framework::ir::kParamScopeAttr, scope_ptr);
 }
 

paddle/fluid/inference/analysis/passes/ir_graph_clean_pass.cc

@@ -31,7 +31,8 @@ void IrInferCleanGraphPass::RunImpl(Argument* argument) {
   std::unordered_set<const framework::ir::Node*> invalid_nodes;
   int valid_op = 0;
   for (auto* node : graph.Nodes()) {
-    PADDLE_ENFORCE_NOT_NULL(node);
+    PADDLE_ENFORCE_NOT_NULL(node, platform::errors::PreconditionNotMet(
+                                      "The node should not be nullptr."));
     if (is_valid_node(node)) {
       invalid_nodes.insert(node);
     } else if (node->IsOp()) {

paddle/fluid/inference/analysis/passes/ir_params_sync_among_devices_pass.cc

@@ -23,8 +23,12 @@ namespace inference {
 namespace analysis {
 
 void IrParamsSyncAmongDevicesPass::RunImpl(Argument *argument) {
-  PADDLE_ENFORCE(argument->scope_valid());
-  PADDLE_ENFORCE(argument->use_gpu_valid());
+  PADDLE_ENFORCE_EQ(
+      argument->scope_valid(), true,
+      platform::errors::PreconditionNotMet("The scope field should be valid"));
+  PADDLE_ENFORCE_EQ(argument->use_gpu_valid(), true,
+                    platform::errors::PreconditionNotMet(
+                        "The use_gpu field should be valid"));
 
   platform::Place place;
 
@@ -40,7 +44,9 @@ void IrParamsSyncAmongDevicesPass::RunImpl(Argument *argument) {
 
   LOG(INFO) << "Sync params from CPU to GPU";
 
-  PADDLE_ENFORCE(argument->gpu_device_id_valid());
+  PADDLE_ENFORCE_EQ(argument->gpu_device_id_valid(), true,
+                    platform::errors::PreconditionNotMet(
+                        "The gpu_device_id field should be valid"));
   place = platform::CUDAPlace(argument->gpu_device_id());
 
   auto *scope = argument->scope_ptr();
@@ -56,7 +62,8 @@ void IrParamsSyncAmongDevicesPass::RunImpl(Argument *argument) {
       continue;
     }
     auto *var = scope->FindLocalVar(var_name);
-    PADDLE_ENFORCE(var != nullptr);
+    PADDLE_ENFORCE_NOT_NULL(var, platform::errors::PreconditionNotMet(
+                                     "The var should not be nullptr"));
     if (var->IsType<framework::LoDTensor>() ||
         var->IsType<framework::Tensor>()) {
       auto *t = var->GetMutable<framework::LoDTensor>();

paddle/fluid/inference/analysis/passes/memory_optimize_pass.cc

@@ -224,7 +224,9 @@ void UpdateOpDescsByReuse(
 
       // modify the graph
       for (auto input_node : node->inputs) {
-        PADDLE_ENFORCE(input_node->IsVar());
+        PADDLE_ENFORCE_EQ(input_node->IsVar(), true,
+                          platform::errors::PreconditionNotMet(
+                              "The input node should be a variable."));
         std::string input_node_name = input_node->Name();
         if (reuse_table.count(input_node_name) &&
             reuse_table.at(input_node_name) != input_node_name) {
@@ -246,7 +248,9 @@ void UpdateOpDescsByReuse(
 
       // modify the graph
       for (auto out_node : node->outputs) {
-        PADDLE_ENFORCE(out_node->IsVar());
+        PADDLE_ENFORCE_EQ(out_node->IsVar(), true,
+                          platform::errors::PreconditionNotMet(
+                              "The output node should be a variable."));
         std::string out_node_name = out_node->Name();
         if (reuse_table.count(out_node_name) &&
             reuse_table.at(out_node_name) != out_node_name) {

paddle/fluid/inference/api/analysis_config.cc

@@ -230,7 +230,8 @@ void AnalysisConfig::EnableMkldnnBfloat16() {
 
 MkldnnQuantizerConfig *AnalysisConfig::mkldnn_quantizer_config() const {
   PADDLE_ENFORCE_NOT_NULL(mkldnn_quantizer_config_,
-                          "MkldnnQuantizer was not enabled yet.");
+                          platform::errors::PreconditionNotMet(
+                              "MkldnnQuantizer was not enabled yet."));
   return mkldnn_quantizer_config_.get();
 }
 

paddle/fluid/inference/api/analysis_predictor.cc

@@ -169,7 +169,8 @@ bool AnalysisPredictor::PrepareScope(
   if (parent_scope) {
     PADDLE_ENFORCE_NOT_NULL(
         parent_scope,
-        "Both program and parent_scope should be set in Clone mode.");
+        platform::errors::PreconditionNotMet(
+            "Both program and parent_scope should be set in Clone mode."));
     scope_ = parent_scope;
     status_is_cloned_ = true;
   } else {
@@ -235,7 +236,9 @@ bool AnalysisPredictor::PrepareExecutor() {
   executor_->Prepare(sub_scope_, *inference_program_, 0,
                      config_.use_feed_fetch_ops_);
 
-  PADDLE_ENFORCE_NOT_NULL(sub_scope_);
+  PADDLE_ENFORCE_NOT_NULL(sub_scope_,
+                          platform::errors::PreconditionNotMet(
+                              "The sub_scope should not be nullptr."));
 
   return true;
 }
@@ -297,7 +300,8 @@ bool AnalysisPredictor::Run(const std::vector<PaddleTensor> &inputs,
   timer.tic();
   // set feed variable
   framework::Scope *scope = sub_scope_ ? sub_scope_ : scope_.get();
-  PADDLE_ENFORCE_NOT_NULL(scope, "The scope should not be nullptr.");
+  PADDLE_ENFORCE_NOT_NULL(scope, platform::errors::PreconditionNotMet(
+                                     "The scope should not be nullptr."));
   if (!SetFeed(inputs, scope)) {
     LOG(ERROR) << "fail to set feed";
     return false;
@@ -399,7 +403,11 @@ bool AnalysisPredictor::GetFetch(std::vector<PaddleTensor> *outputs,
   outputs->resize(fetches_.size());
   for (size_t i = 0; i < fetches_.size(); ++i) {
     int idx = BOOST_GET_CONST(int, fetches_[i]->GetAttr("col"));
-    PADDLE_ENFORCE((size_t)idx == i);
+    PADDLE_ENFORCE_EQ(
+        static_cast<size_t>(idx), i,
+        platform::errors::InvalidArgument(
+            "Fetch op's col attr(%d) should be equal to the index(%d)", idx,
+            i));
     framework::FetchType &fetch_var =
         framework::GetFetchVariable(*scope, "fetch", idx);
     auto &fetch = BOOST_GET(framework::LoDTensor, fetch_var);
@@ -435,10 +443,12 @@ void AnalysisPredictor::PrepareArgument() {
   if (!config_.model_dir().empty()) {
     argument_.SetModelDir(config_.model_dir());
   } else {
-    PADDLE_ENFORCE(
-        !config_.params_file().empty(),
-        "Either model_dir or (param_file, prog_file) should be set.");
-    PADDLE_ENFORCE(!config_.prog_file().empty());
+    PADDLE_ENFORCE_EQ(config_.params_file().empty(), false,
+                      platform::errors::PreconditionNotMet(
+                          "Either model_dir or param_file should be set."));
+    PADDLE_ENFORCE_EQ(config_.prog_file().empty(), false,
+                      platform::errors::PreconditionNotMet(
+                          "Either model_dir or prog_file should be set."));
     std::string dir = inference::analysis::GetDirRoot(config_.prog_file());
 
     argument_.SetModelProgramPath(config_.prog_file());
@@ -503,7 +513,9 @@ void AnalysisPredictor::OptimizeInferenceProgram() {
   PrepareArgument();
   Analyzer().Run(&argument_);
 
-  PADDLE_ENFORCE(argument_.scope_valid());
+  PADDLE_ENFORCE_EQ(
+      argument_.scope_valid(), true,
+      platform::errors::InvalidArgument("The argument scope should be valid."));
   VLOG(5) << "to prepare executor";
   ARGUMENT_CHECK_FIELD((&argument_), ir_analyzed_program);
   inference_program_.reset(
@@ -525,8 +537,10 @@ std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
     FLAGS_minloglevel = 2;  // GLOG_ERROR
   }
   VLOG(3) << "create AnalysisConfig";
-  PADDLE_ENFORCE(config.is_valid(),
-                 "Note: Each config can only be used for one predictor.");
+  PADDLE_ENFORCE_EQ(
+      config.is_valid(), true,
+      platform::errors::InvalidArgument(
+          "Note: Each config can only be used for one predictor."));
 
   if (config.use_gpu()) {
     static std::once_flag gflags_initialized;
@@ -623,7 +637,9 @@ bool AnalysisPredictor::MkldnnQuantize() {
 }
 
 void AnalysisPredictor::PrepareFeedFetch() {
-  PADDLE_ENFORCE_NOT_NULL(sub_scope_);
+  PADDLE_ENFORCE_NOT_NULL(sub_scope_,
+                          platform::errors::InvalidArgument(
+                              "The sub_scope should not be nullptr."));
   CreateFeedFetchVar(sub_scope_);
   for (auto *op : inference_program_->Block(0).AllOps()) {
     if (op->Type() == "feed") {
@@ -646,7 +662,8 @@ void AnalysisPredictor::PrepareFeedFetch() {
 }
 
 void AnalysisPredictor::CreateFeedFetchVar(framework::Scope *scope) {
-  PADDLE_ENFORCE_NOT_NULL(scope);
+  PADDLE_ENFORCE_NOT_NULL(scope, platform::errors::InvalidArgument(
+                                     "The scope should not be nullptr."));
   auto *var = scope->Var("feed");
   var->GetMutable<framework::FeedList>();
   var = scope->Var("fetch");
@@ -667,7 +684,8 @@ AnalysisPredictor::GetInputTensorShape() {
   std::vector<std::string> names = GetInputNames();
   for (std::string name : names) {
     auto *var = inference_program_->Block(0).FindVar(name);
-    PADDLE_ENFORCE_NOT_NULL(var, "input %s does not exist.", name);
+    PADDLE_ENFORCE_NOT_NULL(var, platform::errors::PreconditionNotMet(
+                                     "Input %s does not exist.", name));
     input_shapes[name] = var->GetShape();
   }
   return input_shapes;
@@ -683,7 +701,11 @@ std::vector<std::string> AnalysisPredictor::GetOutputNames() {
 
 std::unique_ptr<ZeroCopyTensor> AnalysisPredictor::GetInputTensor(
     const std::string &name) {
-  PADDLE_ENFORCE(executor_->scope()->FindVar(name), "no name called %s", name);
+  PADDLE_ENFORCE_NOT_NULL(
+      executor_->scope()->FindVar(name),
+      platform::errors::PreconditionNotMet(
+          "The variable named %s is not found in the scope of the exector.",
+          name));
   std::unique_ptr<ZeroCopyTensor> res(
       new ZeroCopyTensor(static_cast<void *>(executor_->scope())));
   res->input_or_output_ = true;
@@ -700,7 +722,11 @@ std::unique_ptr<ZeroCopyTensor> AnalysisPredictor::GetInputTensor(
 
 std::unique_ptr<ZeroCopyTensor> AnalysisPredictor::GetOutputTensor(
     const std::string &name) {
-  PADDLE_ENFORCE(executor_->scope()->FindVar(name), "no name called %s", name);
+  PADDLE_ENFORCE_NOT_NULL(
+      executor_->scope()->FindVar(name),
+      platform::errors::PreconditionNotMet(
+          "he variable named %s is not found in the scope of the exector.",
+          name));
   std::unique_ptr<ZeroCopyTensor> res(
       new ZeroCopyTensor(static_cast<void *>(executor_->scope())));
   res->input_or_output_ = false;
@@ -761,8 +787,11 @@ bool AnalysisPredictor::LoadProgramDesc() {
     std::string pb_content;
     // Read binary
     std::ifstream fin(filename, std::ios::in | std::ios::binary);
-    PADDLE_ENFORCE(static_cast<bool>(fin.is_open()), "Cannot open file %s",
-                   filename);
+    PADDLE_ENFORCE_EQ(
+        static_cast<bool>(fin.is_open()), true,
+        platform::errors::NotFound(
+            "Cannot open file %s, please confirm whether the file is normal.",
+            filename));
     fin.seekg(0, std::ios::end);
     pb_content.resize(fin.tellg());
     fin.seekg(0, std::ios::beg);
@@ -779,7 +808,8 @@ bool AnalysisPredictor::LoadProgramDesc() {
 
 bool AnalysisPredictor::LoadParameters() {
   PADDLE_ENFORCE_NOT_NULL(inference_program_.get(),
-                          "The inference program should be loaded first.");
+                          platform::errors::PreconditionNotMet(
+                              "The inference program should be loaded first."));
 
   const auto &global_block = inference_program_->MutableBlock(0);
 
@@ -855,8 +885,9 @@ void AnalysisPredictor::ClearIntermediateTensor() {
 
 #if PADDLE_WITH_TENSORRT
 bool AnalysisPredictor::SaveTrtCalibToDisk() {
-  PADDLE_ENFORCE(config_.tensorrt_engine_enabled(),
-                 "This func can be invoked only in trt mode");
+  PADDLE_ENFORCE_EQ(config_.tensorrt_engine_enabled(), true,
+                    platform::errors::PreconditionNotMet(
+                        "This func can be invoked only in trt mode"));
   auto &block = inference_program_->Block(0);
   for (auto &op_desc : block.AllOps()) {
     if (op_desc->Type() == "tensorrt_engine") {

paddle/fluid/inference/api/api.cc

@@ -62,9 +62,9 @@ PaddleBuf &PaddleBuf::operator=(const PaddleBuf &other) {
     if (other.length() && other.data())
       memcpy(data_, other.data(), other.length());
     else if (other.length())
-      PADDLE_THROW(
+      PADDLE_THROW(platform::errors::InvalidArgument(
           "Invalid argument, null pointer data with length %u is passed",
-          other.length());
+          other.length()));
 
     length_ = other.length();
     memory_owned_ = true;
@@ -92,7 +92,8 @@ void PaddleBuf::Resize(size_t length) {
     length_ = length;
     memory_owned_ = true;
   } else {
-    PADDLE_THROW("The memory is allocated externally, can not Resized");
+    PADDLE_THROW(platform::errors::PreconditionNotMet(
+        "The memory is allocated externally, can not Resized"));
   }
 }
 
@@ -105,7 +106,11 @@ void PaddleBuf::Reset(void *data, size_t length) {
 
 void PaddleBuf::Free() {
   if (memory_owned_ && data_) {
-    PADDLE_ENFORCE_GT(length_, 0UL);
+    PADDLE_ENFORCE_GT(
+        length_, 0UL,
+        platform::errors::PreconditionNotMet(
+            "The memory used in PaddleBuf %d should be greater than 0",
+            length_));
     delete[] static_cast<char *>(data_);
     data_ = nullptr;
     length_ = 0;

paddle/fluid/inference/api/api_impl.cc

@@ -87,7 +87,9 @@ bool NativePaddlePredictor::Init(
   if (parent_scope) {
     scope_ = parent_scope;
     sub_scope_ = &(parent_scope->NewScope());
-    PADDLE_ENFORCE_NOT_NULL(sub_scope_, "create sub scope fail");
+    PADDLE_ENFORCE_NOT_NULL(sub_scope_,
+                            platform::errors::PreconditionNotMet(
+                                "The sub_scope should not be nullptr."));
   } else {
     paddle::framework::InitDevices(false);
     scope_.reset(new paddle::framework::Scope());
@@ -182,7 +184,10 @@ std::unique_ptr<PaddlePredictor> NativePaddlePredictor::Clone() {
   std::unique_ptr<PaddlePredictor> cls(new NativePaddlePredictor(config_));
   // Hot fix the bug that result diff in multi-thread.
   // TODO(Superjomn) re-implement a real clone here.
-  PADDLE_ENFORCE_NOT_NULL(dynamic_cast<NativePaddlePredictor *>(cls.get()));
+  PADDLE_ENFORCE_NOT_NULL(
+      dynamic_cast<NativePaddlePredictor *>(cls.get()),
+      platform::errors::PreconditionNotMet(
+          "Dynamic_cast from PaddlePredictor to NativePaddlePredictor failed"));
   if (!dynamic_cast<NativePaddlePredictor *>(cls.get())->Init(nullptr)) {
     LOG(ERROR) << "fail to call Init";
     return nullptr;
@@ -224,8 +229,13 @@ bool NativePaddlePredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
       return false;
     }
 
-    PADDLE_ENFORCE_NOT_NULL(input_ptr);
-    PADDLE_ENFORCE_NOT_NULL(inputs[i].data.data());
+    PADDLE_ENFORCE_NOT_NULL(input_ptr,
+                            platform::errors::InvalidArgument(
+                                "The input_ptr should not be nullptr."));
+    PADDLE_ENFORCE_NOT_NULL(
+        inputs[i].data.data(),
+        platform::errors::InvalidArgument(
+            "The data of input tensor should not be null."));
     if (platform::is_cpu_place(place_)) {
       // TODO(panyx0718): Init LoDTensor from existing memcpy to save a copy.
       std::memcpy(static_cast<void *>(input_ptr), inputs[i].data.data(),
@@ -241,7 +251,8 @@ bool NativePaddlePredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
                    platform::CPUPlace(), inputs[i].data.data(),
                    inputs[i].data.length(), dev_ctx->stream());
 #else
-      PADDLE_THROW("Not compile with CUDA, should not reach here.");
+      PADDLE_THROW(platform::errors::Unavailable(
+          "Not compile with CUDA, should not reach here."));
 #endif
     }
 
@@ -287,7 +298,11 @@ bool NativePaddlePredictor::GetFetch(std::vector<PaddleTensor> *outputs,
   outputs->resize(fetchs_.size());
   for (size_t i = 0; i < fetchs_.size(); ++i) {
     int idx = BOOST_GET_CONST(int, fetchs_[i]->GetAttr("col"));
-    PADDLE_ENFORCE((size_t)idx == i);
+    PADDLE_ENFORCE_EQ(
+        static_cast<size_t>(idx), i,
+        platform::errors::InvalidArgument(
+            "Fetch op's col attr(%d) should be equal to the index(%d)", idx,
+            i));
     framework::FetchType &fetch_var =
         framework::GetFetchVariable(*scope, "fetch", idx);
     auto fetch = BOOST_GET_CONST(framework::LoDTensor, fetch_var);
@@ -318,10 +333,15 @@ std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
   VLOG(3) << "create NativePaddlePredictor";
   if (config.use_gpu) {
     // 1. GPU memory
-    PADDLE_ENFORCE_GE(
-        config.fraction_of_gpu_memory, 0.f,
-        "fraction_of_gpu_memory in the config should be set to range (0., 1.]");
-    PADDLE_ENFORCE_GE(config.device, 0, "Invalid device id %d", config.device);
+    PADDLE_ENFORCE_GE(config.fraction_of_gpu_memory, 0.f,
+                      platform::errors::InvalidArgument(
+                          "fraction_of_gpu_memory in the config should be set "
+                          "to range (0., 1.]"));
+    PADDLE_ENFORCE_GE(config.device, 0,
+                      platform::errors::PreconditionNotMet(
+                          "Invalid device id %d, the device id should be "
+                          "greater than or equal to 0.",
+                          config.device));
     std::vector<std::string> flags;
     if (config.fraction_of_gpu_memory >= 0.0f ||
         config.fraction_of_gpu_memory <= 0.95f) {
@@ -336,7 +356,9 @@ std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
 
   std::unique_ptr<PaddlePredictor> predictor(new NativePaddlePredictor(config));
   PADDLE_ENFORCE_NOT_NULL(
-      dynamic_cast<NativePaddlePredictor *>(predictor.get()));
+      dynamic_cast<NativePaddlePredictor *>(predictor.get()),
+      platform::errors::PreconditionNotMet(
+          "Dynamic_cast from PaddlePredictor to NativePaddlePredictor failed"));
   if (!dynamic_cast<NativePaddlePredictor *>(predictor.get())->Init(nullptr)) {
     return nullptr;
   }

paddle/fluid/inference/api/helper.h

@@ -112,16 +112,19 @@ static T convert(const std::string &item,
     std::string message =
         "invalid_argument exception when try to convert : " + item;
     LOG(ERROR) << message;
-    PADDLE_THROW(message);
+    PADDLE_THROW(platform::errors::InvalidArgument(
+        "invalid_argument exception when try to convert %s.", item));
   } catch (std::out_of_range &e) {
     std::string message =
         "out_of_range exception when try to convert : " + item;
     LOG(ERROR) << message;
-    PADDLE_THROW(message);
+    PADDLE_THROW(platform::errors::InvalidArgument(
+        "out_of_range exception when try to convert %s.", item));
   } catch (...) {
     std::string message = "unexpected exception when try to convert " + item;
     LOG(ERROR) << message;
-    PADDLE_THROW(message);
+    PADDLE_THROW(platform::errors::InvalidArgument(
+        "unexpected exception when try to convert %s.", item));
   }
   return res;
 }
@@ -353,7 +356,8 @@ static void PrintTime(int batch_size, int repeat, int num_threads, int tid,
                       double batch_latency, int epoch = 1,
                       const framework::proto::VarType::Type data_type =
                           framework::proto::VarType::FP32) {
-  PADDLE_ENFORCE_GT(batch_size, 0, "Non-positive batch size.");
+  PADDLE_ENFORCE_GT(batch_size, 0, platform::errors::InvalidArgument(
+                                       "Non-positive batch size."));
   double sample_latency = batch_latency / batch_size;
   LOG(INFO) << "====== threads: " << num_threads << ", thread id: " << tid
             << " ======";

paddle/fluid/inference/api/mkldnn_quantizer.cc

@@ -62,9 +62,12 @@ bool AnalysisPredictor::MkldnnQuantizer::CalculateScales() {
             if (scales_.find(var_name) != scales_.end()) continue;
 
             auto* var = predictor_.sub_scope_->FindVar(var_name);
-            PADDLE_ENFORCE(var, "%s is not in the scope", var_name);
-            PADDLE_ENFORCE(var->IsType<LoDTensor>(),
-                           "Only support lod tensor now.");
+            PADDLE_ENFORCE_NOT_NULL(var,
+                                    platform::errors::PreconditionNotMet(
+                                        "%s is not in the scope", var_name));
+            PADDLE_ENFORCE_EQ(var->IsType<LoDTensor>(), true,
+                              platform::errors::PreconditionNotMet(
+                                  "Only support lod tensor now."));
             LoDTensor* var_tensor = var->GetMutable<LoDTensor>();
 
             // force unsigned type if already know it
@@ -82,9 +85,11 @@ bool AnalysisPredictor::MkldnnQuantizer::CalculateScales() {
               } else if (op->Type() == "transpose2" ||
                          op->Type() == "reshape2" || op->Type() == "pool2d") {
                 auto input_var_name = op->Input("X")[0];
-                PADDLE_ENFORCE(scales_.find(input_var_name) != scales_.end(),
+                PADDLE_ENFORCE_NE(
+                    scales_.find(input_var_name), scales_.end(),
+                    platform::errors::PreconditionNotMet(
                         "Input scales must be calculated before the "
-                               "output scales to infer if output is unsigned.");
+                        "output scales to infer if output is unsigned."));
                 if (scales_.find(input_var_name) != scales_.end()) {
                   scales_[var_name] = scales_[input_var_name];
                 }
@@ -94,10 +99,11 @@ bool AnalysisPredictor::MkldnnQuantizer::CalculateScales() {
                 is_unsigned = true;
                 double min_scale = std::numeric_limits<double>::max();
                 for (auto input_var_name : op->Input("X")) {
-                  PADDLE_ENFORCE(
-                      scales_.find(input_var_name) != scales_.end(),
+                  PADDLE_ENFORCE_NE(
+                      scales_.find(input_var_name), scales_.end(),
+                      platform::errors::PreconditionNotMet(
                           "Input scales must be calculated before the "
-                      "output scales to infer if output is unsigned.");
+                          "output scales to infer if output is unsigned."));
                   is_unsigned = is_unsigned && scales_[input_var_name].first;
                   min_scale = std::min(
                       min_scale,
@@ -132,11 +138,12 @@ void AnalysisPredictor::MkldnnQuantizer::CalculateSingleScale(
   auto rule = qconfig_->scale_algo(op_type_name, conn_name);
   if (rule == ScaleAlgo::NONE) return;
 
-  PADDLE_ENFORCE(
-      var_tensor.numel() > 0,
+  PADDLE_ENFORCE_GT(
+      var_tensor.numel(), 0,
+      platform::errors::InvalidArgument(
           "MkldnnQuantizer: LoDTensor of variable %s for quantization of op "
           "%s of connection %s should not be empty.",
-      var_name, op_type_name, conn_name);
+          var_name, op_type_name, conn_name));
 
   switch (rule) {
     case ScaleAlgo::MAX:
@@ -205,10 +212,11 @@ AnalysisPredictor::MkldnnQuantizer::GetKLScalingFactor(
   float min_val = eigen_tensor.minCoeff();
   bool is_positive = min_val >= 0.0f;
   if (is_unsigned)
-    PADDLE_ENFORCE(
-        is_positive,
+    PADDLE_ENFORCE_EQ(
+        is_positive, true,
+        platform::errors::InvalidArgument(
             "Tensor is claimed to be unsigned, but its min value (%f) is < 0.0",
-        min_val);
+            min_val));
 
   int num_quantized_bins = 255;
 
@@ -316,10 +324,11 @@ AnalysisPredictor::MkldnnQuantizer::GetMaxScalingFactor(
   float max_abs = eigen_tensor.abs().maxCoeff();
   float min_val = eigen_tensor.minCoeff();
   if (is_unsigned)
-    PADDLE_ENFORCE(
-        min_val >= 0.0f,
+    PADDLE_ENFORCE_GE(
+        min_val, 0.0f,
+        platform::errors::InvalidArgument(
             "Tensor is claimed to be unsigned, but its min value (%f) is < 0.0",
-        min_val);
+            min_val));
 
   LoDTensor scale_tensor = CreateScaleTensor();
   scale_tensor.data<double>()[0] = 1.0 / max_abs;
@@ -330,16 +339,19 @@ AnalysisPredictor::MkldnnQuantizer::GetMaxScalingFactor(
 std::pair<bool, LoDTensor>
 AnalysisPredictor::MkldnnQuantizer::GetMaxChScalingFactor(
     const LoDTensor& var_tensor, bool is_unsigned, bool is_transposed) const {
-  PADDLE_ENFORCE(var_tensor.dims().size() > 0, "Tensor dimension is empty.");
+  PADDLE_ENFORCE_GT(
+      var_tensor.dims().size(), 0,
+      platform::errors::InvalidArgument("Tensor dimension is empty."));
 
   ConstEigenVectorArrayMap eigen_tensor{var_tensor.data<float>(),
                                         var_tensor.numel(), 1};
   float min_val = eigen_tensor.minCoeff();
   if (is_unsigned)
-    PADDLE_ENFORCE(
-        min_val >= 0.0f,
+    PADDLE_ENFORCE_GE(
+        min_val, 0.0f,
+        platform::errors::InvalidArgument(
             "Tensor is claimed to be unsigned, but its min value (%f) is < 0.0",
-        min_val);
+            min_val));
 
   auto dims = var_tensor.dims();
   constexpr int num_col_dims = 1;
@@ -367,17 +379,19 @@ AnalysisPredictor::MkldnnQuantizer::Histogram(
     const framework::LoDTensor& var_tensor, float min_val, float max_val,
     size_t num_bins) const {
   PADDLE_ENFORCE_GT(num_bins, 0,
+                    platform::errors::InvalidArgument(
                         "MkldnnQuantizer: To calculate Histogram, num_bins (" +
-                        std::to_string(num_bins) + ") must be positive.");
-  PADDLE_ENFORCE_GT(
-      var_tensor.numel(), 0,
-      "MkldnnQuantizer: To calculate Histogram, the tensor must not be empty.");
-  PADDLE_ENFORCE(max_val >= min_val,
+                        std::to_string(num_bins) + ") must be positive."));
+  PADDLE_ENFORCE_GT(var_tensor.numel(), 0,
+                    platform::errors::InvalidArgument(
+                        "MkldnnQuantizer: To calculate Histogram, the tensor "
+                        "must not be empty."));
+  PADDLE_ENFORCE_GE(max_val, min_val,
+                    platform::errors::InvalidArgument(
                         "MkldnnQuantizer: To calculate Histogram, max_val (" +
-                     std::to_string(max_val) +
-                     ") must be greater or equal"
+                        std::to_string(max_val) + ") must be greater or equal"
                                                   "to min_val (" +
-                     std::to_string(min_val) + ").");
+                        std::to_string(min_val) + ")."));
   ConstEigenVectorArrayMap eigen_tensor{var_tensor.data<float>(),
                                         var_tensor.numel(), 1};
   auto bin_width = std::abs(max_val - min_val) / num_bins;
@@ -407,7 +421,8 @@ void AnalysisPredictor::MkldnnQuantizer::PrepareArgument() const {
   auto graph = std::unique_ptr<Graph>(new Graph(arg.main_program()));
   arg.SetMainGraph(graph.release());
   auto* scope_ptr = arg.scope_ptr();
-  PADDLE_ENFORCE(scope_ptr);
+  PADDLE_ENFORCE_NOT_NULL(scope_ptr, platform::errors::PreconditionNotMet(
+                                         "The scope should not be nullptr."));
   arg.main_graph().SetNotOwned(framework::ir::kParamScopeAttr, scope_ptr);
 
   auto* builder = predictor_.config_.pass_builder();
@@ -441,7 +456,9 @@ bool AnalysisPredictor::MkldnnQuantizer::RunQuantizePasses() const {
   PrepareArgument();
   auto& arg = predictor_.argument_;
   Analyzer().Run(&arg);
-  PADDLE_ENFORCE(arg.scope_valid());
+  PADDLE_ENFORCE_EQ(
+      arg.scope_valid(), true,
+      platform::errors::PreconditionNotMet("The scope should be valid."));
   VLOG(5) << "to prepare executor";
   ARGUMENT_CHECK_FIELD((&arg), ir_analyzed_program);
   predictor_.inference_program_.reset(
@@ -456,7 +473,8 @@ bool AnalysisPredictor::MkldnnQuantizer::RunWarmup() const {
   VLOG(3) << "Predictor: run a quantization warmup iteration";
   auto warmup_data = qconfig_->warmup_data();
   PADDLE_ENFORCE_NOT_NULL(warmup_data,
-                          "Warmup data cannot be NULL in the config.");
+                          platform::errors::PreconditionNotMet(
+                              "Warmup data cannot be NULL in the config."));
   PrettyLogH1("--- Running warmup iteration for quantization");
 
   // Run the inference program
@@ -469,7 +487,10 @@ bool AnalysisPredictor::MkldnnQuantizer::RunWarmup() const {
 float AnalysisPredictor::MkldnnQuantizer::SafeEntropy(
     std::vector<int> reference_distr_P, int P_sum,
     std::vector<int> candidate_distr_Q, int Q_sum) const {
-  PADDLE_ENFORCE_EQ(reference_distr_P.size(), candidate_distr_Q.size());
+  PADDLE_ENFORCE_EQ(reference_distr_P.size(), candidate_distr_Q.size(),
+                    platform::errors::InvalidArgument(
+                        "The P size %d should be equal to Q size %d",
+                        reference_distr_P.size(), candidate_distr_Q.size()));
   float tmp_sum1 = 0;
   float tmp_sum2 = 0;
   for (size_t idx = 0; idx < reference_distr_P.size(); idx++) {
@@ -479,10 +500,11 @@ float AnalysisPredictor::MkldnnQuantizer::SafeEntropy(
       tmp_sum1 += 0;
       tmp_sum2 += 0;
     } else {
-      PADDLE_ENFORCE(q_idx != 0, "MkldnnQuantizer: Fatal error!, idx = " +
-                                     std::to_string(idx) +
-                                     " qindex = 0! p_idx = " +
-                                     std::to_string(p_idx));
+      PADDLE_ENFORCE_NE(
+          q_idx, 0,
+          platform::errors::PreconditionNotMet(
+              "MkldnnQuantizer: Fatal error!, idx = " + std::to_string(idx) +
+              " qindex = 0! p_idx = " + std::to_string(p_idx)));
     }
     tmp_sum1 += p_idx * (log(Q_sum * p_idx));
     tmp_sum2 += p_idx * (log(P_sum * q_idx));

paddle/fluid/inference/api/paddle_pass_builder.cc

@@ -231,6 +231,10 @@ void CpuPassStrategy::EnableMkldnnQuantizer() {
 
 void CpuPassStrategy::EnableMkldnnBfloat16() {
 #ifdef PADDLE_WITH_MKLDNN
+  if (!use_mkldnn_bfloat16_) {
+    passes_.push_back("cpu_bfloat16_placement_pass");
+    passes_.push_back("cpu_bfloat16_pass");
+  }
   use_mkldnn_bfloat16_ = true;
 #else
   use_mkldnn_bfloat16_ = false;

paddle/fluid/inference/tensorrt/convert/concat_op.cc

@@ -34,8 +34,11 @@ class ConcatOpConverter : public OpConverter {
       itensors.push_back(engine_->GetITensor(input_name));
     }
     int axis = BOOST_GET_CONST(int, op_desc.GetAttr("axis"));
-    PADDLE_ENFORCE(axis > 0,
-                   "The axis attr of Concat op should be large than 0 for trt");
+    PADDLE_ENFORCE_GT(axis, 0, platform::errors::InvalidArgument(
+                                   "The axis attr of Concat"
+                                   " op should be larger than 0 for trt. "
+                                   "But received %d.",
+                                   axis));
 
     auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Concatenation, itensors.data(),
                                        itensors.size());

paddle/fluid/inference/tensorrt/convert/conv2d_op.cc

@@ -100,7 +100,9 @@ void ConvertConv2d(TensorRTEngine* engine, const framework::proto::OpDesc& op,
   TensorRTEngine::Weight bias{nvinfer1::DataType::kFLOAT, nullptr, 0};
   auto* layer = fadd_layer(const_cast<nvinfer1::ITensor*>(X), n_output, n_input,
                            nv_ksize, weight, bias);
-  PADDLE_ENFORCE(layer != nullptr);
+  PADDLE_ENFORCE_NOT_NULL(layer,
+                          platform::errors::Fatal("TensorRT create conv2d"
+                                                  " layer error."));
   layer->setStride(nv_strides);
   layer->setPadding(nv_paddings);
   layer->setNbGroups(groups);

paddle/fluid/inference/tensorrt/convert/elementwise_op.cc

@@ -43,13 +43,30 @@ class ElementwiseWeightOpConverter : public OpConverter {
     framework::OpDesc op_desc(op, nullptr);
     VLOG(3) << "Convert a fluid elementwise op to TensorRT IScaleLayer";
 
-    PADDLE_ENFORCE_EQ(op_desc.Input("X").size(), 1);
-    PADDLE_ENFORCE_EQ(op_desc.Input("Y").size(), 1);  // Y is a weight
-    PADDLE_ENFORCE_EQ(op_desc.Output("Out").size(), 1);
+    PADDLE_ENFORCE_EQ(
+        op_desc.Input("X").size(), 1,
+        platform::errors::InvalidArgument(
+            "The input op's Input(\"X\").size() "
+            "should equal to 1, but received Input(\"X\").size() = %u.",
+            op_desc.Input("X").size()));
+    PADDLE_ENFORCE_EQ(
+        op_desc.Input("Y").size(), 1,
+        platform::errors::InvalidArgument(
+            "The input op's Input(\"Y\").size() "
+            "should equal to 1, but received Input(\"Y\").size() = %u.",
+            op_desc.Input("Y").size()));  // Y is a weight
+    PADDLE_ENFORCE_EQ(
+        op_desc.Output("Out").size(), 1,
+        platform::errors::InvalidArgument(
+            "The input op's Output(\"Out\").size() "
+            "should equal to 1, but reveceid Output(\"Out\").size() = %u.",
+            op_desc.Output("Out").size()));
 
     auto* X = engine_->GetITensor(op_desc.Input("X").front());
     auto* Y_v = scope.FindVar(op_desc.Input("Y").front());
-    PADDLE_ENFORCE_NOT_NULL(Y_v);
+    PADDLE_ENFORCE_NOT_NULL(
+        Y_v, platform::errors::NotFound("Variable %s not found in scope.",
+                                        op_desc.Input("Y").front().c_str()));
     auto* Y_t = Y_v->GetMutable<framework::LoDTensor>();
     float* weight_data = nullptr;
     weight_data =
@@ -176,9 +193,24 @@ class ElementwiseTensorOpConverter : public OpConverter {
     framework::OpDesc op_desc(op, nullptr);
     nvinfer1::ILayer* layer = nullptr;
 
-    PADDLE_ENFORCE_EQ(op_desc.Input("X").size(), 1);
-    PADDLE_ENFORCE_EQ(op_desc.Input("Y").size(), 1);  // Y is a weight
-    PADDLE_ENFORCE_EQ(op_desc.Output("Out").size(), 1);
+    PADDLE_ENFORCE_EQ(
+        op_desc.Input("X").size(), 1,
+        platform::errors::InvalidArgument(
+            "The input op's Input(\"X\").size() "
+            "should equal to 1, but received Input(\"X\").size() = %u.",
+            op_desc.Input("X").size()));
+    PADDLE_ENFORCE_EQ(
+        op_desc.Input("Y").size(), 1,
+        platform::errors::InvalidArgument(
+            "The input op's Input(\"Y\").size() "
+            "should equal to 1, but received Input(\"Y\").size() = %u.",
+            op_desc.Input("Y").size()));  // Y is a weight
+    PADDLE_ENFORCE_EQ(
+        op_desc.Output("Out").size(), 1,
+        platform::errors::InvalidArgument(
+            "The input op's Output(\"Out\").size() "
+            "should equal to 1, but received Output(\"Out\").size() = %u.",
+            op_desc.Output("Out").size()));
 
     auto* X = engine_->GetITensor(op_desc.Input("X").front());
     auto* Y = engine_->GetITensor(op_desc.Input("Y").front());

paddle/fluid/inference/tensorrt/convert/io_converter.cc

@@ -29,38 +29,67 @@ class DefaultIOConverter : public EngineIOConverter {
   // NOTE out is GPU memory.
   virtual void operator()(const LoDTensor& in, void* out,
                           size_t max_size) override {
-    PADDLE_ENFORCE(out != nullptr);
-    PADDLE_ENFORCE(stream_ != nullptr);
+    PADDLE_ENFORCE_NOT_NULL(out,
+                            platform::errors::InvalidArgument(
+                                "The input param 'out' must not be nullptr."));
+    PADDLE_ENFORCE_NOT_NULL(stream_,
+                            platform::errors::PreconditionNotMet(
+                                "You should set up stream_ by SetStream() "
+                                "before you call the operator()."));
     const auto& place = in.place();
     size_t size = in.memory_size();
-    PADDLE_ENFORCE_LE(size, max_size);
+    PADDLE_ENFORCE_LE(
+        size, max_size,
+        platform::errors::InvalidArgument(
+            "The input Tensor in's memory_size shoule be less than or equal to "
+            "the input max_size. But in's memory_size = %u, max_size = %u.",
+            size, max_size));
     if (is_cpu_place(place)) {
-      PADDLE_ENFORCE_EQ(0, cudaMemcpyAsync(out, in.data<float>(), size,
-                                           cudaMemcpyHostToDevice, *stream_));
+      PADDLE_ENFORCE_CUDA_SUCCESS(cudaMemcpyAsync(
+          out, in.data<float>(), size, cudaMemcpyHostToDevice, *stream_));
     } else if (is_gpu_place(place)) {
-      PADDLE_ENFORCE_EQ(0, cudaMemcpyAsync(out, in.data<float>(), size,
-                                           cudaMemcpyDeviceToDevice, *stream_));
+      PADDLE_ENFORCE_EQ(
+          0, cudaMemcpyAsync(out, in.data<float>(), size,
+                             cudaMemcpyDeviceToDevice, *stream_),
+          platform::errors::External(
+              "cudaMemcpyAsync(cudaMemcpyDeviceToDevice) error."));
     } else {
-      PADDLE_THROW("Unknown device for converter");
+      PADDLE_THROW(platform::errors::NotFound("Unknown device for converter"));
     }
     cudaStreamSynchronize(*stream_);
   }
   // NOTE in is GPU memory.
   virtual void operator()(const void* in, LoDTensor* out,
                           size_t max_size) override {
-    PADDLE_ENFORCE(in != nullptr);
-    PADDLE_ENFORCE(stream_ != nullptr);
+    PADDLE_ENFORCE_NOT_NULL(in,
+                            platform::errors::InvalidArgument(
+                                "The input param 'in' must not be nullptr."));
+    PADDLE_ENFORCE_NOT_NULL(stream_,
+                            platform::errors::PreconditionNotMet(
+                                "You should set up stream_ by SetStream() "
+                                "before you call the operator()."));
     const auto& place = out->place();
     size_t size = out->memory_size();
-    PADDLE_ENFORCE_LE(size, max_size);
+    PADDLE_ENFORCE_LE(
+        size, max_size,
+        platform::errors::InvalidArgument(
+            "The input Tensor out's memory_size shoule be less than or equal "
+            "to the input max_size. "
+            "But out's memory_size = %u, max_size = %u.",
+            size, max_size));
     if (is_cpu_place(place)) {
       PADDLE_ENFORCE_EQ(0, cudaMemcpyAsync(out->data<float>(), in, size,
-                                           cudaMemcpyDeviceToHost, *stream_));
+                                           cudaMemcpyDeviceToHost, *stream_),
+                        platform::errors::External(
+                            "cudaMemcpyAsync(cudaMemcpyDeviceToHost) error."));
     } else if (is_gpu_place(place)) {
-      PADDLE_ENFORCE_EQ(0, cudaMemcpyAsync(out->data<float>(), in, size,
-                                           cudaMemcpyDeviceToDevice, *stream_));
+      PADDLE_ENFORCE_EQ(
+          0, cudaMemcpyAsync(out->data<float>(), in, size,
+                             cudaMemcpyDeviceToDevice, *stream_),
+          platform::errors::External(
+              "cudaMemcpyAsync(cudaMemcpyDeviceToDevice) error."));
     } else {
-      PADDLE_THROW("Unknown device for converter");
+      PADDLE_THROW(platform::errors::NotFound("Unknown device for converter"));
     }
     cudaStreamSynchronize(*stream_);
   }

paddle/fluid/inference/tensorrt/convert/io_converter.h

@@ -44,10 +44,14 @@ class EngineIOConverter {
 
   static void ConvertInput(const std::string& op_type, const LoDTensor& in,
                            void* out, size_t max_size, cudaStream_t* stream) {
-    PADDLE_ENFORCE(stream != nullptr);
+    PADDLE_ENFORCE_NOT_NULL(stream,
+                            platform::errors::InvalidArgument(
+                                "The input stream must not be nullptr."));
     auto* converter = Registry<EngineIOConverter>::Global().Lookup(
         op_type, "default" /* default_type */);
-    PADDLE_ENFORCE_NOT_NULL(converter);
+    PADDLE_ENFORCE_NOT_NULL(
+        converter, platform::errors::Unimplemented(
+                       "The %s in is not supported yet.", op_type.c_str()));
     converter->SetStream(stream);
     (*converter)(in, out, max_size);
   }
@@ -55,10 +59,14 @@ class EngineIOConverter {
   static void ConvertOutput(const std::string& op_type, const void* in,
                             LoDTensor* out, size_t max_size,
                             cudaStream_t* stream) {
-    PADDLE_ENFORCE(stream != nullptr);
+    PADDLE_ENFORCE_NOT_NULL(stream,
+                            platform::errors::InvalidArgument(
+                                "The input stream must not be nullptr."));
     auto* converter = Registry<EngineIOConverter>::Global().Lookup(
         op_type, "default" /* default_type */);
-    PADDLE_ENFORCE_NOT_NULL(converter);
+    PADDLE_ENFORCE_NOT_NULL(
+        converter, platform::errors::Unimplemented(
+                       "The %s in not supported yet.", op_type.c_str()));
     converter->SetStream(stream);
     (*converter)(in, out, max_size);
   }

paddle/fluid/inference/tensorrt/convert/op_converter.h

@@ -53,7 +53,12 @@ class OpConverter {
     OpConverter* it{nullptr};
 
     if (op_desc.Type() == "mul") {
-      PADDLE_ENFORCE_EQ(op_desc.Input("Y").size(), 1UL);
+      PADDLE_ENFORCE_EQ(op_desc.Input("Y").size(), 1UL,
+                        platform::errors::InvalidArgument(
+                            "The input op mul's Input(\"Y\")."
+                            "size() should equal to 1, but reveceid "
+                            "Input(\"Y\").size() = %u.",
+                            op_desc.Input("Y").size()));
       std::string Y = op_desc.Input("Y")[0];
       if (parameters.count(Y)) {
         it = Registry<OpConverter>::Global().Lookup("fc");
@@ -66,38 +71,51 @@ class OpConverter {
       // static std::unordered_set<std::string> add_weight_op_set {"add", "mul",
       // "sub", "div"};
       static std::unordered_set<std::string> add_weight_op_set{"add", "mul"};
-      PADDLE_ENFORCE_EQ(op_desc.Input("Y").size(), 1UL);
+      PADDLE_ENFORCE_EQ(op_desc.Input("Y").size(), 1UL,
+                        platform::errors::InvalidArgument(
+                            "The input op's Input(\"Y\")."
+                            "size() should equal to 1, but reveceid "
+                            "Input(\"Y\").size() = %u.",
+                            op_desc.Input("Y").size()));
       int op_type_len = op_desc.Type().size();
       std::string op_type = op_desc.Type().substr(op_type_len - 3, op_type_len);
       std::string Y = op_desc.Input("Y")[0];
       if (parameters.count(Y)) {
-        PADDLE_ENFORCE(add_weight_op_set.count(op_type) > 0,
-                       "Unsupported elementwise type" + op_type);
+        PADDLE_ENFORCE_GT(
+            add_weight_op_set.count(op_type), 0,
+            platform::errors::Unimplemented("Unsupported elementwise type %s",
+                                            op_type.c_str()));
         it = Registry<OpConverter>::Global().Lookup("elementwise_" + op_type +
                                                     "_weight");
-        PADDLE_ENFORCE_NOT_NULL(it, "no OpConverter for optype [%s]",
-                                op_desc.Type());
+        PADDLE_ENFORCE_NOT_NULL(
+            it, platform::errors::Unimplemented(
+                    "no OpConverter for optype [%s]", op_desc.Type()));
       } else {
-        PADDLE_ENFORCE(add_tensor_op_set.count(op_type) > 0,
-                       "Unsupported elementwise type" + op_type);
+        PADDLE_ENFORCE_GT(
+            add_tensor_op_set.count(op_type), 0,
+            platform::errors::Unimplemented("Unsupported elementwise type %s",
+                                            op_type.c_str()));
         it = Registry<OpConverter>::Global().Lookup("elementwise_" + op_type +
                                                     "_tensor");
       }
-      PADDLE_ENFORCE_NOT_NULL(it, "no OpConverter for optype [%s]",
-                              op_desc.Type());
+      PADDLE_ENFORCE_NOT_NULL(
+          it, platform::errors::Unimplemented("no OpConverter for optype [%s]",
+                                              op_desc.Type()));
     }
 
     if (op_desc.Type() == "depthwise_conv2d") {
       it = Registry<OpConverter>::Global().Lookup("conv2d");
-      PADDLE_ENFORCE_NOT_NULL(it, "no OpConverter for optype [%s]",
-                              op_desc.Type());
+      PADDLE_ENFORCE_NOT_NULL(
+          it, platform::errors::Unimplemented("no OpConverter for optype [%s]",
+                                              op_desc.Type()));
     }
 
     if (!it) {
       it = Registry<OpConverter>::Global().Lookup(op_desc.Type());
     }
-    PADDLE_ENFORCE_NOT_NULL(it, "no OpConverter for optype [%s]",
-                            op_desc.Type());
+    PADDLE_ENFORCE_NOT_NULL(
+        it, platform::errors::Unimplemented("no OpConverter for optype [%s]",
+                                            op_desc.Type()));
 
     it->SetEngine(engine);
     (*it)(op, scope, test_mode);
@@ -149,9 +167,13 @@ class OpConverter {
     for (auto& input : inputs) {
       if (parameters.count(input)) continue;
       auto* var = block_desc->FindVar(input);
-      PADDLE_ENFORCE(var, "no variable called %s", input);
-      PADDLE_ENFORCE_EQ(var->GetType(), FluidDT::VarType_Type_LOD_TENSOR,
-                        "TensorRT engine only takes LoDTensor as input");
+      PADDLE_ENFORCE_NOT_NULL(
+          var, platform::errors::NotFound("no variable called %s in block.",
+                                          input.c_str()));
+      PADDLE_ENFORCE_EQ(
+          var->GetType(), FluidDT::VarType_Type_LOD_TENSOR,
+          platform::errors::InvalidArgument("TensorRT engine only takes "
+                                            "LoDTensor as input"));
       auto var_shape = var->GetShape();
       if (engine->with_dynamic_shape()) {
 #if IS_TRT_VERSION_GE(6000)

paddle/fluid/inference/tensorrt/convert/pad_op.cc

@@ -39,9 +39,22 @@ class PadOpConverter : public OpConverter {
     nvinfer1::Dims input_shape = input->getDimensions();
     int nbDims = input_shape.nbDims;
     int pad_size = static_cast<int>(paddings.size());
-    PADDLE_ENFORCE_GE(nbDims, 2);
-    PADDLE_ENFORCE_EQ((nbDims + 1) * 2, pad_size);
-    PADDLE_ENFORCE(pad_value == 0.0, "The pad layer of TRT only support zero.");
+    PADDLE_ENFORCE_GE(
+        nbDims, 2,
+        platform::errors::InvalidArgument(
+            "Input X[0]'s dimension should greater than or equal to 2. "
+            "But received %d.",
+            nbDims));
+    PADDLE_ENFORCE_EQ(
+        (nbDims + 1) * 2, pad_size,
+        platform::errors::InvalidArgument("Input X[0]'s dimension(nbDims for "
+                                          "short) should meet the condition:"
+                                          "(nbDims + 1) * 2 == pad_size. But "
+                                          "received nbDims:%d, pad_size:%d.",
+                                          nbDims, pad_size));
+    PADDLE_ENFORCE_EQ(pad_value, 0.0,
+                      platform::errors::InvalidArgument(
+                          "The pad layer of TRT only support zero."));
 
     nvinfer1::DimsHW pre_pad(paddings[pad_size - 4], paddings[pad_size - 2]);
     nvinfer1::DimsHW post_pad(paddings[pad_size - 3], paddings[pad_size - 1]);
@@ -50,7 +63,9 @@ class PadOpConverter : public OpConverter {
                                        *const_cast<nvinfer1::ITensor*>(input),
                                        pre_pad, post_pad);
 
-    PADDLE_ENFORCE(layer != nullptr);
+    PADDLE_ENFORCE_NOT_NULL(layer,
+                            platform::errors::External(
+                                "add padding layer to tensorrt engine error"));
     auto output_name = op_desc.Output("Out")[0];
     RreplenishLayerAndOutput(layer, "pad", {output_name}, test_mode);
   }

paddle/fluid/inference/tensorrt/convert/slice_op.cc

@@ -23,9 +23,8 @@ class SliceOpConverter : public OpConverter {
  public:
   void operator()(const framework::proto::OpDesc& op,
                   const framework::Scope& scope, bool test_mode) override {
-// This OP is implemented by trt dynamic shpae plugin.
-// Dynamic shape plugin requires TRT version greater than 6.0.
-#if IS_TRT_VERSION_GE(6000)
+    // This OP is implemented by trt dynamic shpae plugin.
+    // Dynamic shape plugin requires TRT version greater than 6.0.
     VLOG(4) << "convert slice op to tensorrt layer";
     framework::OpDesc op_desc(op, nullptr);
     // Declare inputs
@@ -38,27 +37,65 @@ class SliceOpConverter : public OpConverter {
     std::vector<int> ends =
         BOOST_GET_CONST(std::vector<int>, op_desc.GetAttr("ends"));
 
+    PADDLE_ENFORCE_EQ(
+        starts.size(), axes.size(),
+        platform::errors::InvalidArgument(
+            "The size of starts must be equal to the size of axes."));
+    PADDLE_ENFORCE_EQ(
+        ends.size(), axes.size(),
+        platform::errors::InvalidArgument(
+            "The size of ends must be equal to the size of axes."));
+
+    auto input_dims = input->getDimensions();
+    if (!engine_->with_dynamic_shape()) {
+      // notice that input shape is [CHW] without batch axis when input has
+      // static shape
+      for (size_t i = input_dims.nbDims; i > 0; i--) {
+        input_dims.d[i] = input_dims.d[i - 1];
+      }
+      input_dims.d[0] = 1;  // fake batchsize, not useful here
+      for (size_t i = 0; i < axes.size(); i++) {
+        // split on batch is not supported in TensorRT
+        PADDLE_ENFORCE_NE(axes[i], 0, platform::errors::InvalidArgument(
+                                          "Invalid slice axis. Slice on batch "
+                                          "axis is not supported in TensorRT"));
+        if (starts[i] < 0) {
+          starts[i] = std::max(starts[i] + input_dims.d[axes[i]], 0);
+        }
+        if (ends[i] < 0) {
+          ends[i] = std::max(ends[i] + input_dims.d[axes[i]], 0);
+        }
+        ends[i] = std::min(ends[i], input_dims.d[axes[i]]);
+        PADDLE_ENFORCE_GT(
+            ends[i], starts[i],
+            platform::errors::InvalidArgument(
+                "Attr(ends) should be greater than attr(starts) in "
+                "slice op. But received ends = %d, starts = %d.",
+                ends[i], starts[i]));
+      }
+    }
+
     nvinfer1::ILayer* layer = nullptr;
     if (engine_->with_dynamic_shape()) {
+#if IS_TRT_VERSION_GE(6000)
       bool ban_fp16 = engine_->disable_trt_plugin_fp16();
       plugin::SlicePluginDynamic* plugin =
-          new plugin::SlicePluginDynamic(starts, ends, ends, ban_fp16);
+          new plugin::SlicePluginDynamic(starts, ends, axes, ban_fp16);
       layer = engine_->AddPluginV2(&input, 1, plugin);
-    } else {
-      PADDLE_THROW(platform::errors::Fatal(
-          "You are running the Ernie(Bert) model in static"
-          "shape mode, which is not supported for the time being.\n"
-          "You can use the config.SetTRTDynamicShapeInfo(...) interface"
-          " to set the shape information to run the dynamic shape mode."));
-    }
-
-    auto output_name = op_desc.Output("Out")[0];
-    RreplenishLayerAndOutput(layer, "skip_layernorm", {output_name}, test_mode);
 #else
       PADDLE_THROW(platform::errors::Fatal(
           "You are running the TRT Dynamic Shape mode, need to confirm that "
           "your TRT version is no less than 6.0"));
 #endif
+    } else {
+      bool ban_fp16 = engine_->disable_trt_plugin_fp16();
+      plugin::SlicePlugin* plugin =
+          new plugin::SlicePlugin(starts, ends, axes, ban_fp16);
+      layer = engine_->AddPlugin(&input, 1, plugin);
+    }
+
+    auto output_name = op_desc.Output("Out")[0];
+    RreplenishLayerAndOutput(layer, "slice", {output_name}, test_mode);
   }
 };
 

paddle/fluid/inference/tensorrt/convert/swish_op.cc

@@ -28,11 +28,20 @@ class SwishOpConverter : public OpConverter {
     framework::OpDesc op_desc(op, nullptr);
     // Declare inputs
     int input_num = op_desc.Input("X").size();
-    PADDLE_ENFORCE(input_num == 1);
+    PADDLE_ENFORCE_EQ(input_num, 1,
+                      platform::errors::InvalidArgument(
+                          "The input X's size must equal to 1 in TRT swish op."
+                          " But received X's size %d.",
+                          input_num));
     auto* input = engine_->GetITensor(op_desc.Input("X")[0]);
     // Get output
     size_t output_num = op_desc.Output("Out").size();
-    PADDLE_ENFORCE(output_num == 1);
+    PADDLE_ENFORCE_EQ(
+        output_num, 1UL,
+        platform::errors::InvalidArgument(
+            "The ouput Out's size must equal to 1 in TRT swish op. "
+            "But received Out's size %u.",
+            output_num));
     // Get attrs
     float beta = BOOST_GET_CONST(float, op_desc.GetAttr("beta"));
 

paddle/fluid/inference/tensorrt/convert/ut_helper.h

@@ -49,7 +49,10 @@ void RandomizeTensor(framework::LoDTensor* tensor, const platform::Place& place,
                      const platform::DeviceContext& ctx) {
   auto dims = tensor->dims();
   size_t num_elements = analysis::AccuDims(dims, dims.size());
-  PADDLE_ENFORCE_GT(num_elements, 0);
+  PADDLE_ENFORCE_GT(
+      num_elements, 0UL,
+      platform::errors::PermissionDenied("RandomizeTensor only can be used for "
+                                         "tensor which dims is not zero."));
 
   platform::CPUPlace cpu_place;
   framework::LoDTensor temp_tensor;
@@ -79,7 +82,8 @@ class TRTConvertValidation {
         scope_(scope),
         if_add_batch_(if_add_batch),
         max_batch_size_(max_batch_size) {
-    PADDLE_ENFORCE_EQ(cudaStreamCreate(&stream_), 0);
+    PADDLE_ENFORCE_EQ(cudaStreamCreate(&stream_), 0,
+                      platform::errors::External("cudaStreamCreate error."));
     engine_.reset(new TensorRTEngine(max_batch_size, workspace_size));
     engine_->InitNetwork();
   }
@@ -154,7 +158,12 @@ class TRTConvertValidation {
   void Execute(int batch_size,
                std::unordered_set<std::string> neglected_output = {}) {
     // Execute Fluid Op
-    PADDLE_ENFORCE_LE(batch_size, max_batch_size_);
+    PADDLE_ENFORCE_LE(batch_size, max_batch_size_,
+                      platform::errors::InvalidArgument(
+                          "Runtime batch_size should be less than or equal to "
+                          "max_batch_size_. "
+                          "But received batch_size:%d, max_batch_size_:%d",
+                          batch_size, max_batch_size_));
     platform::CUDADeviceContext ctx(place_);
     op_->Run(scope_, place_);
     cudaStreamSynchronize(stream_);

paddle/fluid/inference/tensorrt/op_teller.cc

@@ -31,6 +31,7 @@ struct SimpleOpTypeSetTeller : public Teller {
     teller_set.insert("fused_embedding_eltwise_layernorm");
     teller_set.insert("multihead_matmul");
     teller_set.insert("skip_layernorm");
+    teller_set.insert("slice");
 #endif
   }
 

paddle/fluid/inference/tensorrt/plugin/slice_op_plugin.cu

@@ -26,8 +26,10 @@ namespace inference {
 namespace tensorrt {
 namespace plugin {
 
-// Dynamic Plugin below.
-#if IS_TRT_VERSION_GE(6000)
+SlicePlugin *CreateSlicePluginDeserialize(const void *buffer, size_t length) {
+  return new SlicePlugin(buffer, length);
+}
+REGISTER_TRT_PLUGIN("slice_plugin", CreateSlicePluginDeserialize);
 
 template <typename T>
 __global__ void SliceKernel(int num, int dims, const T *input,
@@ -56,11 +58,196 @@ __global__ void SliceKernel(int num, int dims, const T *input,
   }
 }
 
+SlicePlugin::SlicePlugin(std::vector<int> starts, std::vector<int> ends,
+                         std::vector<int> axes, bool ban_fp16)
+    : starts_(starts), ends_(ends), axes_(axes), ban_fp16_(ban_fp16) {
+  cudaEventCreate(&copy_event_);
+  cudaStreamCreate(&copy_stream_);
+}
+
+SlicePlugin::SlicePlugin(void const *serial_data, size_t serial_length) {
+  deserializeBase(serial_data, serial_length);
+  DeserializeValue(&serial_data, &serial_length, &starts_);
+  DeserializeValue(&serial_data, &serial_length, &ends_);
+  DeserializeValue(&serial_data, &serial_length, &axes_);
+  DeserializeValue(&serial_data, &serial_length, &ban_fp16_);
+  cudaEventCreate(&copy_event_);
+  cudaStreamCreate(&copy_stream_);
+}
+
+SlicePlugin::~SlicePlugin() {
+  cudaStreamDestroy(copy_stream_);
+  cudaEventDestroy(copy_event_);
+  cudaFree(offset_temp_data_);
+}
+
+SlicePlugin *SlicePlugin::clone() const {
+  return new SlicePlugin(starts_, ends_, axes_, ban_fp16_);
+}
+
+bool SlicePlugin::supportsFormat(nvinfer1::DataType type,
+                                 nvinfer1::PluginFormat format) const {
+#ifdef SUPPORTS_CUDA_FP16
+  return ((type == nvinfer1::DataType::kFLOAT ||
+           type == nvinfer1::DataType::kHALF) &&
+          (format == nvinfer1::PluginFormat::kNCHW));
+#else
+  return ((type == nvinfer1::DataType::kFLOAT) &&
+          (format == nvinfer1::PluginFormat::kNCHW));
+#endif
+}
+
+nvinfer1::Dims SlicePlugin::getOutputDimensions(int index,
+                                                const nvinfer1::Dims *inputs,
+                                                int nb_input_dims) {
+  auto in_dims = inputs[0];
+  nvinfer1::Dims out_dims = in_dims;
+  for (size_t i = 0; i < axes_.size(); i++) {
+    int start = starts_[i];
+    int end = ends_[i];
+    out_dims.d[axes_[i] - 1] = end - start;
+  }
+  return out_dims;
+}
+
+int SlicePlugin::enqueue(int batch_size, const void *const *inputs,
+                         void **outputs, void *workspace, cudaStream_t stream) {
+  auto input_dims = getInputDims(0);
+
+  // notice input dims is [C, H, W], add input batch dim here
+  auto out_dims = getOutputDimensions(0, &input_dims, 1);
+  input_dims.nbDims += 1;
+  out_dims.nbDims += 1;
+  for (auto i = input_dims.nbDims; i > 0; --i) {
+    input_dims.d[i] = input_dims.d[i - 1];
+    out_dims.d[i] = out_dims.d[i - 1];
+  }
+  input_dims.d[0] = batch_size;
+  out_dims.d[0] = batch_size;
+
+  auto num_dims = input_dims.nbDims;
+  size_t out_num = ProductDim(out_dims);
+
+  std::vector<int> seg_offsets;
+  std::vector<int> offsets;
+  std::vector<int> extends;
+
+  offsets.resize(num_dims);
+  extends.resize(num_dims);
+  seg_offsets.resize(num_dims);
+
+  seg_offsets[num_dims - 1] = 1;
+  for (int i = num_dims - 2; i >= 0; i--) {
+    seg_offsets[i] = input_dims.d[i + 1] * seg_offsets[i + 1];
+  }
+  for (size_t i = 0; i < num_dims; ++i) {
+    offsets[i] = 0;
+    extends[i] = out_dims.d[i];
+  }
+  for (size_t i = 0; i < axes_.size(); ++i) {
+    offsets[axes_[i]] = starts_[i];
+  }
+
+  std::vector<int> offset_info;
+  for (size_t i = 0; i < num_dims; ++i) {
+    offset_info.push_back(offsets[i]);
+    offset_info.push_back(extends[i]);
+    offset_info.push_back(seg_offsets[i]);
+  }
+
+  if (offset_temp_data_ == nullptr) {
+    cudaMalloc(&offset_temp_data_, 3 * num_dims * sizeof(int));
+  }
+
+  cudaMemcpyAsync(offset_temp_data_, offset_info.data(),
+                  sizeof(int) * 3 * num_dims, cudaMemcpyHostToDevice,
+                  copy_stream_);
+
+  cudaEventRecord(copy_event_, copy_stream_);
+  cudaStreamWaitEvent(stream, copy_event_, 0);
+
+  int threads = 256;
+  int blocks = (out_num + threads - 1) / threads;
+  auto input_type = getDataType();
+  if (input_type == nvinfer1::DataType::kFLOAT) {
+    const float *input1 = static_cast<const float *>(inputs[0]);
+    float *output = static_cast<float *>(outputs[0]);
+    SliceKernel<float><<<blocks, threads, 3 * num_dims * sizeof(int), stream>>>(
+        out_num, num_dims, input1, offset_temp_data_, output);
+  } else if (input_type == nvinfer1::DataType::kHALF) {
+#ifdef SUPPORTS_CUDA_FP16
+    const half *input1 = static_cast<const half *>(inputs[0]);
+    half *output = static_cast<half *>(outputs[0]);
+    SliceKernel<half><<<blocks, threads, 3 * num_dims * sizeof(int), stream>>>(
+        out_num, num_dims, input1, offset_temp_data_, output);
+#else
+    PADDLE_THROW(platform::errors::Fatal(
+        "The cuda archs you specific should greater than 600."));
+#endif
+  } else {
+    PADDLE_THROW(platform::errors::Fatal(
+        "The Slice TRT Plugin's input type should be float or half."));
+  }
+  return cudaGetLastError() != cudaSuccess;
+}
+
+size_t SlicePlugin::getSerializationSize() {
+  return getBaseSerializationSize() + SerializedSize(getPluginType()) +
+         SerializedSize(starts_) + SerializedSize(ends_) +
+         SerializedSize(axes_) + SerializedSize(ban_fp16_);
+}
+
+void SlicePlugin::serialize(void *buffer) {
+  SerializeValue(&buffer, getPluginType());
+  serializeBase(buffer);
+  SerializeValue(&buffer, starts_);
+  SerializeValue(&buffer, ends_);
+  SerializeValue(&buffer, axes_);
+  SerializeValue(&buffer, ban_fp16_);
+}
+
+// Dynamic Plugin below.
+#if IS_TRT_VERSION_GE(6000)
+SlicePluginDynamic::SlicePluginDynamic(std::vector<int> starts,
+                                       std::vector<int> ends,
+                                       std::vector<int> axes, bool ban_fp16)
+    : starts_(starts), ends_(ends), axes_(axes), ban_fp16_(ban_fp16) {
+  cudaEventCreate(&copy_event_);
+  cudaStreamCreate(&copy_stream_);
+}
+
+SlicePluginDynamic::SlicePluginDynamic(void const *serialData,
+                                       size_t serialLength) {
+  DeserializeValue(&serialData, &serialLength, &starts_);
+  DeserializeValue(&serialData, &serialLength, &ends_);
+  DeserializeValue(&serialData, &serialLength, &axes_);
+  DeserializeValue(&serialData, &serialLength, &ban_fp16_);
+  cudaEventCreate(&copy_event_);
+  cudaStreamCreate(&copy_stream_);
+}
+
+void SlicePluginDynamic::destroy() {
+  cudaStreamDestroy(copy_stream_);
+  cudaEventDestroy(copy_event_);
+  cudaFree(offset_temp_data_);
+  delete this;
+}
+
 int SlicePluginDynamic::initialize() { return 0; }
 
-size_t SlicePluginDynamic::getSerializationSize() const { return 0; }
+size_t SlicePluginDynamic::getSerializationSize() const {
+  size_t size = SerializedSize(starts_) + SerializedSize(ends_) +
+                SerializedSize(axes_) + SerializedSize(ban_fp16_);
 
-void SlicePluginDynamic::serialize(void *buffer) const {}
+  return size;
+}
+
+void SlicePluginDynamic::serialize(void *buffer) const {
+  SerializeValue(&buffer, starts_);
+  SerializeValue(&buffer, ends_);
+  SerializeValue(&buffer, axes_);
+  SerializeValue(&buffer, ban_fp16_);
+}
 
 nvinfer1::DimsExprs SlicePluginDynamic::getOutputDimensions(
     int output_index, const nvinfer1::DimsExprs *inputs, int nb_inputs,
@@ -136,9 +323,9 @@ int SlicePluginDynamic::enqueue(const nvinfer1::PluginTensorDesc *input_desc,
   std::vector<int> offsets;
   std::vector<int> extends;
 
-  offsets.reserve(num_dims);
-  extends.reserve(num_dims);
-  seg_offsets.reserve(num_dims);
+  offsets.resize(num_dims);
+  extends.resize(num_dims);
+  seg_offsets.resize(num_dims);
 
   seg_offsets[num_dims - 1] = 1;
   for (int i = num_dims - 2; i >= 0; i--) {
@@ -160,16 +347,16 @@ int SlicePluginDynamic::enqueue(const nvinfer1::PluginTensorDesc *input_desc,
     offset_info.push_back(seg_offsets[i]);
   }
 
-  framework::Tensor offset_temp_tensor;
+  if (offset_temp_data_ == nullptr) {
+    cudaMalloc(&offset_temp_data_, 3 * num_dims * sizeof(int));
+  }
 
-  int device_id;
-  cudaGetDevice(&device_id);
-  offset_temp_tensor.Resize({3 * num_dims});
-  auto *offset_temp_data =
-      offset_temp_tensor.mutable_data<int>(platform::CUDAPlace(device_id));
+  cudaMemcpyAsync(offset_temp_data_, offset_info.data(),
+                  sizeof(int) * 3 * num_dims, cudaMemcpyHostToDevice,
+                  copy_stream_);
 
-  cudaMemcpyAsync(offset_temp_data, offset_info.data(),
-                  sizeof(int) * 3 * num_dims, cudaMemcpyHostToDevice, stream);
+  cudaEventRecord(copy_event_, copy_stream_);
+  cudaStreamWaitEvent(stream, copy_event_, 0);
 
   int threads = 256;
   int blocks = (out_num + threads - 1) / threads;
@@ -178,13 +365,13 @@ int SlicePluginDynamic::enqueue(const nvinfer1::PluginTensorDesc *input_desc,
     const float *input1 = static_cast<const float *>(inputs[0]);
     float *output = static_cast<float *>(outputs[0]);
     SliceKernel<float><<<blocks, threads, 3 * num_dims * sizeof(int), stream>>>(
-        out_num, num_dims, input1, offset_temp_data, output);
+        out_num, num_dims, input1, offset_temp_data_, output);
   } else if (input_type == nvinfer1::DataType::kHALF) {
 #ifdef SUPPORTS_CUDA_FP16
     const half *input1 = static_cast<const half *>(inputs[0]);
     half *output = static_cast<half *>(outputs[0]);
     SliceKernel<half><<<blocks, threads, 3 * num_dims * sizeof(int), stream>>>(
-        out_num, num_dims, input1, offset_temp_data, output);
+        out_num, num_dims, input1, offset_temp_data_, output);
 #else
     PADDLE_THROW(platform::errors::Fatal(
         "The cuda archs you specific should greater than 600."));

paddle/fluid/inference/tensorrt/plugin/slice_op_plugin.h

@@ -26,17 +26,56 @@ namespace inference {
 namespace tensorrt {
 namespace plugin {
 
+class SlicePlugin : public PluginTensorRT {
+ public:
+  explicit SlicePlugin(std::vector<int> starts, std::vector<int> ends,
+                       std::vector<int> axes, bool ban_fp16);
+
+  // It was used for tensorrt deserialization.
+  // It should not be called by users.
+  SlicePlugin(void const* serial_data, size_t serial_length);
+  ~SlicePlugin();
+  SlicePlugin* clone() const override;
+
+  const char* getPluginType() const override { return "slice_plugin"; }
+  int getNbOutputs() const override { return 1; }
+  int initialize() override { return 0; }
+  bool supportsFormat(nvinfer1::DataType type,
+                      nvinfer1::PluginFormat format) const override;
+  nvinfer1::Dims getOutputDimensions(int index, const nvinfer1::Dims* inputs,
+                                     int nb_input_dims) override;
+  int enqueue(int batch_size, const void* const* inputs, void** outputs,
+              void* workspace, cudaStream_t stream) override;
+
+ protected:
+  size_t getSerializationSize() override;
+
+  // TRT will call this func  to serialize the configuration of TRT
+  // It should not be called by users.
+  void serialize(void* buffer) override;
+
+ private:
+  std::vector<int> starts_;
+  std::vector<int> ends_;
+  std::vector<int> axes_;
+  bool ban_fp16_{false};
+  int* offset_temp_data_{nullptr};
+  cudaEvent_t copy_event_;
+  cudaStream_t copy_stream_;
+};
+
 #if IS_TRT_VERSION_GE(6000)
 class SlicePluginDynamic : public DynamicPluginTensorRT {
  public:
   explicit SlicePluginDynamic(std::vector<int> starts, std::vector<int> ends,
-                              std::vector<int> axes, bool ban_fp16)
-      : starts_(starts), ends_(ends), axes_(axes), ban_fp16_(ban_fp16) {}
-  SlicePluginDynamic(void const* serialData, size_t serialLength) {}
+                              std::vector<int> axes, bool ban_fp16);
+
   nvinfer1::IPluginV2DynamicExt* clone() const override {
     return new SlicePluginDynamic(starts_, ends_, axes_, ban_fp16_);
   }
 
+  SlicePluginDynamic(void const* serialData, size_t serialLength);
+
   const char* getPluginType() const override { return "slice_plugin"; }
   int getNbOutputs() const override { return 1; }
   int initialize() override;
@@ -72,15 +111,54 @@ class SlicePluginDynamic : public DynamicPluginTensorRT {
                                        const nvinfer1::DataType* inputTypes,
                                        int nbInputs) const override;
 
-  void destroy() override { delete this; }
+  void destroy() override;
 
  private:
   std::vector<int> starts_;
   std::vector<int> ends_;
   std::vector<int> axes_;
-
   bool ban_fp16_{false};
+  int* offset_temp_data_{nullptr};
+  cudaEvent_t copy_event_;
+  cudaStream_t copy_stream_;
 };
+
+class SlicePluginV2Creator : public nvinfer1::IPluginCreator {
+ public:
+  SlicePluginV2Creator() {}
+  const char* getPluginName() const override { return "slice_plugin"; }
+
+  const char* getPluginVersion() const override { return "1"; }
+
+  const nvinfer1::PluginFieldCollection* getFieldNames() override {
+    return &field_collection_;
+  }
+
+  nvinfer1::IPluginV2* createPlugin(
+      const char* name, const nvinfer1::PluginFieldCollection* fc) override {
+    return nullptr;
+  }
+
+  nvinfer1::IPluginV2* deserializePlugin(const char* name,
+                                         const void* serialData,
+                                         size_t serialLength) override {
+    auto plugin = new SlicePluginDynamic(serialData, serialLength);
+    return plugin;
+  }
+
+  void setPluginNamespace(const char* libNamespace) override {
+    namespace_ = libNamespace;
+  }
+
+  const char* getPluginNamespace() const override { return namespace_.c_str(); }
+
+ private:
+  std::string namespace_;
+  nvinfer1::PluginFieldCollection field_collection_;
+};
+
+REGISTER_TRT_PLUGIN_V2(SlicePluginV2Creator);
+
 #endif
 
 }  // namespace plugin

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

@@ -480,10 +480,9 @@ if(WITH_GPU AND TENSORRT_FOUND)
         inference_download_and_uncompress(${TEST_TRT_ERNIE_MODEL} ${INFERENCE_URL}/tensorrt_test "ernie_model_4_unserialized.tgz")
     endif()
 
-    # disable test_trt_dynamic_shape_ernie_ser_deser temporary
-    #inference_analysis_test(test_trt_dynamic_shape_ernie_ser_deser SRCS trt_dynamic_shape_ernie_deserialize_test.cc
-    #        EXTRA_DEPS ${INFERENCE_EXTRA_DEPS} 
-    #        ARGS --infer_model=${TEST_TRT_ERNIE_MODEL}/ernie_model_4_unserialized)
+    inference_analysis_test(test_trt_dynamic_shape_ernie_ser_deser SRCS trt_dynamic_shape_ernie_deserialize_test.cc
+            EXTRA_DEPS ${INFERENCE_EXTRA_DEPS} 
+            ARGS --infer_model=${TEST_TRT_ERNIE_MODEL}/ernie_model_4_unserialized)
 
 endif()
 

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

@@ -245,8 +245,14 @@ TEST(Analyzer_bert, transfer_scope_cache) {
   // Since paddle::framework::global_transfer_scope_cache() and
   // paddle::framework::global_transfer_data_cache() are thread_local,
   // their pointer should be different among different thread id.
-  PADDLE_ENFORCE(global_transfer_scope_cache.size(), threads_num);
-  PADDLE_ENFORCE(global_transfer_data_cache.size(), threads_num);
+  PADDLE_ENFORCE_EQ(
+      global_transfer_scope_cache.size(), threads_num,
+      paddle::platform::errors::Fatal(
+          "The size of scope cache is not equal to thread number."));
+  PADDLE_ENFORCE_EQ(
+      global_transfer_data_cache.size(), threads_num,
+      paddle::platform::errors::Fatal(
+          "The size of data cache is not equal to thread number."));
 }
 
 }  // namespace inference

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

@@ -69,11 +69,13 @@ void PD_run() {
   PD_DeletePaddleTensor(input);
   int size;
   const int* out_shape = PD_GetPaddleTensorShape(out_data, &size);
-  CHECK(size == 2) << "The Output shape's size is NOT match.";
+  PADDLE_ENFORCE_EQ(size, 2, paddle::platform::errors::InvalidArgument(
+                                 "The Output shape's size is NOT match."));
   std::vector<int> ref_outshape_size({9, 6});
   for (int i = 0; i < 2; ++i) {
-    CHECK(out_shape[i] == ref_outshape_size[i])
-        << "The Output's shape is NOT match.";
+    PADDLE_ENFORCE_EQ(out_shape[i], ref_outshape_size[i],
+                      paddle::platform::errors::InvalidArgument(
+                          "The Output shape's size is NOT match."));
   }
   PD_DeletePaddleBuf(buf);
 }

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

@@ -36,9 +36,9 @@ void zero_copy_run() {
   PD_SwitchIrDebug(config, true);
   PD_SetModel(config, prog_file.c_str(), params_file.c_str());
   bool use_feed_fetch = PD_UseFeedFetchOpsEnabled(config);
-  CHECK(!use_feed_fetch) << "NO";
+  EXPECT_FALSE(use_feed_fetch);
   bool specify_input_names = PD_SpecifyInputName(config);
-  CHECK(specify_input_names) << "NO";
+  EXPECT_TRUE(specify_input_names);
 
   const int batch_size = 1;
   const int channels = 3;
@@ -85,13 +85,13 @@ TEST(PD_AnalysisConfig, profile_mkldnn) {
   PD_SwitchIrDebug(config, true);
   PD_EnableMKLDNN(config);
   bool mkldnn_enable = PD_MkldnnEnabled(config);
-  CHECK(mkldnn_enable) << "NO";
+  EXPECT_TRUE(mkldnn_enable);
   PD_EnableMkldnnQuantizer(config);
   bool quantizer_enable = PD_MkldnnQuantizerEnabled(config);
-  CHECK(quantizer_enable) << "NO";
+  EXPECT_TRUE(quantizer_enable);
   PD_EnableMkldnnBfloat16(config);
   bool bfloat16_enable = PD_MkldnnBfloat16Enabled(config);
-  CHECK(bfloat16_enable) << "NO";
+  EXPECT_TRUE(bfloat16_enable);
   PD_SetMkldnnCacheCapacity(config, 0);
   PD_SetModel(config, prog_file.c_str(), params_file.c_str());
   PD_DeleteAnalysisConfig(config);

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

@@ -126,7 +126,9 @@ void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
   std::string turn_mask_pre = "turn_mask_";
 
   auto one_batch = data->NextBatch();
-  PADDLE_ENFORCE(!one_batch.response.empty());
+  PADDLE_ENFORCE(
+      !one_batch.response.empty(),
+      paddle::platform::errors::Fatal("The response of one batch is empty."));
   int size = one_batch.response[0].size();
   CHECK_EQ(size, kMaxTurnLen);
   // turn tensor assignment
@@ -214,11 +216,17 @@ void profile(bool use_mkldnn = false) {
                  input_slots_all, &outputs, FLAGS_num_threads);
 
   if (FLAGS_num_threads == 1 && !FLAGS_test_all_data) {
-    PADDLE_ENFORCE_GT(outputs.size(), 0);
+    PADDLE_ENFORCE_GT(outputs.size(), 0,
+                      paddle::platform::errors::Fatal(
+                          "The size of outputs should be greater than 0."));
     auto output = outputs.back();
-    PADDLE_ENFORCE_GT(output.size(), 0);
+    PADDLE_ENFORCE_GT(output.size(), 0,
+                      paddle::platform::errors::Fatal(
+                          "The size of output should be greater than 0."));
     size_t size = GetSize(output[0]);
-    PADDLE_ENFORCE_GT(size, 0);
+    PADDLE_ENFORCE_GT(size, 0,
+                      paddle::platform::errors::Fatal(
+                          "The size of output should be greater than 0."));
     float *result = static_cast<float *>(output[0].data.data());
     for (size_t i = 0; i < size; i++) {
       EXPECT_NEAR(result[i], result_data[i], 1e-3);

paddle/fluid/inference/tests/api/analyzer_int8_object_detection_tester.cc

@@ -146,8 +146,9 @@ std::shared_ptr<std::vector<PaddleTensor>> GetWarmupData(
   auto iterations = test_data.size();
   PADDLE_ENFORCE_LE(
       static_cast<size_t>(num_images), iterations * test_data_batch_size,
+      paddle::platform::errors::Fatal(
           "The requested quantization warmup data size " +
-          std::to_string(num_images) + " is bigger than all test data size.");
+          std::to_string(num_images) + " is bigger than all test data size."));
 
   PaddleTensor images;
   images.name = "image";
@@ -237,8 +238,9 @@ std::shared_ptr<std::vector<PaddleTensor>> GetWarmupData(
   }
   PADDLE_ENFORCE_EQ(
       static_cast<size_t>(num_objects), static_cast<size_t>(objects_accum),
-      "The requested num of objects " + std::to_string(num_objects) +
-          " is the same as objects_accum.");
+      paddle::platform::errors::Fatal("The requested num of objects " +
+                                      std::to_string(num_objects) +
+                                      " is the same as objects_accum."));
 
   auto warmup_data = std::make_shared<std::vector<PaddleTensor>>(4);
   (*warmup_data)[0] = std::move(images);

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

@@ -98,7 +98,9 @@ void GetOneBatch(std::vector<PaddleTensor> *input_slots, DataRecord *data,
   input_tensor.name = "word";
   input_tensor.dtype = PaddleDType::INT64;
   TensorAssignData<int64_t>(&input_tensor, {one_batch.data}, one_batch.lod);
-  PADDLE_ENFORCE_EQ(batch_size, static_cast<int>(one_batch.lod.size() - 1));
+  PADDLE_ENFORCE_EQ(
+      batch_size, static_cast<int>(one_batch.lod.size() - 1),
+      paddle::platform::errors::Fatal("The lod size of one batch is invaild."));
   input_slots->assign({input_tensor});
 }
 
@@ -137,12 +139,17 @@ TEST(Analyzer_LAC, profile) {
         24, 25, 25, 25, 38, 30, 31, 14, 15, 44, 24, 25, 25, 25, 25, 25,
         44, 24, 25, 25, 25, 36, 42, 43, 44, 14, 15, 44, 14, 15, 44, 14,
         15, 44, 38, 39, 14, 15, 44, 22, 23, 23, 23, 23, 23, 23, 23};
-    PADDLE_ENFORCE_GT(outputs.size(), 0);
+    PADDLE_ENFORCE_GT(outputs.size(), 0,
+                      paddle::platform::errors::Fatal(
+                          "The size of output should be greater than 0."));
     auto output = outputs.back();
-    PADDLE_ENFORCE_EQ(output.size(), 1UL);
+    PADDLE_ENFORCE_EQ(output.size(), 1UL,
+                      paddle::platform::errors::Fatal(
+                          "The size of output should be equal to 1."));
     size_t size = GetSize(output[0]);
     size_t batch1_size = sizeof(lac_ref_data) / sizeof(int64_t);
-    PADDLE_ENFORCE_GE(size, batch1_size);
+    PADDLE_ENFORCE_GE(size, batch1_size, paddle::platform::errors::Fatal(
+                                             "The size of batch is invaild."));
     int64_t *pdata = static_cast<int64_t *>(output[0].data.data());
     for (size_t i = 0; i < batch1_size; ++i) {
       EXPECT_EQ(pdata[i], lac_ref_data[i]);

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

@@ -117,11 +117,17 @@ void profile(bool memory_load = false) {
     // the first inference result
     const int chinese_ner_result_data[] = {30, 45, 41, 48, 17, 26,
                                            48, 39, 38, 16, 25};
-    PADDLE_ENFORCE_GT(outputs.size(), 0);
+    PADDLE_ENFORCE_GT(outputs.size(), 0,
+                      paddle::platform::errors::Fatal(
+                          "The size of output should be greater than 0."));
     auto output = outputs.back();
-    PADDLE_ENFORCE_EQ(output.size(), 1UL);
+    PADDLE_ENFORCE_EQ(output.size(), 1UL,
+                      paddle::platform::errors::Fatal(
+                          "The size of output should be equal to 1."));
     size_t size = GetSize(output[0]);
-    PADDLE_ENFORCE_GT(size, 0);
+    PADDLE_ENFORCE_GT(size, 0,
+                      paddle::platform::errors::Fatal(
+                          "The size of output should be greater than 0."));
     int64_t *result = static_cast<int64_t *>(output[0].data.data());
     for (size_t i = 0; i < std::min<size_t>(11, size); i++) {
       EXPECT_EQ(result[i], chinese_ner_result_data[i]);

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

@@ -136,11 +136,17 @@ TEST(Analyzer_Pyramid_DNN, profile) {
                  input_slots_all, &outputs, FLAGS_num_threads);
 
   if (FLAGS_num_threads == 1 && !FLAGS_test_all_data && !FLAGS_zero_copy) {
-    PADDLE_ENFORCE_GT(outputs.size(), 0);
+    PADDLE_ENFORCE_GT(outputs.size(), 0,
+                      paddle::platform::errors::Fatal(
+                          "The size of output should be greater than 0."));
     auto output = outputs.back();
-    PADDLE_ENFORCE_EQ(output.size(), 1UL);
+    PADDLE_ENFORCE_EQ(output.size(), 1UL,
+                      paddle::platform::errors::Fatal(
+                          "The size of output should be equal to 1."));
     size_t size = GetSize(output[0]);
-    PADDLE_ENFORCE_GT(size, 0);
+    PADDLE_ENFORCE_GT(size, 0,
+                      paddle::platform::errors::Fatal(
+                          "The size of output should be greater than 0."));
     float *result = static_cast<float *>(output[0].data.data());
     // output is probability, which is in (0, 1).
     for (size_t i = 0; i < size; i++) {

paddle/fluid/inference/tests/api/analyzer_rnn2_tester.cc

@@ -135,11 +135,17 @@ TEST(Analyzer_rnn2, profile) {
 
   if (FLAGS_num_threads == 1 && !FLAGS_test_all_data) {
     // the first inference result
-    PADDLE_ENFORCE_GT(outputs.size(), 0);
+    PADDLE_ENFORCE_GT(outputs.size(), 0,
+                      paddle::platform::errors::Fatal(
+                          "The size of output should be greater than 0."));
     auto output = outputs.back();
-    PADDLE_ENFORCE_GT(output.size(), 0);
+    PADDLE_ENFORCE_GT(output.size(), 0,
+                      paddle::platform::errors::Fatal(
+                          "The size of output should be greater than 0."));
     size_t size = GetSize(output[0]);
-    PADDLE_ENFORCE_GT(size, 0);
+    PADDLE_ENFORCE_GT(size, 0,
+                      paddle::platform::errors::Fatal(
+                          "The size of output should be greater than 0."));
     float *result = static_cast<float *>(output[0].data.data());
     for (size_t i = 0; i < size; i++) {
       EXPECT_NEAR(result[i], result_data[i], 1e-3);

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

@@ -47,7 +47,8 @@ struct DataRecord {
       num_lines++;
       std::vector<std::string> data;
       split(line, '\t', &data);
-      PADDLE_ENFORCE(data.size() >= 4);
+      PADDLE_ENFORCE_GT(data.size(), 4, paddle::platform::errors::Fatal(
+                                            "The size of data is invaild."));
       // load title1 data
       std::vector<int64_t> title1_data;
       split_to_int64(data[0], ' ', &title1_data);
@@ -120,11 +121,17 @@ TEST(Analyzer_seq_conv1, profile) {
 
   if (FLAGS_num_threads == 1 && !FLAGS_test_all_data) {
     // the first inference result
-    PADDLE_ENFORCE_GT(outputs.size(), 0);
+    PADDLE_ENFORCE_GT(outputs.size(), 0,
+                      paddle::platform::errors::Fatal(
+                          "The size of output should be greater than 0."));
     auto output = outputs.back();
-    PADDLE_ENFORCE_EQ(output.size(), 1UL);
+    PADDLE_ENFORCE_EQ(output.size(), 1UL,
+                      paddle::platform::errors::Fatal(
+                          "The size of output should be equal to 0."));
     size_t size = GetSize(output[0]);
-    PADDLE_ENFORCE_GT(size, 0);
+    PADDLE_ENFORCE_GT(size, 0,
+                      paddle::platform::errors::Fatal(
+                          "The size of output should be greater than 0."));
     float *result = static_cast<float *>(output[0].data.data());
     // output is probability, which is in (0, 1).
     for (size_t i = 0; i < size; i++) {

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

@@ -56,20 +56,26 @@ struct DataRecord {
       std::vector<float> slot_data;
       split_to_float(data[1], ' ', &slot_data);
       std::string name = data[0];
-      PADDLE_ENFORCE_EQ(slot_data.size() % 11, 0UL,
-                        "line %d, %s should be divisible", num_lines, name);
+      PADDLE_ENFORCE_EQ(
+          slot_data.size() % 11, 0UL,
+          paddle::platform::errors::Fatal("line %d, %s should be divisible",
+                                          num_lines, name));
       datasets[name].emplace_back(std::move(slot_data));
     }
     num_samples = num_lines / num_slots;
-    PADDLE_ENFORCE_EQ(num_samples * num_slots, static_cast<size_t>(num_lines),
-                      "num samples should be divisible");
-    PADDLE_ENFORCE_GT(num_samples, 0UL);
+    PADDLE_ENFORCE_EQ(
+        num_samples * num_slots, static_cast<size_t>(num_lines),
+        paddle::platform::errors::Fatal("num samples should be divisible"));
+    PADDLE_ENFORCE_GT(num_samples, 0UL,
+                      paddle::platform::errors::Fatal(
+                          "The num of samples should be greater than 0."));
   }
 
   void Prepare(int bs) {
     for (auto it = datasets.begin(); it != datasets.end(); ++it) {
-      PADDLE_ENFORCE_EQ(it->second.size(), num_samples,
-                        "size of each slot should be equal");
+      PADDLE_ENFORCE_EQ(
+          it->second.size(), num_samples,
+          paddle::platform::errors::Fatal("size of each slot should be equal"));
     }
     size_t num_batches = num_samples / bs;
     EXPECT_GT(num_batches, 0UL);
@@ -90,8 +96,10 @@ struct DataRecord {
           std::copy(datas[id].begin(), datas[id].end(),
                     std::back_inserter(slot.data[k]));
           size_t len = datas[id].size() / 11;
-          PADDLE_ENFORCE_EQ(len * 11, datas[id].size(),
-                            "%s %d size should be divisible", slot.name, id);
+          PADDLE_ENFORCE_EQ(
+              len * 11, datas[id].size(),
+              paddle::platform::errors::Fatal("%s %d size should be divisible",
+                                              slot.name, id));
           lod[k + 1] = lod[k] + len;
         }
         slot.shape.assign({static_cast<int>(lod[bs]), 11});

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

@@ -22,7 +22,9 @@ struct DataReader {
       : file(new std::ifstream(path)) {}
 
   bool NextBatch(std::vector<PaddleTensor> *input, int batch_size) {
-    PADDLE_ENFORCE_EQ(batch_size, 1);
+    PADDLE_ENFORCE_EQ(batch_size, 1,
+                      paddle::platform::errors::Fatal(
+                          "The size of batch should be equal to 1."));
     std::string line;
     PaddleTensor tensor;
     tensor.dtype = PaddleDType::INT64;
@@ -81,7 +83,9 @@ TEST(Analyzer_Text_Classification, profile) {
 
   if (FLAGS_num_threads == 1) {
     // Get output
-    PADDLE_ENFORCE_GT(outputs.size(), 0);
+    PADDLE_ENFORCE_GT(outputs.size(), 0,
+                      paddle::platform::errors::Fatal(
+                          "The size of output should be greater than 0."));
     LOG(INFO) << "get outputs " << outputs.back().size();
     for (auto &output : outputs.back()) {
       LOG(INFO) << "output.shape: " << to_string(output.shape);

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

@@ -59,7 +59,9 @@ void SetConfig(AnalysisConfig *cfg) {
 }
 
 void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
-  PADDLE_ENFORCE_EQ(FLAGS_test_all_data, 0, "Only have single batch of data.");
+  PADDLE_ENFORCE_EQ(
+      FLAGS_test_all_data, 0,
+      paddle::platform::errors::Fatal("Only have single batch of data."));
   std::string line;
   std::ifstream file(FLAGS_infer_data);
   std::getline(file, line);
@@ -99,7 +101,9 @@ void profile(bool use_mkldnn = false) {
     auto refer = ProcessALine(line);
     file.close();
 
-    PADDLE_ENFORCE_GT(outputs.size(), 0);
+    PADDLE_ENFORCE_GT(outputs.size(), 0,
+                      paddle::platform::errors::Fatal(
+                          "The size of output should be greater than 0."));
     auto &output = outputs.back().front();
     size_t numel = output.data.length() / PaddleDtypeSize(output.dtype);
     CHECK_EQ(numel, refer.data.size());

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

@@ -12,15 +12,33 @@ 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 <dirent.h>
 #include <gflags/gflags.h>
 #include <glog/logging.h>
 #include <gtest/gtest.h>
+#include <unistd.h>
 
 #include "paddle/fluid/inference/tests/api/trt_test_helper.h"
 
 namespace paddle {
 namespace inference {
 
+int DeleteCache(std::string path) {
+  DIR* dir = opendir(path.c_str());
+  if (dir == NULL) return 0;
+  struct dirent* ptr;
+  while ((ptr = readdir(dir)) != NULL) {
+    if (std::strcmp(ptr->d_name, ".") == 0 ||
+        std::strcmp(ptr->d_name, "..") == 0) {
+      continue;
+    } else if (ptr->d_type == 8) {
+      std::string file_rm = path + "/" + ptr->d_name;
+      return remove(file_rm.c_str());
+    }
+  }
+  return 0;
+}
+
 void run(const AnalysisConfig& config, std::vector<float>* out_data) {
   auto predictor = CreatePaddlePredictor(config);
   auto input_names = predictor->GetInputNames();
@@ -86,6 +104,11 @@ void run(const AnalysisConfig& config, std::vector<float>* out_data) {
 void trt_ernie(bool with_fp16, std::vector<float> result) {
   AnalysisConfig config;
   std::string model_dir = FLAGS_infer_model;
+  // Delete serialization cache to perform serialization first rather than
+  // deserialization.
+  std::string opt_cache_dir = FLAGS_infer_model + "/_opt_cache";
+  DeleteCache(opt_cache_dir);
+
   SetConfig(&config, model_dir, true /* use_gpu */);
 
   config.SwitchUseFeedFetchOps(false);

paddle/fluid/inference/tests/test_helper.h

@@ -21,6 +21,7 @@ limitations under the License. */
 
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/inference/io.h"
+#include "paddle/fluid/platform/errors.h"
 #include "paddle/fluid/platform/port.h"
 #include "paddle/fluid/platform/profiler.h"
 
@@ -162,7 +163,8 @@ void TestInference(const std::string& dirname,
     //   int device_id = place.GetDeviceId();
     paddle::platform::SetDeviceId(0);
 #else
-    PADDLE_THROW("'CUDAPlace' is not supported in CPU only device.");
+    PADDLE_THROW(paddle::platform::errors::Unavailable(
+        "'CUDAPlace' is not supported in CPU only device."));
 #endif
   }
 

paddle/fluid/memory/allocation/best_fit_allocator_test.cu

@@ -16,6 +16,7 @@
 #include <random>
 #include <thread>  // NOLINT
 #include <vector>
+
 #include "gtest/gtest.h"
 #include "paddle/fluid/memory/allocation/best_fit_allocator.h"
 #include "paddle/fluid/memory/allocation/cuda_allocator.h"
@@ -41,12 +42,14 @@ TEST(BestFitAllocator, concurrent_cuda) {
   LockedAllocator concurrent_allocator(
       std::unique_ptr<Allocator>(new BestFitAllocator(cuda_allocation.get())));
 
+  platform::CUDAPlace gpu(0);
+  platform::CUDADeviceContext dev_ctx(gpu);
+
   auto th_main = [&](std::random_device::result_type seed) {
     std::default_random_engine engine(seed);
     std::uniform_int_distribution<size_t> dist(1U, 1024U);
-    platform::CUDAPlace gpu(0);
-    platform::CUDADeviceContext dev_ctx(gpu);
     std::array<size_t, 1024> buf;
+
     for (size_t i = 0; i < 128; ++i) {
       size_t allocate_size = dist(engine);
 

paddle/fluid/operators/arg_min_max_op_base.h

@@ -110,10 +110,12 @@ struct VisitDataArgMinMaxFunctor {
         CALL_ARG_MINMAX_FUNCTOR(6);
         break;
       default:
-        PADDLE_THROW(
+        PADDLE_ENFORCE_LE(
+            x_dims.size(), 6,
+            platform::errors::InvalidArgument(
                 "%s operator doesn't supports tensors whose ranks are greater "
                 "than 6.",
-            (EnumArgMinMaxValue == kArgMin ? "argmin" : "argmax"));
+                (EnumArgMinMaxValue == kArgMin ? "argmin" : "argmax")));
         break;
 #undef CALL_ARG_MINMAX_FUNCTOR
     }
@@ -164,7 +166,8 @@ class ArgMinMaxOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_LT(
         axis, x_dims.size(),
         platform::errors::InvalidArgument(
-            "'axis'(%d) must be less than Rank(X)(%d).", axis, x_dims.size()));
+            "'axis'(%d) must be less than Rank(X)(%d) of Input(X).", axis,
+            x_dims.size()));
 
     const int& dtype = ctx->Attrs().Get<int>("dtype");
     PADDLE_ENFORCE_EQ(
@@ -192,10 +195,11 @@ class ArgMinMaxOp : public framework::OperatorWithKernel {
         }
         PADDLE_ENFORCE_LE(
             all_element_num, INT_MAX,
+            platform::errors::InvalidArgument(
                 "The element num of the argmin/argmax input at axis is "
                 "%d, is larger than int32 maximum value:%d, you must "
                 "set the dtype of argmin/argmax to 'int64'.",
-            all_element_num, INT_MAX);
+                all_element_num, INT_MAX));
       }
     }
     std::vector<int64_t> vec;

paddle/fluid/operators/assign_op.h

@@ -52,7 +52,10 @@ class AssignFunctor {
 
   template <typename T>
   void operator()(const T &v) const {
-    PADDLE_THROW("Not support type for assign op %s", typeid(T).name());
+    PADDLE_ENFORCE_EQ(
+        true, false,
+        platform::errors::PermissionDenied(
+            "Not support type for assign op with type %s", typeid(T).name()));
   }
 
  private:

paddle/fluid/operators/cudnn_lstm_cache.h

+/* Copyright (c) 2020 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/tensor.h"
+#include "paddle/fluid/platform/cudnn_helper.h"
+#include "paddle/fluid/platform/dynload/cudnn.h"
+
+namespace paddle {
+namespace operators {
+
+class ScopedRNNBase {
+ public:
+  ScopedRNNBase(int seq_length, int batch_size, int input_size, int hidden_size,
+                int num_layers, float dropout_prob, int seed, int weight_numel,
+                bool initialized, bool is_bidirec)
+      : seq_length_(seq_length),
+        batch_size_(batch_size),
+        input_size_(input_size),
+        hidden_size_(hidden_size),
+        num_layers_(num_layers),
+        dropout_prob_(dropout_prob),
+        seed_(seed),
+        weight_numel_(weight_numel),
+        initialized_(initialized),
+        is_bidirec_(is_bidirec) {}
+
+  template <typename T>
+  void Create(const cudnnHandle_t& handle, const platform::Place& place,
+              const std::vector<int>& sequence_length, size_t* workspace_size,
+              size_t* reserve_size, framework::Tensor* dropout_state) {
+    int numDirections = is_bidirec_ ? 2 : 1;
+    cudnnDataType_t cudnn_type = platform::CudnnDataType<T>::type;
+
+    // ------------------- cudnn x, y descriptors ---------------------
+    std::vector<int> dims_x = {batch_size_, input_size_, 1};
+    std::vector<int> strides_x = {input_size_, 1, 1};
+    std::vector<int> dims_y = {batch_size_, hidden_size_ * numDirections, 1};
+    std::vector<int> strides_y = {hidden_size_ * numDirections, 1, 1};
+    for (int i = 0; i < seq_length_; ++i) {
+      x_descs_.emplace_back(x_desc_.descriptor<T>(dims_x, strides_x));
+      y_descs_.emplace_back(y_desc_.descriptor<T>(dims_y, strides_y));
+    }
+    if (!sequence_length.empty()) {
+      x_seq_desc_.descriptor<T>(seq_length_, batch_size_, input_size_, true,
+                                sequence_length);
+      y_seq_desc_.descriptor<T>(seq_length_, batch_size_,
+                                hidden_size_ * numDirections, true,
+                                sequence_length);
+    }
+
+    // ------------------- cudnn hx, hy, cx, cy descriptors----------
+    std::vector<int> dims_hx = {num_layers_ * numDirections, batch_size_,
+                                hidden_size_};
+    std::vector<int> strides_hx = {hidden_size_ * batch_size_, hidden_size_, 1};
+    init_h_desc_.descriptor<T>(dims_hx, strides_hx);
+    init_c_desc_.descriptor<T>(dims_hx, strides_hx);
+    last_h_desc_.descriptor<T>(dims_hx, strides_hx);
+    last_c_desc_.descriptor<T>(dims_hx, strides_hx);
+
+    // ------------------- cudnn dropout descriptors ---------------------
+    size_t state_size;
+    if (!initialized_) {
+      PADDLE_ENFORCE_CUDA_SUCCESS(
+          platform::dynload::cudnnDropoutGetStatesSize(handle, &state_size));
+      dropout_state->mutable_data<uint8_t>({static_cast<int64_t>(state_size)},
+                                           place);
+    }
+    dropout_desc_.descriptor(handle, place, initialized_, dropout_prob_,
+                             dropout_state, seed_, state_size);
+
+// ------------------- cudnn rnn descriptors ---------------------
+#if CUDNN_VERSION >= 6000
+    PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetRNNDescriptor_v6(
+        handle, rnn_desc_.desc(), hidden_size_, num_layers_,
+        dropout_desc_.desc(), CUDNN_LINEAR_INPUT,
+        is_bidirec_ ? CUDNN_BIDIRECTIONAL : CUDNN_UNIDIRECTIONAL, CUDNN_LSTM,
+        CUDNN_RNN_ALGO_STANDARD, cudnn_type));
+#else
+    PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetRNNDescriptor(
+        rnn_desc_.desc(), hidden_size_, num_layers_, dropout_desc_.desc(),
+        CUDNN_LINEAR_INPUT,
+        is_bidirec_ ? CUDNN_BIDIRECTIONAL : CUDNN_UNIDIRECTIONAL, CUDNN_LSTM,
+        cudnn_type));
+#endif
+    if (!sequence_length.empty()) {
+      PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetRNNPaddingMode(
+          rnn_desc_.desc(), CUDNN_RNN_PADDED_IO_ENABLED));
+    }
+
+    // ------------------- cudnn weights_size ---------------------
+    size_t weights_size_;
+    PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnGetRNNParamsSize(
+        handle, rnn_desc_.desc(), x_descs_[0], &weights_size_, cudnn_type));
+    PADDLE_ENFORCE_EQ(
+        weights_size_, sizeof(T) * weight_numel_,
+        platform::errors::InvalidArgument(
+            "The cudnn lstm and setting weight size should be same."));
+    // ------------------- cudnn weight descriptors ---------------------
+    platform::DataLayout layout = platform::DataLayout::kNCHW;
+    int dim_tmp = weights_size_ / sizeof(T);
+    std::vector<int> dim_w = {dim_tmp, 1, 1};
+    weight_desc_.descriptor<T>(layout, dim_w);
+    // ------------------- cudnn workspace, reserve size ---------------------
+    PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnGetRNNWorkspaceSize(
+        handle, rnn_desc_.desc(), seq_length_, x_descs_.data(),
+        workspace_size));
+    PADDLE_ENFORCE_CUDA_SUCCESS(
+        platform::dynload::cudnnGetRNNTrainingReserveSize(
+            handle, rnn_desc_.desc(), seq_length_, x_descs_.data(),
+            reserve_size));
+  }
+  cudnnTensorDescriptor_t* x_descs() { return x_descs_.data(); }
+  cudnnTensorDescriptor_t* y_descs() { return y_descs_.data(); }
+  cudnnRNNDataDescriptor_t x_seq_desc() { return x_seq_desc_.desc(); }
+  cudnnRNNDataDescriptor_t y_seq_desc() { return y_seq_desc_.desc(); }
+  cudnnTensorDescriptor_t init_h_desc() { return init_h_desc_.desc(); }
+  cudnnTensorDescriptor_t init_c_desc() { return init_c_desc_.desc(); }
+  cudnnTensorDescriptor_t last_h_desc() { return last_h_desc_.desc(); }
+  cudnnTensorDescriptor_t last_c_desc() { return last_c_desc_.desc(); }
+  cudnnRNNDescriptor_t rnn_desc() { return rnn_desc_.desc(); }
+  cudnnDropoutDescriptor_t dropout_desc() { return dropout_desc_.desc(); }
+  cudnnFilterDescriptor_t weight_desc() { return weight_desc_.desc(); }
+
+ private:
+  int seq_length_;
+  int batch_size_;
+  int input_size_;
+  int hidden_size_;
+  int num_layers_;
+  float dropout_prob_;
+  int seed_;
+  int weight_numel_;
+  bool initialized_;
+  bool is_bidirec_;
+  std::vector<cudnnTensorDescriptor_t> x_descs_;
+  std::vector<cudnnTensorDescriptor_t> y_descs_;
+
+  platform::ScopedTensorDescriptor x_desc_;
+  platform::ScopedTensorDescriptor y_desc_;
+  platform::ScopedRNNTensorDescriptor x_seq_desc_;
+  platform::ScopedRNNTensorDescriptor y_seq_desc_;
+  platform::ScopedTensorDescriptor init_h_desc_;
+  platform::ScopedTensorDescriptor init_c_desc_;
+  platform::ScopedTensorDescriptor last_h_desc_;
+  platform::ScopedTensorDescriptor last_c_desc_;
+  platform::ScopedDropoutDescriptor dropout_desc_;
+  platform::ScopedFilterDescriptor weight_desc_;
+  platform::ScopedRNNDescriptor rnn_desc_;
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/cudnn_lstm_op.cc

@@ -51,6 +51,16 @@ class CudnnLSTMOp : public framework::OperatorWithKernel {
                           "received InitH's rank is %d.",
                           init_h_dims.size()));
 
+    if (ctx->HasInput("SequenceLength")) {
+      auto seq_dims = ctx->GetInputDim("SequenceLength");
+      PADDLE_ENFORCE_EQ(
+          in_dims[1], seq_dims[0],
+          platform::errors::InvalidArgument(
+              "The size of SequenceLength has to equal the batch_size. But "
+              "received batch_size is %d and the size of SequenceLength is %d.",
+              in_dims[1], seq_dims[0]));
+    }
+
     PADDLE_ENFORCE_EQ(
         in_dims[1], init_h_dims[1],
         platform::errors::InvalidArgument(
@@ -113,6 +123,12 @@ class CudnnLSTMOpMaker : public framework::OpProtoAndCheckerMaker {
              "(Tensor) the learnable hidden-hidden weights."
              " The shape is (N), where N is total weight size of the LSTM. "
              " cudnn concatenate all the weight to one Tensor");
+    AddInput("SequenceLength",
+             "(Tensor) When the input data is padding, "
+             "set this parameter. This parameter represents "
+             "the variable sequence lengths in a batch. "
+             "The size of the vector has to equal the batch_size.")
+        .AsDispensable();
     AddOutput("Reserve",
               "(Tensor, a temporary output Tensor to store the reserve_data "
               "of cudnn kernel.")
@@ -155,13 +171,6 @@ class CudnnLSTMOpMaker : public framework::OpProtoAndCheckerMaker {
         .SetDefault(1);
     AddAttr<bool>("is_test", "True if in test phase.").SetDefault(false);
     AddAttr<int>("seed", "seed to used if fix_seed is True").SetDefault(0);
-    AddAttr<std::vector<int>>("sequence_length",
-                              "(vector<int>) When the input data is padding, "
-                              "set this parameter. This parameter represents "
-                              "the variable sequence"
-                              "lengths in a batch. The size of the vector has "
-                              "to equal the batch_size.")
-        .SetDefault({});
     AddComment(R"DOC(
 CUDNN LSTM implementation
 
@@ -243,6 +252,9 @@ class CudnnLSTMGradOpMaker : public framework::SingleGradOpMaker<T> {
     op->SetInput("InitH", this->Input("InitH"));
     op->SetInput("InitC", this->Input("InitC"));
     op->SetInput("W", this->Input("W"));
+    if (this->HasInput("SequenceLength")) {
+      op->SetInput("SequenceLength", this->Input("SequenceLength"));
+    }
     op->SetInput("Reserve", this->Output("Reserve"));
     op->SetInput("StateOut", this->Output("StateOut"));
     op->SetInput("Out", this->Output("Out"));

paddle/fluid/operators/cudnn_lstm_op.cu.cc

@@ -13,8 +13,9 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/framework/op_registry.h"
-#include "paddle/fluid/operators/cudnn_rnn_cache.h"
+#include "paddle/fluid/operators/cudnn_lstm_cache.h"
 #include "paddle/fluid/operators/math/math_function.h"
+#include "paddle/fluid/operators/utils.h"
 #include "paddle/fluid/platform/cudnn_desc.h"
 #include "paddle/fluid/platform/cudnn_helper.h"
 
@@ -24,6 +25,43 @@ namespace operators {
 using LoDTensor = framework::LoDTensor;
 using Tensor = framework::Tensor;
 
+template <typename T>
+void LSTMInferece(const bool &has_seq_length, const cudnnHandle_t &handle,
+                  const int &seq_length, ScopedRNNBase *rnn, const T *x_data,
+                  const T *init_h_data, const T *init_c_data, const T *w_data,
+                  T *out_data, T *last_h_data, T *last_c_data,
+                  framework::Tensor *workspace_data,
+                  const size_t &workspace_size) {
+  if (!has_seq_length) {
+    // for inference
+    // This interface is used when the input/output is unpadded.
+    PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnRNNForwardInference(
+        handle, rnn->rnn_desc(), seq_length, rnn->x_descs(), x_data,
+        rnn->init_h_desc(), init_h_data, rnn->init_c_desc(), init_c_data,
+        rnn->weight_desc(), w_data, rnn->y_descs(), out_data,
+        rnn->last_h_desc(), last_h_data, rnn->last_c_desc(), last_c_data,
+        workspace_data->data<uint8_t>(), workspace_size));
+  } else {
+#if CUDNN_VERSION >= 7201
+    // for inference
+    // This interface is used when the input/output is padded.
+    PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnRNNForwardInferenceEx(
+        handle, rnn->rnn_desc(), rnn->x_seq_desc(), x_data, rnn->init_h_desc(),
+        init_h_data, rnn->init_c_desc(), init_c_data, rnn->weight_desc(),
+        w_data, rnn->y_seq_desc(), out_data, rnn->last_h_desc(), last_h_data,
+        rnn->last_c_desc(), last_c_data, nullptr, nullptr, nullptr, nullptr,
+        nullptr, nullptr, nullptr, nullptr, workspace_data->data<uint8_t>(),
+        workspace_size));
+#else
+    // CUDNN VERSION has to >=7.2.1
+    PADDLE_THROW(platform::errors::Unavailable(
+        "The padded input is supported by "
+        "cudnnRNNForwardInferenceEx, but it only works when "
+        "the version of cudnn is larger than 7.2.1"));
+#endif
+  }
+}
+
 template <typename T>
 class CudnnLSTMGPUKernel : public framework::OpKernel<T> {
  public:
@@ -56,7 +94,13 @@ class CudnnLSTMGPUKernel : public framework::OpKernel<T> {
     int num_layers = ctx.Attr<int>("num_layers");
     bool is_test = ctx.Attr<bool>("is_test");
     int seed = ctx.Attr<int>("seed");
-    auto sequence_length = ctx.Attr<std::vector<int>>("sequence_length");
+
+    bool has_seq_length = ctx.HasInput("SequenceLength");
+    std::vector<int> SequenceLength;
+    if (has_seq_length) {
+      auto *sequence_length = ctx.Input<Tensor>("SequenceLength");
+      SequenceLength = operators::GetDataFromTensor<int>(sequence_length);
+    }
 
     auto &dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
     auto handle = dev_ctx.cudnn_handle();
@@ -70,58 +114,32 @@ class CudnnLSTMGPUKernel : public framework::OpKernel<T> {
     size_t workspace_size;
     size_t reserve_size;
 
-    platform::ScopedRNNBase rnn(seq_length, batch_size, input_size, hidden_size,
+    ScopedRNNBase rnn(seq_length, batch_size, input_size, hidden_size,
                       num_layers, dropout_prob, seed, weight_numel,
                       state_initialized, is_bidirec);
-    rnn.Create<T>(handle, ctx.GetPlace(), sequence_length, &workspace_size,
+    rnn.Create<T>(handle, ctx.GetPlace(), SequenceLength, &workspace_size,
                   &reserve_size, state_out);
 
     framework::Tensor workspace_data_;
-    workspace_data_.Resize({static_cast<int64_t>(workspace_size)});
-    workspace_data_.mutable_data<uint8_t>(ctx.GetPlace());
+    workspace_data_.mutable_data<uint8_t>(
+        {static_cast<int64_t>(workspace_size)}, ctx.GetPlace());
 
     auto *reserve_data = reserve->mutable_data<uint8_t>(
         {static_cast<int64_t>(reserve_size)}, ctx.GetPlace());
 
     if (is_test) {
-      if (sequence_length.empty()) {
-        // for inference
-        // This interface is used when the input/output is unpadded.
-        PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnRNNForwardInference(
-            handle, rnn.rnn_desc(), seq_length, rnn.x_desc(), x_data,
-            rnn.hx_desc(), init_h_data, rnn.cx_desc(), init_c_data,
-            rnn.w_desc(), w_data, rnn.y_desc(), out_data, rnn.hy_desc(),
-            last_h_data, rnn.cy_desc(), last_c_data,
-            workspace_data_.data<uint8_t>(), workspace_size));
+      LSTMInferece<T>(has_seq_length, handle, seq_length, &rnn, x_data,
+                      init_h_data, init_c_data, w_data, out_data, last_h_data,
+                      last_c_data, &workspace_data_, workspace_size);
     } else {
-#if CUDNN_VERSION >= 7201
-        // for inference
-        // This interface is used when the input/output is padded.
-        PADDLE_ENFORCE_CUDA_SUCCESS(
-            platform::dynload::cudnnRNNForwardInferenceEx(
-                handle, rnn.rnn_desc(), rnn.x_seq_desc(), x_data, rnn.hx_desc(),
-                init_h_data, rnn.cx_desc(), init_c_data, rnn.w_desc(), w_data,
-                rnn.y_seq_desc(), out_data, rnn.hy_desc(), last_h_data,
-                rnn.cy_desc(), last_c_data, nullptr, nullptr, nullptr, nullptr,
-                nullptr, nullptr, nullptr, nullptr,
-                workspace_data_.data<uint8_t>(), workspace_size));
-#else
-        PADDLE_ENFORCE_NOT_NULL(
-            nullptr, platform::errors::Unavailable(
-                         "The padded input is supported by "
-                         "cudnnRNNForwardInferenceEx, but it only works when "
-                         "the version of cudnn is larger than 7.2.1"));
-#endif
-      }
-    } else {
-      if (sequence_length.empty()) {
+      if (!has_seq_length) {
         // for train
         // This interface is used when the input/output is unpadded.
         PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnRNNForwardTraining(
-            handle, rnn.rnn_desc(), seq_length, rnn.x_desc(), x_data,
-            rnn.hx_desc(), init_h_data, rnn.cx_desc(), init_c_data,
-            rnn.w_desc(), w_data, rnn.y_desc(), out_data, rnn.hy_desc(),
-            last_h_data, rnn.cy_desc(), last_c_data,
+            handle, rnn.rnn_desc(), seq_length, rnn.x_descs(), x_data,
+            rnn.init_h_desc(), init_h_data, rnn.init_c_desc(), init_c_data,
+            rnn.weight_desc(), w_data, rnn.y_descs(), out_data,
+            rnn.last_h_desc(), last_h_data, rnn.last_c_desc(), last_c_data,
             workspace_data_.data<uint8_t>(), workspace_size, reserve_data,
             reserve_size));
       } else {
@@ -130,16 +148,15 @@ class CudnnLSTMGPUKernel : public framework::OpKernel<T> {
         // This interface is used when the input/output is padded.
         PADDLE_ENFORCE_CUDA_SUCCESS(
             platform::dynload::cudnnRNNForwardTrainingEx(
-                handle, rnn.rnn_desc(), rnn.x_seq_desc(), x_data, rnn.hx_desc(),
-                init_h_data, rnn.cx_desc(), init_c_data, rnn.w_desc(), w_data,
-                rnn.y_seq_desc(), out_data, rnn.hy_desc(), last_h_data,
-                rnn.cy_desc(), last_c_data, nullptr, nullptr, nullptr, nullptr,
-                nullptr, nullptr, nullptr, nullptr,
-                workspace_data_.data<uint8_t>(), workspace_size, reserve_data,
-                reserve_size));
+                handle, rnn.rnn_desc(), rnn.x_seq_desc(), x_data,
+                rnn.init_h_desc(), init_h_data, rnn.init_c_desc(), init_c_data,
+                rnn.weight_desc(), w_data, rnn.y_seq_desc(), out_data,
+                rnn.last_h_desc(), last_h_data, rnn.last_c_desc(), last_c_data,
+                nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr,
+                nullptr, workspace_data_.data<uint8_t>(), workspace_size,
+                reserve_data, reserve_size));
 #else
-        PADDLE_ENFORCE_NOT_NULL(
-            nullptr, platform::errors::Unavailable(
+        PADDLE_THROW(platform::errors::Unavailable(
             "The padded input is supported by "
             "cudnnRNNForwardTrainingEx, but it only works when "
             "the version of cudnn is larger than 7.2.1"));
@@ -203,7 +220,13 @@ class CudnnLSTMGPUGradKernel : public framework::OpKernel<T> {
     int hidden_size = ctx.Attr<int>("hidden_size");
     int num_layers = ctx.Attr<int>("num_layers");
     int seed = ctx.Attr<int>("seed");
-    auto sequence_length = ctx.Attr<std::vector<int>>("sequence_length");
+
+    bool has_seq_length = ctx.HasInput("SequenceLength");
+    std::vector<int> SequenceLength;
+    if (has_seq_length) {
+      auto *sequence_length = ctx.Input<Tensor>("SequenceLength");
+      SequenceLength = operators::GetDataFromTensor<int>(sequence_length);
+    }
 
     int seq_length = input_dims[0];
     int batch_size = input->dims()[1];
@@ -213,33 +236,33 @@ class CudnnLSTMGPUGradKernel : public framework::OpKernel<T> {
     size_t workspace_size;
     size_t reserve_size;
 
-    platform::ScopedRNNBase rnn(seq_length, batch_size, input_size, hidden_size,
-                                num_layers, dropout_prob, seed, weight_numel,
-                                true, is_bidirec);
+    ScopedRNNBase rnn(seq_length, batch_size, input_size, hidden_size,
+                      num_layers, dropout_prob, seed, weight_numel, true,
+                      is_bidirec);
 
-    rnn.Create<T>(handle, ctx.GetPlace(), sequence_length, &workspace_size,
+    rnn.Create<T>(handle, ctx.GetPlace(), SequenceLength, &workspace_size,
                   &reserve_size, const_cast<Tensor *>(state_out));
 
     framework::Tensor workspace_data_;
-    workspace_data_.Resize({static_cast<int64_t>(workspace_size)});
-    workspace_data_.mutable_data<uint8_t>(ctx.GetPlace());
+    workspace_data_.mutable_data<uint8_t>(
+        {static_cast<int64_t>(workspace_size)}, ctx.GetPlace());
     const uint8_t *reserve_data = reserve->data<uint8_t>();
 
-    if (sequence_length.empty()) {
+    if (!has_seq_length) {
       // This interface is used when the input/output is unpadded.
       PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnRNNBackwardData(
-          handle, rnn.rnn_desc(), seq_length, rnn.y_desc(), out_data,
-          rnn.y_desc(), out_grad_data, rnn.hy_desc(), last_h_grad_data,
-          rnn.cy_desc(), last_c_grad_data, rnn.w_desc(), weight_data,
-          rnn.hx_desc(), init_h_data, rnn.cx_desc(), init_c_data, rnn.x_desc(),
-          in_grad_data, rnn.hx_desc(), init_h_grad_data, rnn.cx_desc(),
-          init_c_grad_data, workspace_data_.data<uint8_t>(), workspace_size,
-          const_cast<uint8_t *>(reserve_data), reserve_size));
+          handle, rnn.rnn_desc(), seq_length, rnn.y_descs(), out_data,
+          rnn.y_descs(), out_grad_data, rnn.last_h_desc(), last_h_grad_data,
+          rnn.last_c_desc(), last_c_grad_data, rnn.weight_desc(), weight_data,
+          rnn.init_h_desc(), init_h_data, rnn.init_c_desc(), init_c_data,
+          rnn.x_descs(), in_grad_data, rnn.init_h_desc(), init_h_grad_data,
+          rnn.init_c_desc(), init_c_grad_data, workspace_data_.data<uint8_t>(),
+          workspace_size, const_cast<uint8_t *>(reserve_data), reserve_size));
 
       PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnRNNBackwardWeights(
-          handle, rnn.rnn_desc(), seq_length, rnn.x_desc(), input->data<T>(),
-          rnn.hx_desc(), init_h->data<T>(), rnn.y_desc(), out->data<T>(),
-          workspace_data_.data<uint8_t>(), workspace_size, rnn.w_desc(),
+          handle, rnn.rnn_desc(), seq_length, rnn.x_descs(), input->data<T>(),
+          rnn.init_h_desc(), init_h->data<T>(), rnn.y_descs(), out->data<T>(),
+          workspace_data_.data<uint8_t>(), workspace_size, rnn.weight_desc(),
           weight_grad->data<T>(), const_cast<uint8_t *>(reserve_data),
           reserve_size));
     } else {
@@ -248,24 +271,22 @@ class CudnnLSTMGPUGradKernel : public framework::OpKernel<T> {
       // This interface is used when the input/output is padded.
       PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnRNNBackwardDataEx(
           handle, rnn.rnn_desc(), rnn.y_seq_desc(), out_data, rnn.y_seq_desc(),
-          out_grad_data, nullptr, nullptr, rnn.hy_desc(), last_h_grad_data,
-          rnn.cy_desc(), last_c_grad_data, rnn.w_desc(), weight_data,
-          rnn.hx_desc(), init_h_data, rnn.cx_desc(), init_c_data,
-          rnn.x_seq_desc(), in_grad_data, rnn.hx_desc(), init_h_grad_data,
-          rnn.cx_desc(), init_c_grad_data, nullptr, nullptr,
+          out_grad_data, nullptr, nullptr, rnn.last_h_desc(), last_h_grad_data,
+          rnn.last_c_desc(), last_c_grad_data, rnn.weight_desc(), weight_data,
+          rnn.init_h_desc(), init_h_data, rnn.init_c_desc(), init_c_data,
+          rnn.x_seq_desc(), in_grad_data, rnn.init_h_desc(), init_h_grad_data,
+          rnn.init_c_desc(), init_c_grad_data, nullptr, nullptr,
           workspace_data_.data<uint8_t>(), workspace_size,
           const_cast<uint8_t *>(reserve_data), reserve_size));
 
       PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnRNNBackwardWeightsEx(
           handle, rnn.rnn_desc(), rnn.x_seq_desc(), input->data<T>(),
-          rnn.hx_desc(), init_h->data<T>(), rnn.y_seq_desc(), out->data<T>(),
-          workspace_data_.data<uint8_t>(), workspace_size, rnn.w_desc(),
-          weight_grad->data<T>(), const_cast<uint8_t *>(reserve_data),
-          reserve_size));
+          rnn.init_h_desc(), init_h->data<T>(), rnn.y_seq_desc(),
+          out->data<T>(), workspace_data_.data<uint8_t>(), workspace_size,
+          rnn.weight_desc(), weight_grad->data<T>(),
+          const_cast<uint8_t *>(reserve_data), reserve_size));
 #else
-      PADDLE_ENFORCE_NOT_NULL(
-          nullptr,
-          platform::errors::Unavailable(
+      PADDLE_THROW(platform::errors::Unavailable(
           "The padded input of rnn is supported by cudnnRNNBackwardDataEx, "
           "cudnnRNNBackwardWeightsEx, but it only works when the version "
           "of cudnn is larger than 7.2.1"));

paddle/fluid/operators/distributed_ops/allreduce_op.h

@@ -76,7 +76,8 @@ class AllReduceOpKernel : public framework::OpKernel<T> {
       PADDLE_ENFORCE_CUDA_SUCCESS(cudaStreamSynchronize(stream));
     }
 #else
-    PADDLE_THROW("PaddlePaddle should compile with GPU.");
+    PADDLE_THROW(platform::errors::PreconditionNotMet(
+        "PaddlePaddle should compile with GPU."));
 #endif
   }
 };

paddle/fluid/operators/distributed_ops/broadcast_op.cc

@@ -58,7 +58,8 @@ template <typename T>
 class BroadcastOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    PADDLE_THROW("Broadcast op can run on gpu place only for now.");
+    PADDLE_THROW(platform::errors::PreconditionNotMet(
+        "Broadcast op can run on gpu place only for now."));
   }
 };
 

paddle/fluid/operators/distributed_ops/broadcast_op.cu.cc

@@ -68,10 +68,11 @@ class NCCLBroadcastOpKernel : public framework::OpKernel<T> {
             << " From " << root_dev_id << " to " << dev_id;
 
     if (ctx.Attr<bool>("sync_mode")) {
-      PADDLE_ENFORCE(cudaStreamSynchronize(stream));
+      PADDLE_ENFORCE_CUDA_SUCCESS(cudaStreamSynchronize(stream));
     }
 #else
-    PADDLE_THROW("PaddlePaddle should compile with GPU.");
+    PADDLE_THROW(platform::errors::PreconditionNotMet(
+        "PaddlePaddle should compile with GPU."));
 #endif
   }
 };

paddle/fluid/operators/elementwise/test_elementwise_add_op_inplace.cc

@@ -33,9 +33,12 @@ namespace operators {
 static void Memcpy(void *dst, const void *src, size_t n, bool copy_to_gpu) {
   if (copy_to_gpu) {
 #ifdef PADDLE_WITH_CUDA
-    PADDLE_ENFORCE(cudaMemcpy(dst, src, n, cudaMemcpyHostToDevice));
+    PADDLE_ENFORCE_CUDA_SUCCESS(
+        cudaMemcpy(dst, src, n, cudaMemcpyHostToDevice));
 #else
-    PADDLE_THROW("Not compiled with cuda");
+    PADDLE_THROW(
+        platform::errors::InvalidArgument("Check your paddle version, current "
+                                          "version is not compiled with cuda"));
 #endif
   } else {
     std::memcpy(dst, src, n);
@@ -88,11 +91,22 @@ bool TestMain(const platform::Place &place, const framework::DDim &dims,
 
   framework::LoDTensor cpu_out;
   auto &out_tensor = scope.FindVar(out_name)->Get<framework::LoDTensor>();
-  PADDLE_ENFORCE(scope.kids().empty());
+  PADDLE_ENFORCE_EQ(scope.kids().empty(), true,
+                    platform::errors::InvalidArgument(
+                        "The scope can not have the child scopes,"
+                        "please check your code."));
   if (inplace) {
-    PADDLE_ENFORCE_EQ(&out_tensor, x);
+    PADDLE_ENFORCE_EQ(
+        &out_tensor, x,
+        platform::errors::InvalidArgument(
+            "The output tensor should be same as input x in inplace mode,"
+            " but now is not same."));
   } else {
-    PADDLE_ENFORCE_EQ(&out_tensor, z);
+    PADDLE_ENFORCE_EQ(
+        &out_tensor, z,
+        platform::errors::InvalidArgument(
+            "The output tensor should be same as output z in normal mode,"
+            " but now is not same."));
   }
 
   if (is_gpu_place) {

paddle/fluid/operators/elementwise/test_elementwise_op_grad_grad.h

@@ -92,7 +92,9 @@ class TestElementwiseOpGradGrad {
         auto dst_place = BOOST_GET_CONST(platform::CUDAPlace, place_);
         memory::Copy(dst_place, dst, src_place, src, bytes, nullptr);
 #else
-        PADDLE_THROW("Not compiled with cuda");
+        PADDLE_THROW(platform::errors::InvalidArgument(
+            "Check your paddle version, current version is not compiled with "
+            "cuda"));
 #endif
       }
     }
@@ -107,7 +109,10 @@ class TestElementwiseOpGradGrad {
     op->Run(scope_, place_);
     platform::DeviceContextPool::Instance().Get(place_)->Wait();
     framework::LoDTensor cpu_out;
-    PADDLE_ENFORCE_EQ(scope_.kids().empty(), true, "scope has child scopes");
+    PADDLE_ENFORCE_EQ(scope_.kids().empty(), true,
+                      platform::errors::InvalidArgument(
+                          "The scope can not have the child scopes,"
+                          "please check your code."));
 
     // get outputs from scope and compare them with expected_outs
     bool all_equal = true;

paddle/fluid/operators/gather_op.cc

@@ -37,8 +37,21 @@ class GatherOp : public framework::OperatorWithKernel {
                           "Output(Out) of GatherOp should not be null."));
 
     auto index_dims = ctx->GetInputDim("Index");
-    PADDLE_ENFORCE(index_dims.size() == 1 ||
-                   (index_dims.size() == 2 && index_dims[1] == 1));
+
+    if (index_dims.size() == 2) {
+      PADDLE_ENFORCE_EQ(
+          index_dims[1], 1,
+          platform::errors::InvalidArgument(
+              "The last dim of index should be 1 when it is 2D, but we get %d",
+              index_dims[1]));
+    } else {
+      PADDLE_ENFORCE_EQ(
+          index_dims.size(), 1,
+          platform::errors::InvalidArgument(
+              "The index should be 1D, when it is not 2D, but we get %d",
+              index_dims.size()));
+    }
+
     int batch_size = ctx->GetInputDim("Index")[0];
     framework::DDim output_dims(ctx->GetInputDim("X"));
     output_dims[0] = batch_size;

paddle/fluid/operators/isfinite_op.cc

@@ -43,7 +43,11 @@ class OverflowOp : public framework::OperatorWithKernel {
     } else if (x_var->IsType<framework::SelectedRows>()) {
       dtype = x_var->Get<framework::SelectedRows>().value().type();
     } else {
-      PADDLE_THROW("Cannot find the input data type by all input data");
+      PADDLE_ENFORCE_EQ(
+          true, false,
+          platform::errors::InvalidArgument(
+              "The input type mismatch, the type of Input(X) must be Tensor or "
+              "SelectedRows, please check your input."));
     }
     return framework::OpKernelType(framework::proto::VarType::Type(dtype),
                                    ctx.GetPlace());

paddle/fluid/operators/isfinite_op.h

@@ -57,7 +57,11 @@ class OverflowKernel : public framework::OpKernel<T> {
       auto& in = ctx.Input<framework::SelectedRows>("X")->value();
       functor(in, out);
     } else {
-      PADDLE_THROW("Unsupported input type.");
+      PADDLE_ENFORCE_EQ(
+          true, false,
+          platform::errors::InvalidArgument(
+              "The input type mismatch, the type of Input(X) must be Tensor or "
+              "SelectedRows, please check your input."));
     }
   }
 };

paddle/fluid/operators/linspace_op.cc

@@ -22,8 +22,6 @@ class LinspaceOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("Start"),
-                   "Input(Start) of LinspaceOp should not be null.");
     OP_INOUT_CHECK(ctx->HasInput("Start"), "Input", "Start", "linspace");
     OP_INOUT_CHECK(ctx->HasInput("Stop"), "Input", "Stop", "linspace");
     OP_INOUT_CHECK(ctx->HasInput("Num"), "Input", "Num", "linspace");

paddle/fluid/operators/linspace_op.cu

@@ -63,7 +63,10 @@ class CUDALinspaceKernel : public framework::OpKernel<T> {
     framework::TensorCopy(*num_t, platform::CPUPlace(), &n);
     int32_t num = n.data<int32_t>()[0];
 
-    PADDLE_ENFORCE(num > 0, "The num of linspace op should be larger than 0.");
+    PADDLE_ENFORCE_GT(num, 0, platform::errors::InvalidArgument(
+                                  "The num of linspace op should be larger "
+                                  "than 0, but received num is %d",
+                                  num));
 
     out->Resize(framework::make_ddim({num}));
     T* out_data = out->mutable_data<T>(context.GetPlace());

paddle/fluid/operators/linspace_op.h

@@ -46,7 +46,10 @@ class CPULinspaceKernel : public framework::OpKernel<T> {
 
     T start = start_t.data<T>()[0];
     T stop = stop_t.data<T>()[0];
-    PADDLE_ENFORCE(num > 0, "The num of linspace op should be larger than 0.");
+    PADDLE_ENFORCE_GT(num, 0, platform::errors::InvalidArgument(
+                                  "The num of linspace op should be larger "
+                                  "than 0, but received num is %d",
+                                  num));
 
     out->Resize(framework::make_ddim({num}));
 

paddle/fluid/operators/mkldnn/quantize_mkldnn_op.cc

@@ -48,6 +48,7 @@ class QuantOpKernel : public framework::OpKernel<T> {
     const T* input_data = input->data<T>();
 
     bool is_negative = ctx.Attr<bool>("is_negative_input");
+    bool bfloat16 = ctx.Attr<bool>("bfloat16");
     std::string key =
         platform::CreateKey(platform::ThreadIDasStr(), src_tz, scale_data,
                             is_negative, ctx.OutputName("Output"));
@@ -74,7 +75,10 @@ class QuantOpKernel : public framework::OpKernel<T> {
           src_md, engine, to_void_cast<T>(input_data));
 
       std::shared_ptr<mkldnn::memory::desc> dst_md;
-      if (is_negative) {
+      if (bfloat16) {
+        platform::SetDstMemoryQuantized<paddle::platform::bfloat16>(
+            ctx, output, dst_tz, engine, dst_md, dst_memory, out_format);
+      } else if (is_negative) {
         platform::SetDstMemoryQuantized<int8_t>(ctx, output, dst_tz, engine,
                                                 dst_md, dst_memory, out_format);
       } else {
@@ -96,7 +100,11 @@ class QuantOpKernel : public framework::OpKernel<T> {
       dst_memory = std::static_pointer_cast<mkldnn::memory>(
           dev_ctx.GetBlob(key_dst_mem));
       auto place = ctx.GetPlace();
-      if (is_negative) {
+
+      if (bfloat16) {
+        dst_memory->set_data_handle(
+            output->mutable_data<paddle::platform::bfloat16>(place));
+      } else if (is_negative) {
         dst_memory->set_data_handle(output->mutable_data<int8_t>(place));
       } else {
         dst_memory->set_data_handle(output->mutable_data<uint8_t>(place));

paddle/fluid/operators/quantize_op.cc

@@ -40,6 +40,8 @@ void QuantOpMaker::Make() {
   AddAttr<std::string>("output_format",
                        "Convert format to NHWC or NCHW during quantization.")
       .SetDefault("NHWC");
+  AddAttr<bool>("bfloat16", "(bool, default false) Convert to bfloat16")
+      .SetDefault(false);
   AddComment(R"DOC(This op will quantize data from FP32 to INT8)DOC");
 }
 

paddle/fluid/operators/scale_op.h

@@ -60,7 +60,10 @@ class ScaleKernel : public framework::OpKernel<T> {
     out->mutable_data<T>(in->place());
 
     PADDLE_ENFORCE_EQ(in->dims(), out->dims(),
-                      "in and out should have the same dim");
+                      paddle::platform::errors::InvalidArgument(
+                          "the input and output should have the same dim"
+                          "but input dim is %s, output dim is %s",
+                          in->dims(), out->dims()));
 
     auto eigen_out = framework::EigenVector<T>::Flatten(*out);
     auto eigen_in = framework::EigenVector<T>::Flatten(*in);

paddle/fluid/operators/sum_op.cc

@@ -186,10 +186,17 @@ class SumOp : public framework::OperatorWithKernel {
           }
         }
       }
-      PADDLE_THROW("Cannot find the input data type by all input data");
+      PADDLE_THROW(platform::errors::InvalidArgument(
+          "Expected each tensor in Input(x) in sum op has be initialized, but "
+          "some tensor in Input(x) is not be initialized, please check your "
+          "code.",
+          framework::ToTypeName(x_vars[0]->Type())));
     }
-    PADDLE_THROW("Unexpected branch. Input type is %s",
-                 framework::ToTypeName(x_vars[0]->Type()));
+    PADDLE_THROW(platform::errors::InvalidArgument(
+        "Expected type of Input(X) must be Tensor,  SelectedRows or "
+        "LodTensorArray. But got "
+        "unsupport type: %s.",
+        framework::ToTypeName(x_vars[0]->Type())));
   }
 };
 

paddle/fluid/operators/sum_op.cu

@@ -169,8 +169,18 @@ void SumToLoDTensor(const framework::ExecutionContext &context) {
       auto row_numel = sr_value.numel() / sr_rows.size();
       auto out_dims = out->dims();
 
-      PADDLE_ENFORCE_EQ(sr.height(), out_dims[0]);
-      PADDLE_ENFORCE_EQ(row_numel, out->numel() / sr.height());
+      PADDLE_ENFORCE_EQ(sr.height(), out_dims[0],
+                        platform::errors::InvalidArgument(
+                            "The table height of input must be same as output, "
+                            "but received input height is %d"
+                            ", output height is %d",
+                            sr.height(), out_dims[0]));
+      PADDLE_ENFORCE_EQ(row_numel, out->numel() / sr.height(),
+                        platform::errors::InvalidArgument(
+                            "The table width of input must be same as output, "
+                            "but received input width is %d"
+                            ", output width is %d",
+                            row_numel, out->numel() / sr.height()));
 
       auto *sr_data = sr_value.data<T>();
       auto *sr_out_data = out->data<T>();
@@ -231,8 +241,11 @@ class SumKernel<platform::CUDADeviceContext, T>
     } else if (out_var->IsType<framework::LoDTensorArray>()) {
       LodTensorArrayCompute<platform::CUDADeviceContext, T>(context);
     } else {
-      PADDLE_THROW("Unexpected branch, output variable type is %s",
-                   framework::ToTypeName(out_var->Type()));
+      PADDLE_THROW(platform::errors::InvalidArgument(
+          "Expected type of Ouput(out) must be Tensor,  SelectedRows or "
+          "LodTensorArray. But got "
+          "unsupport type: %s.",
+          framework::ToTypeName(out_var->Type())));
     }
   }
 };

paddle/fluid/operators/sum_op.h

@@ -182,7 +182,11 @@ class SumKernel : public framework::OpKernel<T> {
           auto &in_t = in_vars[i]->Get<framework::SelectedRows>();
           functor(context.template device_context<DeviceContext>(), in_t, out);
         } else {
-          PADDLE_THROW("Variable type must be LoDTensor/SelectedRows.");
+          PADDLE_THROW(platform::errors::InvalidArgument(
+              "Expected type of Input(X) of %d-th must be Tensor, "
+              "SelectedRows. But got "
+              "unsupport type: %s.",
+              framework::ToTypeName(in_vars[i]->Type())));
         }
       }
     } else if (out_var->IsType<framework::SelectedRows>()) {
@@ -190,8 +194,11 @@ class SumKernel : public framework::OpKernel<T> {
     } else if (out_var->IsType<framework::LoDTensorArray>()) {
       LodTensorArrayCompute<DeviceContext, T>(context);
     } else {
-      PADDLE_THROW("Unexpected branch, output variable type is %s",
-                   framework::ToTypeName(out_var->Type()));
+      PADDLE_THROW(platform::errors::InvalidArgument(
+          "Expected type of Output(out) must be Tensor, SelectedRows, "
+          "LoDTensorArray. But got "
+          "unsupport type: %s.",
+          framework::ToTypeName(out_var->Type())));
     }
   }
 };

paddle/fluid/operators/uniform_random_op.cc

@@ -54,9 +54,11 @@ class CPUUniformRandomKernel : public framework::OpKernel<T> {
       tensor = out_var->GetMutable<framework::LoDTensor>();
       if (!new_shape.empty()) tensor->Resize(framework::make_ddim(new_shape));
     } else {
-      PADDLE_THROW(
-          "uniform_random_op's output only"
-          "supports SelectedRows and LoDTensor");
+      PADDLE_THROW(platform::errors::InvalidArgument(
+          "Expected type of Output(out) in uniform_random_op must be Tensor, "
+          "SelectedRows. But got "
+          "unsupport type: %s.",
+          framework::ToTypeName(out_var->Type())));
     }
     T *data = tensor->mutable_data<T>(ctx.GetPlace());
 

paddle/fluid/operators/uniform_random_op.cu

@@ -116,9 +116,11 @@ class GPUUniformRandomKernel : public framework::OpKernel<T> {
       tensor = out_var->GetMutable<framework::LoDTensor>();
       if (!new_shape.empty()) tensor->Resize(framework::make_ddim(new_shape));
     } else {
-      PADDLE_THROW(
-          "uniform_random_op's output only"
-          "supports SelectedRows and LoDTensor");
+      PADDLE_THROW(platform::errors::InvalidArgument(
+          "Expected type of Output(out) in uniform_random_op must be Tensor, "
+          "SelectedRows. But got "
+          "unsupport type: %s.",
+          framework::ToTypeName(out_var->Type())));
     }
     T* data = tensor->mutable_data<T>(context.GetPlace());
     unsigned int seed = static_cast<unsigned int>(context.Attr<int>("seed"));

paddle/fluid/operators/uniform_random_op.h

@@ -50,7 +50,10 @@ inline std::vector<int64_t> GetNewDataFromShapeTensor(
     }
     return vec_new_data;
   } else {
-    PADDLE_THROW("The dtype of shape tensor must be int32 or int64.");
+    PADDLE_THROW(platform::errors::InvalidArgument(
+        "Expected dtype of ShapeTensor must be int32, int64. But got "
+        "unsupport dtype: %s.",
+        paddle::framework::DataTypeToString(new_data_tensor->type())));
   }
 }
 
@@ -84,7 +87,11 @@ inline std::vector<int64_t> GetNewDataFromShapeTensorList(
         vec_new_shape.push_back(*tensor->data<int64_t>());
       }
     } else {
-      PADDLE_THROW("The dtype of shape tensor must be int32 or int64.");
+      PADDLE_THROW(platform::errors::InvalidArgument(
+          "Expected dtype of ShapeTensorList of %d-th must be int32, int64. "
+          "But got "
+          "unsupport dtype: %s.",
+          i, paddle::framework::DataTypeToString(tensor->type())));
     }
   }
 

paddle/fluid/platform/cudnn_helper.h

@@ -287,6 +287,8 @@ class ScopedTensorDescriptor {
     return descriptor(CudnnDataType<T>::type, dim, stride);
   }
 
+  inline cudnnTensorDescriptor_t desc() { return desc_; }
+
  private:
   cudnnTensorDescriptor_t desc_;
   DISABLE_COPY_AND_ASSIGN(ScopedTensorDescriptor);
@@ -329,6 +331,8 @@ class ScopedRNNTensorDescriptor {
                       input_size, time_major, seq_length);
   }
 
+  inline cudnnRNNDataDescriptor_t desc() { return desc_; }
+
  private:
   cudnnRNNDataDescriptor_t desc_;
   DISABLE_COPY_AND_ASSIGN(ScopedRNNTensorDescriptor);
@@ -361,6 +365,7 @@ class ScopedDropoutDescriptor {
     }
     return desc_;
   }
+  inline cudnnDropoutDescriptor_t desc() { return desc_; }
 
  private:
   cudnnDropoutDescriptor_t desc_;
@@ -376,7 +381,7 @@ class ScopedRNNDescriptor {
     PADDLE_ENFORCE_CUDA_SUCCESS(dynload::cudnnDestroyRNNDescriptor(desc_));
   }
 
-  inline cudnnRNNDescriptor_t descriptor() { return desc_; }
+  inline cudnnRNNDescriptor_t desc() { return desc_; }
 
  private:
   cudnnRNNDescriptor_t desc_;
@@ -419,172 +424,13 @@ class ScopedFilterDescriptor {
                       kernel, groups);
   }
 
+  inline cudnnFilterDescriptor_t desc() { return desc_; }
+
  private:
   cudnnFilterDescriptor_t desc_;
   DISABLE_COPY_AND_ASSIGN(ScopedFilterDescriptor);
 };
 
-class ScopedRNNBase {
- public:
-  ScopedRNNBase(int seq_length, int batch_size, int input_size, int hidden_size,
-                int num_layers, float dropout_prob, int seed, int weight_numel,
-                bool initialized, bool is_bidirec)
-      : seq_length_(seq_length),
-        batch_size_(batch_size),
-        input_size_(input_size),
-        hidden_size_(hidden_size),
-        num_layers_(num_layers),
-        dropout_prob_(dropout_prob),
-        seed_(seed),
-        weight_numel_(weight_numel),
-        initialized_(initialized),
-        is_bidirec_(is_bidirec) {}
-
-  template <typename T>
-  void Create(const cudnnHandle_t& handle, const platform::Place& place,
-              std::vector<int> sequence_length, size_t* workspace_size,
-              size_t* reserve_size, framework::Tensor* dropout_state) {
-    int numDirections = is_bidirec_ ? 2 : 1;
-    cudnnDataType_t cudnn_type = platform::CudnnDataType<T>::type;
-
-    // ------------------- cudnn x, y descriptors ---------------------
-    std::vector<int> dims_x = {batch_size_, input_size_, 1};
-    std::vector<int> strides_x = {input_size_, 1, 1};
-
-    std::vector<int> dims_y = {batch_size_, hidden_size_ * numDirections, 1};
-    std::vector<int> strides_y = {hidden_size_ * numDirections, 1, 1};
-
-    for (int i = 0; i < seq_length_; ++i) {
-      x_desc_.emplace_back(x_d.descriptor<T>(dims_x, strides_x));
-      y_desc_.emplace_back(y_d.descriptor<T>(dims_y, strides_y));
-    }
-
-    if (!sequence_length.empty()) {
-      x_seq_desc_ = x_seq_d.descriptor<T>(seq_length_, batch_size_, input_size_,
-                                          true, sequence_length);
-      y_seq_desc_ = y_seq_d.descriptor<T>(seq_length_, batch_size_,
-                                          hidden_size_ * numDirections, true,
-                                          sequence_length);
-    }
-
-    // ------------------- cudnn hx, hy, cx, cy descriptors----------
-    std::vector<int> dims_hx = {num_layers_ * numDirections, batch_size_,
-                                hidden_size_};
-    std::vector<int> strides_hx = {hidden_size_ * batch_size_, hidden_size_, 1};
-
-    hx_desc_ = hx_d.descriptor<T>(dims_hx, strides_hx);
-    cx_desc_ = cx_d.descriptor<T>(dims_hx, strides_hx);
-    hy_desc_ = hy_d.descriptor<T>(dims_hx, strides_hx);
-    cy_desc_ = cy_d.descriptor<T>(dims_hx, strides_hx);
-
-    // ------------------- cudnn dropout descriptors ---------------------
-    size_t state_size;
-    if (!initialized_) {
-      PADDLE_ENFORCE_CUDA_SUCCESS(
-          dynload::cudnnDropoutGetStatesSize(handle, &state_size));
-      dropout_state->mutable_data<uint8_t>({static_cast<int64_t>(state_size)},
-                                           place);
-    }
-    dropout_desc_ =
-        dropout_d.descriptor(handle, place, initialized_, dropout_prob_,
-                             dropout_state, seed_, state_size);
-
-    // ------------------- cudnn rnn descriptors ---------------------
-    rnn_desc_ = rnn_d.descriptor();
-
-#if CUDNN_VERSION >= 6000
-    PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetRNNDescriptor_v6(
-        handle, rnn_desc_, hidden_size_, num_layers_, dropout_desc_,
-        CUDNN_LINEAR_INPUT,
-        is_bidirec_ ? CUDNN_BIDIRECTIONAL : CUDNN_UNIDIRECTIONAL, CUDNN_LSTM,
-        CUDNN_RNN_ALGO_STANDARD, cudnn_type));
-#else
-    PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetRNNDescriptor(
-        rnn_desc_, hidden_size_, num_layers_, dropout_desc_, CUDNN_LINEAR_INPUT,
-        is_bidirec_ ? CUDNN_BIDIRECTIONAL : CUDNN_UNIDIRECTIONAL, CUDNN_LSTM,
-        cudnn_type));
-#endif
-    if (!sequence_length.empty()) {
-      PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetRNNPaddingMode(
-          rnn_desc_, CUDNN_RNN_PADDED_IO_ENABLED));
-    }
-    // ------------------- cudnn weights_size ---------------------
-    size_t weights_size_;
-    PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnGetRNNParamsSize(
-        handle, rnn_desc_, x_desc_[0], &weights_size_, cudnn_type));
-
-    PADDLE_ENFORCE_EQ(
-        weights_size_, sizeof(T) * weight_numel_,
-        platform::errors::InvalidArgument(
-            "The cudnn lstm and setting weight size should be same."));
-
-    // ------------------- cudnn weight descriptors ---------------------
-    platform::DataLayout layout = platform::DataLayout::kNCHW;
-    int dim_tmp = weights_size_ / sizeof(T);
-    std::vector<int> dim_w = {dim_tmp, 1, 1};
-    w_desc_ = w_d.descriptor<T>(layout, dim_w);
-
-    // ------------------- cudnn workspace, reserve size ---------------------
-    PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnGetRNNWorkspaceSize(
-        handle, rnn_desc_, seq_length_, x_desc_.data(), workspace_size));
-    PADDLE_ENFORCE_CUDA_SUCCESS(
-        platform::dynload::cudnnGetRNNTrainingReserveSize(
-            handle, rnn_desc_, seq_length_, x_desc_.data(), reserve_size));
-  }
-
-  cudnnTensorDescriptor_t* x_desc() { return x_desc_.data(); }
-  cudnnTensorDescriptor_t* y_desc() { return y_desc_.data(); }
-  cudnnRNNDataDescriptor_t x_seq_desc() { return x_seq_desc_; }
-  cudnnRNNDataDescriptor_t y_seq_desc() { return y_seq_desc_; }
-  cudnnTensorDescriptor_t hx_desc() { return hx_desc_; }
-  cudnnTensorDescriptor_t cx_desc() { return cx_desc_; }
-  cudnnTensorDescriptor_t hy_desc() { return hy_desc_; }
-  cudnnTensorDescriptor_t cy_desc() { return cy_desc_; }
-  cudnnRNNDescriptor_t rnn_desc() { return rnn_desc_; }
-  cudnnDropoutDescriptor_t dropout_desc() { return dropout_desc_; }
-  cudnnFilterDescriptor_t w_desc() { return w_desc_; }
-
- private:
-  int seq_length_;
-  int batch_size_;
-  int input_size_;
-  int hidden_size_;
-  int num_layers_;
-  float dropout_prob_;
-  int seed_;
-  int weight_numel_;
-  bool initialized_;
-  bool is_bidirec_;
-
-  std::vector<cudnnTensorDescriptor_t> x_desc_;
-  std::vector<cudnnTensorDescriptor_t> y_desc_;
-  cudnnRNNDataDescriptor_t x_seq_desc_;
-  cudnnRNNDataDescriptor_t y_seq_desc_;
-  // A tensor descriptor describing the initial hidden state of the RNN.
-  cudnnTensorDescriptor_t hx_desc_;
-  // A tensor descriptor describing the initial cell state for LSTM networks.
-  cudnnTensorDescriptor_t cx_desc_;
-  // A tensor descriptor describing the final hidden state of the RNN.
-  cudnnTensorDescriptor_t hy_desc_;
-  // A tensor descriptor describing the final cell state for LSTM networks.
-  cudnnTensorDescriptor_t cy_desc_;
-  cudnnDropoutDescriptor_t dropout_desc_;
-  cudnnFilterDescriptor_t w_desc_;
-  cudnnRNNDescriptor_t rnn_desc_;
-
-  ScopedTensorDescriptor x_d;
-  ScopedTensorDescriptor y_d;
-  ScopedRNNTensorDescriptor x_seq_d;
-  ScopedRNNTensorDescriptor y_seq_d;
-  ScopedTensorDescriptor hx_d;
-  ScopedTensorDescriptor cx_d;
-  ScopedTensorDescriptor hy_d;
-  ScopedTensorDescriptor cy_d;
-  ScopedDropoutDescriptor dropout_d;
-  ScopedFilterDescriptor w_d;
-  ScopedRNNDescriptor rnn_d;
-};
-
 class ScopedConvolutionDescriptor {
  public:
   ScopedConvolutionDescriptor() {

paddle/fluid/platform/mkldnn_helper.h

@@ -443,6 +443,13 @@ inline bool HasOpINT8DataType(const paddle::framework::OpDesc* op) {
           op->GetAttrIfExists<bool>("use_quantizer"));
 }
 
+inline bool HasOpBFLOAT16DataType(const paddle::framework::OpDesc* op) {
+  return op->GetAttrIfExists<std::string>("mkldnn_data_type") == "bfloat16";
+}
+
+inline bool HasOpFLOAT32DataType(const paddle::framework::OpDesc* op) {
+  return op->GetAttrIfExists<std::string>("mkldnn_data_type") == "float32";
+}
 enum class RNNReorderType { PP_NTC, PP_TNC, NTC_PP, TNC_PP };
 
 }  // namespace platform

paddle/fluid/pybind/CMakeLists.txt

@@ -38,6 +38,7 @@ set(PYBIND_SRCS
   imperative.cc
   ir.cc
   inference_api.cc
+  compatible.cc
   generator_py.cc)
 
 if(WITH_GLOO)

paddle/fluid/pybind/compatible.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/pybind/compatible.h"
+
+#include <memory>
+#include <string>
+
+#include "paddle/fluid/framework/op_version_registry.h"
+
+namespace py = pybind11;
+
+using paddle::framework::compatible::PassVersionCheckerRegistrar;
+
+namespace paddle {
+namespace pybind {
+
+void BindCompatible(py::module* m) {
+  py::class_<PassVersionCheckerRegistrar>(*m, "PassVersionChecker")
+      .def_static("IsCompatible", [](const std::string& name) -> bool {
+        auto instance = PassVersionCheckerRegistrar::GetInstance();
+        return instance.IsPassCompatible(name);
+      });
+}
+
+}  // namespace pybind
+}  // namespace paddle

paddle/fluid/pybind/compatible.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 <pybind11/pybind11.h>
+
+namespace paddle {
+namespace pybind {
+void BindCompatible(pybind11::module *m);
+}  // namespace pybind
+}  // namespace paddle

paddle/fluid/pybind/protobuf.cc

@@ -184,6 +184,7 @@ void BindVarDsec(pybind11::module *m) {
       .value("FP16", pd::proto::VarType::FP16)
       .value("FP32", pd::proto::VarType::FP32)
       .value("FP64", pd::proto::VarType::FP64)
+      .value("BF16", pd::proto::VarType::BF16)
       .value("LOD_TENSOR", pd::proto::VarType::LOD_TENSOR)
       .value("SELECTED_ROWS", pd::proto::VarType::SELECTED_ROWS)
       .value("FEED_MINIBATCH", pd::proto::VarType::FEED_MINIBATCH)

paddle/fluid/pybind/pybind.cc

@@ -60,6 +60,7 @@ limitations under the License. */
 #include "paddle/fluid/platform/place.h"
 #include "paddle/fluid/platform/profiler.h"
 #include "paddle/fluid/pybind/box_helper_py.h"
+#include "paddle/fluid/pybind/compatible.h"
 #include "paddle/fluid/pybind/const_value.h"
 #include "paddle/fluid/pybind/data_set_py.h"
 #include "paddle/fluid/pybind/exception.h"
@@ -2619,6 +2620,7 @@ All parameter, weight, gradient are variables in Paddle.
   BindGraph(&m);
   BindNode(&m);
   BindInferenceApi(&m);
+  BindCompatible(&m);
   BindDataset(&m);
   BindGenerator(&m);
 #ifdef PADDLE_WITH_CRYPTO

paddle/scripts/paddle_build.bat

@@ -51,6 +51,17 @@ if %ERRORLEVEL% NEQ 0 (
     exit /b 7
 )
 
+rem ------pre install clcache and init config----------
+pip install clcache
+:: set USE_CLCACHE to enable clcache
+set USE_CLCACHE=1
+:: In some scenarios, CLCACHE_HARDLINK can save one file copy.
+set CLCACHE_HARDLINK=1
+:: If it takes more than 1000s to obtain the right to use the cache, an error will be reported
+set CLCACHE_OBJECT_CACHE_TIMEOUT_MS=1000000
+:: set maximum cache size to 20G
+clcache.exe -M 21474836480
+
 rem ------initialize common variable------
 if not defined CUDA_TOOLKIT_ROOT_DIR set CUDA_TOOLKIT_ROOT_DIR="C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.0"
 if not defined BRANCH set BRANCH=develop
@@ -173,7 +184,7 @@ echo Build third_party successfully!
 set build_times=1
 :build_paddle
 echo Build Paddle the %build_times% time:
-msbuild /m:%PARALLEL_PROJECT_COUNT% /p:Configuration=Release /verbosity:minimal paddle.sln
+msbuild /m:%PARALLEL_PROJECT_COUNT% /p:TrackFileAccess=false /p:CLToolExe=clcache.exe /p:CLToolPath=%PYTHON_ROOT%\Scripts /p:Configuration=Release /verbosity:minimal paddle.sln
 if %ERRORLEVEL% NEQ 0 (
     set /a build_times=%build_times%+1
     if %build_times% GTR 2 (

python/paddle/__init__.py

@@ -49,6 +49,7 @@ import paddle.optimizer
 import paddle.metric
 import paddle.device
 import paddle.incubate.complex as complex
+import paddle.regularizer
 
 # TODO: define alias in tensor and framework directory
 

python/paddle/distributed/__init__.py

@@ -21,6 +21,7 @@ from .parallel import get_rank
 from .parallel import get_world_size
 from paddle.fluid.dygraph.parallel import prepare_context  #DEFINE_ALIAS
 from paddle.fluid.dygraph.parallel import ParallelEnv  #DEFINE_ALIAS
+from paddle.distributed.fleet.dataset import *
 
 from . import collective
 from .collective import *
@@ -30,11 +31,8 @@ __all__ = ["spawn"]
 
 # dygraph parallel apis
 __all__ += [
-    "init_parallel_env",
-    "get_rank",
-    "get_world_size",
-    "prepare_context",
-    "ParallelEnv",
+    "init_parallel_env", "get_rank", "get_world_size", "prepare_context",
+    "ParallelEnv", "InMemoryDataset", "QueueDataset"
 ]
 
 # collective apis

python/paddle/distributed/cloud_utils.py

@@ -19,7 +19,7 @@ from paddle.distributed.utils import get_cluster, logger
 
 def get_cloud_cluster(args_node_ips, args_node_ip, args_port, selected_gpus):
     """
-    args_node_ips, args_node_ip:string
+    args_node_ips:string, args_node_ip:string, args_port: int, selected_gpus:list
     """
     #you can automatically get ip info while using paddlecloud multi nodes mode.
     node_ips = os.getenv("PADDLE_TRAINERS")
@@ -31,6 +31,9 @@ def get_cloud_cluster(args_node_ips, args_node_ip, args_port, selected_gpus):
     node_rank = os.getenv("PADDLE_TRAINER_ID")
     assert node_rank is not None, "PADDLE_TRAINER_ID should not be None"
 
+    paddle_ports_num = int(os.getenv("TRAINER_PORTS_NUM"))
+    assert paddle_ports_num is not None, "TRAINER_PORTS_NUM should not be None"
+
     node_ips = node_ips.split(",")
     num_nodes = len(node_ips)
     node_rank = int(node_rank)
@@ -47,14 +50,16 @@ automatically got from PADDLE_TRAINERS(multi nodes) or POD_IP(single node).\
 Your input cluster_node_ips: {} doesn't equals to IPs: {} from \
 paddlecloud environment.".format(args_node_ips, node_ips))
 
+    # DISTRIBUTED_TRAINER_ENDPOINTS: new environment since paddlecloud 1.8.4
+    # e.g: DISTRIBUTED_TRAINER_ENDPOINTS="ip1:port1,ip1:port2,ip1:port3,ip1:port4,ip2:port5,ip2:port6,ip2:port7,ip2:port8"
+    trainer_endpoints = os.getenv("DISTRIBUTED_TRAINER_ENDPOINTS")
+    if trainer_endpoints is None:
         started_port = args_port
-    print("num_nodes:", num_nodes)
         if num_nodes > 1:
             try:
                 paddle_port = int(os.getenv("PADDLE_PORT", ""))
-            paddle_port_num = int(os.getenv("TRAINER_PORTS_NUM", ""))
 
-            if paddle_port_num >= len(
+                if paddle_ports_num >= len(
                         selected_gpus) and paddle_port != args_port:
                     logger.warning("Use Cloud specified port:{}.".format(
                         paddle_port))
@@ -66,13 +71,26 @@ paddlecloud environment.".format(args_node_ips, node_ips))
 
         if started_port is None:
             started_port = 6170
-
-    logger.debug("parsed from args:node_ips:{} \
-        node_ip:{} node_rank:{} started_port:{}"
-                 .format(node_ips, node_ip, node_rank, started_port))
-
-    ports = [x for x in range(started_port, started_port + len(selected_gpus))]
-    cluster, pod = get_cluster(node_ips, node_ip, ports, selected_gpus)
+        ports = [
+            x for x in range(started_port, started_port + len(selected_gpus))
+        ]
+        trainer_endpoints = []
+        for ip in node_ips:
+            trainer_endpoints.append(["%s:%d" % (ip, port) for port in ports])
+    else:
+        trainer_endpoints_ori = trainer_endpoints.split(",")
+        trainer_endpoints = []
+        assert num_nodes * paddle_ports_num == len(trainer_endpoints_ori)
+        for i in range(num_nodes):
+            trainer_endpoints.append(trainer_endpoints_ori[
+                i * paddle_ports_num:(i + 1) * paddle_ports_num])
+
+    logger.debug("parsed from args: node_ips:{} \
+        node_ip:{} node_rank:{} trainer_endpoints:{}"
+                 .format(node_ips, node_ip, node_rank, trainer_endpoints))
+
+    cluster, pod = get_cluster(node_ips, node_ip, trainer_endpoints,
+                               selected_gpus)
     return cluster, cluster.pods[node_rank]
 
 

python/paddle/distributed/fleet/__init__.py

@@ -23,7 +23,6 @@ from .dataset import *
 __all__ = [
     "DistributedStrategy",
     "UtilBase",
-    "DatasetFactory",
     "UserDefinedRoleMaker",
     "PaddleCloudRoleMaker",
     "Fleet",

python/paddle/distributed/fleet/base/strategy_compiler.py

@@ -60,7 +60,7 @@ class StrategyCompiler(StrategyCompilerBase):
 
     def _get_valid_strategy(self, dist_strategy, can_not_apply_optimizer_list):
         import copy
-        valid_strategy = copy.copy(dist_strategy)
+        valid_strategy = copy.deepcopy(dist_strategy)
         invalid_optimizers = []
         for candidate in self._meta_optimizer_candidates:
             is_valid = False

python/paddle/distributed/fleet/cloud_utils.py

@@ -19,7 +19,7 @@ from paddle.distributed.fleet.launch_utils import get_cluster, logger
 
 def get_cloud_cluster(args_node_ips, selected_gpus, args_port=6170):
     """
-    args_node_ips, args_node_ip:string
+    args_node_ips:string, selected_gpus:list, args_port: int
     """
     #you can automatically get ip info while using paddlecloud multi nodes mode.
     node_ips = os.getenv("PADDLE_TRAINERS")
@@ -31,6 +31,9 @@ def get_cloud_cluster(args_node_ips, selected_gpus, args_port=6170):
     node_rank = os.getenv("PADDLE_TRAINER_ID")
     assert node_rank is not None, "PADDLE_TRAINER_ID should not be None"
 
+    paddle_ports_num = int(os.getenv("TRAINER_PORTS_NUM"))
+    assert paddle_ports_num is not None, "TRAINER_PORTS_NUM should not be None"
+
     node_ips = node_ips.split(",")
     num_nodes = len(node_ips)
     node_rank = int(node_rank)
@@ -42,14 +45,16 @@ automatically got from PADDLE_TRAINERS(multi nodes) or POD_IP(single node).\
 Your input cluster_node_ips: {} doesn't equals to IPs: {} from \
 paddlecloud environment.".format(args_node_ips, node_ips))
 
+    # DISTRIBUTED_TRAINER_ENDPOINTS: new environment since paddlecloud 1.8.4
+    # e.g: DISTRIBUTED_TRAINER_ENDPOINTS="ip1:port1,ip1:port2,ip1:port3,ip1:port4,ip2:port5,ip2:port6,ip2:port7,ip2:port8"
+    trainer_endpoints = os.getenv("DISTRIBUTED_TRAINER_ENDPOINTS")
+    if trainer_endpoints is None:
         started_port = args_port
-    print("num_nodes:", num_nodes)
         if num_nodes > 1:
             try:
                 paddle_port = int(os.getenv("PADDLE_PORT", ""))
-            paddle_port_num = int(os.getenv("TRAINER_PORTS_NUM", ""))
 
-            if paddle_port_num >= len(
+                if paddle_ports_num >= len(
                         selected_gpus) and paddle_port != args_port:
                     logger.warning("Use Cloud specified port:{}.".format(
                         paddle_port))
@@ -61,13 +66,26 @@ paddlecloud environment.".format(args_node_ips, node_ips))
 
         if started_port is None:
             started_port = 6170
-
-    logger.debug("parsed from args:node_ips:{} \
-        node_ip:{} node_rank:{} started_port:{}"
-                 .format(node_ips, node_ip, node_rank, started_port))
-
-    ports = [x for x in range(started_port, started_port + len(selected_gpus))]
-    cluster, pod = get_cluster(node_ips, node_ip, ports, selected_gpus)
+        ports = [
+            x for x in range(started_port, started_port + len(selected_gpus))
+        ]
+        trainer_endpoints = []
+        for ip in node_ips:
+            trainer_endpoints.append(["%s:%d" % (ip, port) for port in ports])
+    else:
+        trainer_endpoints_ori = trainer_endpoints.split(",")
+        trainer_endpoints = []
+        assert num_nodes * paddle_ports_num == len(trainer_endpoints_ori)
+        for i in range(num_nodes):
+            trainer_endpoints.append(trainer_endpoints_ori[
+                i * paddle_ports_num:(i + 1) * paddle_ports_num])
+
+    logger.debug("parsed from args: node_ips:{} \
+        node_ip:{} node_rank:{} trainer_endpoints:{}"
+                 .format(node_ips, node_ip, node_rank, trainer_endpoints))
+
+    cluster, pod = get_cluster(node_ips, node_ip, trainer_endpoints,
+                               selected_gpus)
     return cluster, cluster.pods[node_rank]
 
 
@@ -75,7 +93,8 @@ def use_paddlecloud():
     node_ips = os.getenv("PADDLE_TRAINERS")
     node_ip = os.getenv("POD_IP")
     node_rank = os.getenv("PADDLE_TRAINER_ID")
-    if node_ips is None or node_ip is None or node_rank is None:
+    paddle_ports_num = os.getenv("TRAINER_PORTS_NUM")
+    if node_ips is None or node_ip is None or node_rank is None or paddle_ports_num is None:
         return False
     else:
         return True

python/paddle/distributed/fleet/dataset/dataset.py

@@ -14,54 +14,11 @@
 """This is definition of dataset class, which is high performance IO."""
 
 import paddle
-import paddle.fluid as fluid
 from paddle.fluid.proto import data_feed_pb2
 from google.protobuf import text_format
 import paddle.fluid.core as core
 
 
-class DatasetFactory(object):
-    """
-    DatasetFactory is a factory which create dataset by its name,
-    you can create "QueueDataset" or "InMemoryDataset", or "FileInstantDataset",
-    the default is "QueueDataset".
-
-    Example:
-        .. code-block:: python
-
-          import paddle.fluid as fluid
-          dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
-
-    """
-
-    def __init__(self):
-        """ Init. """
-        pass
-
-    def create_dataset(self, datafeed_class="QueueDataset"):
-        """
-        Create "QueueDataset" or "InMemoryDataset", or "FileInstantDataset",
-        the default is "QueueDataset".
-
-        Args:
-            datafeed_class(str): datafeed class name, QueueDataset or InMemoryDataset.
-                                 Default is QueueDataset.
-
-        Examples:
-            .. code-block:: python
-
-              import paddle.fluid as fluid
-              dataset = fluid.DatasetFactory().create_dataset()
-
-        """
-        try:
-            dataset = globals()[datafeed_class]()
-            return dataset
-        except:
-            raise ValueError("datafeed class %s does not exist" %
-                             datafeed_class)
-
-
 class DatasetBase(object):
     """ Base dataset class. """
 
@@ -75,96 +32,67 @@ class DatasetBase(object):
         self.thread_num = 1
         self.filelist = []
 
-    def set_pipe_command(self, pipe_command):
+    def init(self,
+             batch_size=1,
+             thread_num=1,
+             use_var=[],
+             pipe_command="cat",
+             input_type=0,
+             fs_name="",
+             fs_ugi="",
+             download_cmd="cat"):
         """
-        Set pipe command of current dataset
-        A pipe command is a UNIX pipeline command that can be used only
-
-        Examples:
-            .. code-block:: python
-
-              import paddle.fluid as fluid
-              dataset = fluid.DatasetFactory().create_dataset()
-              dataset.set_pipe_command("python my_script.py")
+        should be called only once in user's python scripts to initialize setings of dataset instance. 
+        Normally, it is called by InMemoryDataset or QueueDataset.
 
         Args:
-            pipe_command(str): pipe command
+            batch_size(int): batch size. It will be effective during training. default is 1.
+            thread_num(int): thread num, it is the num of readers. default is 1.
+            use_var(list): list of variables. Variables which you will use. default is [].
+            pipe_command(str): pipe command of current dataset. A pipe command is a UNIX pipeline command that can be used only. default is "cat"
+            input_type(int): the input type of generated input. 0 is for one sample, 1 is for one batch. defalut is 0.
+            fs_name(str): fs name. default is "".
+            fs_ugi(str): fs ugi. default is "".
+            download_cmd(str): customized download command. default is "cat"
 
-        """
-        self.proto_desc.pipe_command = pipe_command
 
-    def set_rank_offset(self, rank_offset):
         """
-        Set rank_offset for merge_pv. It set the message of Pv.
-
-        Examples:
-            .. code-block:: python
-
-              import paddle.fluid as fluid
-              dataset = fluid.DatasetFactory().create_dataset()
-              dataset.set_rank_offset("rank_offset")
-
-        Args:
-            rank_offset(str): rank_offset's name
+        self._set_batch_size(batch_size)
+        self._set_thread(thread_num)
+        self._set_use_var(use_var)
+        self._set_pipe_command(pipe_command)
+        self._set_input_type(input_type)
+        self._set_hdfs_config(fs_name, fs_ugi)
+        self._set_download_cmd(download_cmd)
 
+    def _set_pipe_command(self, pipe_command):
         """
-        self.proto_desc.rank_offset = rank_offset
-
-    def set_fea_eval(self, record_candidate_size, fea_eval=True):
-        """
-        set fea eval mode for slots shuffle to debug the importance level of
-        slots(features), fea_eval need to be set True for slots shuffle.
-        
-        Args:
-            record_candidate_size(int): size of instances candidate to shuffle 
-                                        one slot
-            fea_eval(bool): whether enable fea eval mode to enable slots shuffle.
-                            default is True.
+        Set pipe command of current dataset
+        A pipe command is a UNIX pipeline command that can be used only
 
         Examples:
             .. code-block:: python
 
-            import paddle.fluid as fluid
-            dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
-            dataset.set_fea_eval(1000000, True)
-
-        """
-        if fea_eval:
-            self.dataset.set_fea_eval(fea_eval, record_candidate_size)
-        self.fea_eval = fea_eval
-
-    def slots_shuffle(self, slots):
-        """
-        Slots Shuffle 
-        Slots Shuffle is a shuffle method in slots level, which is usually used 
-        in sparse feature with large scale of instances. To compare the metric, i.e.
-        auc while doing slots shuffle on one or several slots with baseline to 
-        evaluate the importance level of slots(features).
+              import paddle
+              dataset = paddle.distributed.fleet.dataset.DatasetBase()
+              dataset._set_pipe_command("python my_script.py")
 
         Args:
-            slots(list[string]): the set of slots(string) to do slots shuffle.
+            pipe_command(str): pipe command
 
-        Examples:
-            import paddle.fluid as fluid
-            dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
-            dataset.set_merge_by_lineid()
-            #suppose there is a slot 0
-            dataset.slots_shuffle(['0'])
         """
-        if self.fea_eval:
-            slots_set = set(slots)
-            self.dataset.slots_shuffle(slots_set)
+        self.proto_desc.pipe_command = pipe_command
 
-    def set_batch_size(self, batch_size):
+    def _set_batch_size(self, batch_size):
         """
         Set batch size. Will be effective during training
 
         Examples:
             .. code-block:: python
 
-              import paddle.fluid as fluid
-              dataset = fluid.DatasetFactory().create_dataset()
-              dataset.set_batch_size(128)
+              import paddle
+              dataset = paddle.distributed.fleet.DatasetBase()
+              dataset._set_batch_size(128)
 
         Args:
             batch_size(int): batch size
@@ -172,32 +100,16 @@ class DatasetBase(object):
         """
         self.proto_desc.batch_size = batch_size
 
-    def set_pv_batch_size(self, pv_batch_size):
-        """
-        Set pv batch size. It will be effective during enable_pv_merge
-
-        Examples:
-            .. code-block:: python
-
-              import paddle.fluid as fluid
-              dataset = fluid.DatasetFactory().create_dataset()
-              dataset.set_pv_batch(128)
-        Args:
-            pv_batch_size(int): pv batch size
-
-        """
-        self.proto_desc.pv_batch_size = pv_batch_size
-
-    def set_thread(self, thread_num):
+    def _set_thread(self, thread_num):
         """
         Set thread num, it is the num of readers.
 
         Examples:
             .. code-block:: python
 
-              import paddle.fluid as fluid
-              dataset = fluid.DatasetFactory().create_dataset()
-               dataset.set_thread(12)
+              import paddle
+              dataset = paddle.distributed.fleet.DatasetBase()
+              dataset._set_thread(12)
 
         Args:
             thread_num(int): thread num
@@ -212,8 +124,8 @@ class DatasetBase(object):
         Examples:
             .. code-block:: python
 
-              import paddle.fluid as fluid
-              dataset = fluid.DatasetFactory().create_dataset()
+              import paddle
+              dataset = paddle.distributed.fleet.DatasetBase()
               dataset.set_filelist(['a.txt', 'b.txt'])
 
         Args:
@@ -222,19 +134,19 @@ class DatasetBase(object):
         self.dataset.set_filelist(filelist)
         self.filelist = filelist
 
-    def set_input_type(self, input_type):
+    def _set_input_type(self, input_type):
         self.proto_desc.input_type = input_type
 
-    def set_use_var(self, var_list):
+    def _set_use_var(self, var_list):
         """
         Set Variables which you will use.
 
         Examples:
             .. code-block:: python
 
-              import paddle.fluid as fluid
-              dataset = fluid.DatasetFactory().create_dataset()
-              dataset.set_use_var([data, label])
+              import paddle
+              dataset = paddle.distributed.fleet.DatasetBase()
+              dataset._set_use_var([data, label])
 
         Args:
             var_list(list): variable list
@@ -253,19 +165,19 @@ class DatasetBase(object):
                 slot_var.type = "uint64"
             else:
                 raise ValueError(
-                    "Currently, fluid.dataset only supports dtype=float32 and dtype=int64"
+                    "Currently, paddle.distributed.fleet.dataset only supports dtype=float32 and dtype=int64"
                 )
 
-    def set_hdfs_config(self, fs_name, fs_ugi):
+    def _set_hdfs_config(self, fs_name, fs_ugi):
         """
         Set hdfs config: fs name ad ugi
 
         Examples:
             .. code-block:: python
 
-              import paddle.fluid as fluid
-              dataset = fluid.DatasetFactory().create_dataset()
-              dataset.set_hdfs_config("my_fs_name", "my_fs_ugi")
+              import paddle
+              dataset = paddle.distributed.fleet.DatasetBase()
+              dataset._set_hdfs_config("my_fs_name", "my_fs_ugi")
 
         Args:
             fs_name(str): fs name
@@ -273,16 +185,16 @@ class DatasetBase(object):
         """
         self.dataset.set_hdfs_config(fs_name, fs_ugi)
 
-    def set_download_cmd(self, download_cmd):
+    def _set_download_cmd(self, download_cmd):
         """
         Set customized download cmd: download_cmd
 
         Examples:
             .. code-block:: python
 
-              import paddle.fluid as fluid
-              dataset = fluid.DatasetFactory().create_dataset()
-              dataset.set_download_cmd("./read_from_afs")
+              import paddle
+              dataset = paddle.distributed.fleet.DatasetBase()
+              dataset._set_download_cmd("./read_from_afs")
 
         Args:
             download_cmd(str): customized download command
@@ -297,22 +209,22 @@ class DatasetBase(object):
         if self.thread_num > len(self.filelist):
             self.thread_num = len(self.filelist)
         self.dataset.set_thread_num(self.thread_num)
-        self.dataset.set_data_feed_desc(self.desc())
+        self.dataset.set_data_feed_desc(self._desc())
         self.dataset.create_readers()
 
     def _finish_to_run(self):
         self.dataset.destroy_readers()
 
-    def desc(self):
+    def _desc(self):
         """
         Returns a protobuf message for this DataFeedDesc
 
         Examples:
             .. code-block:: python
 
-              import paddle.fluid as fluid
-              dataset = fluid.DatasetFactory().create_dataset()
-              print(dataset.desc())
+              import paddle
+              dataset = paddle.distributed.fleet.DatasetBase()
+              print(dataset._desc())
 
         Returns:
             A string message
@@ -330,10 +242,10 @@ class InMemoryDataset(DatasetBase):
     """
     InMemoryDataset, it will load data into memory
     and shuffle data before training.
-    This class should be created by DatasetFactory
 
     Example:
-        dataset = paddle.fluid.DatasetFactory().create_dataset("InMemoryDataset")
+        import paddle
+        dataset = paddle.distributed.InMemoryDataset()
     """
 
     def __init__(self):
@@ -351,7 +263,229 @@ class InMemoryDataset(DatasetBase):
         self.merge_by_lineid = False
         self.fleet_send_sleep_seconds = None
 
-    def set_feed_type(self, data_feed_type):
+    def _init_distributed_settings(self, **kwargs):
+        """
+        should be called only once in user's python scripts to initialize distributed-related setings of dataset instance
+        Args:
+            kwargs: Keyword arguments. Currently, we support following keys in **kwargs:
+
+            merge_size(int): ins size to merge, if merge_size > 0, set merge by line id, 
+                             instances of same line id will be merged after shuffle, 
+                             you should parse line id in data generator. default is -1.
+            parse_ins_id(bool): Set if Dataset need to parse ins_id. default is False.
+            parse_content(bool): Set if Dataset need to parse content. default is False.
+            fleet_send_batch_size(int): Set fleet send batch size in one rpc, default is 1024
+            fleet_send_sleep_seconds(int): Set fleet send sleep time, default is 0
+            fea_eval(bool): Set if Dataset need to do feature importance evaluation using slots shuffle.
+                            default is False.
+            candidate_size(int): if fea_eval is set True, set the candidate size used in slots shuffle.
+
+        Examples:
+            .. code-block:: python
+
+              import paddle
+              dataset = paddle.distributed.InMemoryDataset()
+              dataset.init(
+                    batch_size=1,
+                    thread_num=2,
+                    input_type=1,
+                    pipe_command="cat",
+                    use_var=[])
+              dataset._init_distributed_settings(
+                    parse_ins_id=True,
+                    parse_content=True,
+                    fea_eval=True,
+                    candidate_size=10000)
+              
+        """
+        merge_size = kwargs.get("merge_size", -1)
+        if merge_size > 0:
+            self._set_merge_by_lineid(merge_size)
+
+        parse_ins_id = kwargs.get("parse_ins_id", False)
+        self._set_parse_ins_id(parse_ins_id)
+
+        parse_content = kwargs.get("parse_content", False)
+        self._set_parse_content(parse_content)
+
+        fleet_send_batch_size = kwargs.get("fleet_send_batch_size", None)
+        if fleet_send_batch_size:
+            self._set_fleet_send_batch_size(fleet_send_batch_size)
+
+        fleet_send_sleep_seconds = kwargs.get("fleet_send_sleep_seconds", None)
+        if fleet_send_sleep_seconds:
+            self._set_fleet_send_sleep_seconds(fleet_send_sleep_seconds)
+
+        fea_eval = kwargs.get("fea_eval", False)
+        if fea_eval:
+            candidate_size = kwargs.get("candidate_size", 10000)
+            self._set_fea_eval(candidate_size, True)
+
+    def update_settings(self, **kwargs):
+        """
+        should be called in user's python scripts to update setings of dataset instance
+        Args:
+            kwargs: Keyword arguments. Currently, we support following keys in **kwargs,
+                    including single node settings and advanced distributed related settings:
+
+            batch_size(int): batch size. It will be effective during training. default is 1.
+            thread_num(int): thread num, it is the num of readers. default is 1.
+            use_var(list): list of variables. Variables which you will use. default is [].
+            input_type(int): the input type of generated input. 0 is for one sample, 1 is for one batch. defalut is 0.
+            fs_name(str): fs name. default is "".
+            fs_ugi(str): fs ugi. default is "".
+            pipe_command(str): pipe command of current dataset. A pipe command is a UNIX pipeline command that can be used only. default is "cat"
+            download_cmd(str): customized download command. default is "cat"
+            data_feed_type(str): data feed type used in c++ code. default is "MultiSlotInMemoryDataFeed".
+            queue_num(int): Dataset output queue num, training threads get data from queues. default is-1, which is set same as thread number in c++.
+
+            merge_size(int): ins size to merge, if merge_size > 0, set merge by line id, 
+                             instances of same line id will be merged after shuffle, 
+                             you should parse line id in data generator. default is -1.
+            parse_ins_id(bool): Set if Dataset need to parse ins_id. default is False.
+            parse_content(bool): Set if Dataset need to parse content. default is False.
+            fleet_send_batch_size(int): Set fleet send batch size in one rpc, default is 1024
+            fleet_send_sleep_seconds(int): Set fleet send sleep time, default is 0
+            fea_eval(bool): Set if Dataset need to do feature importance evaluation using slots shuffle.
+                            default is False.
+            candidate_size(int): if fea_eval is set True, set the candidate size used in slots shuffle.
+
+        Examples:
+            .. code-block:: python
+
+              import paddle
+              dataset = paddle.distributed.InMemoryDataset()
+              dataset.init(
+                    batch_size=1,
+                    thread_num=2,
+                    input_type=1,
+                    pipe_command="cat",
+                    use_var=[])
+              dataset._init_distributed_settings(
+                    parse_ins_id=True,
+                    parse_content=True,
+                    fea_eval=True,
+                    candidate_size=10000)
+              dataset.update_settings(batch_size=2)
+            
+        """
+        for key in kwargs:
+            if key == "pipe_command":
+                self._set_pipe_command(kwargs[key])
+            elif key == "batch_size":
+                self._set_batch_size(kwargs[key])
+            elif key == "thread_num":
+                self._set_thread(kwargs[key])
+            elif key == "use_var":
+                self._set_use_var(kwargs[key])
+            elif key == "input_type":
+                self._set_input_type(kwargs[key])
+            elif key == "fs_name" and "fs_ugi" in kwargs:
+                self._set_hdfs_config(kwargs[key], kwargs["fs_ugi"])
+            elif key == "download_cmd":
+                self._set_download_cmd(kwargs[key])
+            elif key == "merge_size" and kwargs.get("merge_size", -1) > 0:
+                self._set_merge_by_lineid(kwargs[key])
+            elif key == "parse_ins_id":
+                self._set_parse_ins_id(kwargs[key])
+            elif key == "parse_content":
+                self._set_parse_content(kwargs[key])
+            elif key == "fleet_send_batch_size":
+                self._set_fleet_send_batch_size(kwargs[key])
+            elif key == "fleet_send_sleep_seconds":
+                self._set_fleet_send_sleep_seconds(kwargs[key])
+            elif key == "fea_eval" and kwargs[key] == True:
+                candidate_size = kwargs.get("candidate_size", 10000)
+                self._set_fea_eval(candidate_size, True)
+
+    def init(self, **kwargs):
+        """
+        should be called only once in user's python scripts to initialize setings of dataset instance
+        Args:
+            kwargs: Keyword arguments. Currently, we support following keys in **kwargs:
+            
+            batch_size(int): batch size. It will be effective during training. default is 1.
+            thread_num(int): thread num, it is the num of readers. default is 1.
+            use_var(list): list of variables. Variables which you will use. default is [].
+            input_type(int): the input type of generated input. 0 is for one sample, 1 is for one batch. defalut is 0.
+            fs_name(str): fs name. default is "".
+            fs_ugi(str): fs ugi. default is "".
+            pipe_command(str): pipe command of current dataset. A pipe command is a UNIX pipeline command that can be used only. default is "cat"
+            download_cmd(str): customized download command. default is "cat"
+            data_feed_type(str): data feed type used in c++ code. default is "MultiSlotInMemoryDataFeed".
+            queue_num(int): Dataset output queue num, training threads get data from queues. default is -1, which is set same as thread number in c++.
+
+        Examples:
+            .. code-block:: python
+
+                import paddle
+                with open("test_queue_dataset_run_a.txt", "w") as f:
+                    data = "2 1 2 2 5 4 2 2 7 2 1 3\n"
+                    data += "2 6 2 2 1 4 2 2 4 2 2 3\n"
+                    data += "2 5 2 2 9 9 2 2 7 2 1 3\n"
+                    data += "2 7 2 2 1 9 2 3 7 2 5 3\n"
+                    f.write(data)
+                with open("test_queue_dataset_run_b.txt", "w") as f:
+                    data = "2 1 2 2 5 4 2 2 7 2 1 3\n"
+                    data += "2 6 2 2 1 4 2 2 4 2 2 3\n"
+                    data += "2 5 2 2 9 9 2 2 7 2 1 3\n"
+                    data += "2 7 2 2 1 9 2 3 7 2 5 3\n"
+                    f.write(data)
+
+                slots = ["slot1", "slot2", "slot3", "slot4"]
+                slots_vars = []
+                for slot in slots:
+                    var = fluid.data(
+                        name=slot, shape=[None, 1], dtype="int64", lod_level=1)
+                    slots_vars.append(var)
+
+                dataset = paddle.distributed.InMemoryDataset()
+                dataset.init(
+                    batch_size=1,
+                    thread_num=2,
+                    input_type=1,
+                    pipe_command="cat",
+                    use_var=slots_vars)
+                dataset.set_filelist(
+                    ["test_queue_dataset_run_a.txt", "test_queue_dataset_run_b.txt"])
+                dataset.load_into_memory()
+
+                exe = fluid.Executor(fluid.CPUPlace() if not core.is_compiled_with_cuda(
+                ) else fluid.CUDAPlace(0))
+                exe.run(fluid.default_startup_program())
+                exe.train_from_dataset(fluid.default_main_program(),
+                                           dataset)
+                os.remove("./test_queue_dataset_run_a.txt")
+                os.remove("./test_queue_dataset_run_b.txt")
+        """
+        batch_size = kwargs.get("batch_size", 1)
+        thread_num = kwargs.get("thread_num", 1)
+        use_var = kwargs.get("use_var", [])
+        input_type = kwargs.get("input_type", 0)
+        fs_name = kwargs.get("fs_name", "")
+        fs_ugi = kwargs.get("fs_ugi", "")
+        pipe_command = kwargs.get("pipe_command", "cat")
+        download_cmd = kwargs.get("download_cmd", "cat")
+
+        super(InMemoryDataset, self).init(
+            batch_size=batch_size,
+            thread_num=thread_num,
+            use_var=use_var,
+            pipe_command=pipe_command,
+            input_type=input_type,
+            fs_name=fs_name,
+            fs_ugi=fs_ugi,
+            download_cmd=download_cmd)
+
+        data_feed_type = kwargs.get("data_feed_type",
+                                    "MultiSlotInMemoryDataFeed")
+        self._set_feed_type(data_feed_type)
+
+        if kwargs.get("queue_num", -1) > 0:
+            queue_num = kwargs.get("queue_num", -1)
+            self._set_queue_num(queue_num)
+
+    def _set_feed_type(self, data_feed_type):
         """
         Set data_feed_desc
         """
@@ -373,7 +507,7 @@ class InMemoryDataset(DatasetBase):
         self.dataset.set_parse_logkey(self.parse_logkey)
         self.dataset.set_merge_by_sid(self.merge_by_sid)
         self.dataset.set_enable_pv_merge(self.enable_pv_merge)
-        self.dataset.set_data_feed_desc(self.desc())
+        self.dataset.set_data_feed_desc(self._desc())
         self.dataset.create_channel()
         self.dataset.create_readers()
 
@@ -387,7 +521,7 @@ class InMemoryDataset(DatasetBase):
             self.dataset.dynamic_adjust_channel_num(self.thread_num, False)
         self.dataset.dynamic_adjust_readers_num(self.thread_num)
 
-    def set_queue_num(self, queue_num):
+    def _set_queue_num(self, queue_num):
         """
         Set Dataset output queue num, training threads get data from queues
 
@@ -397,17 +531,17 @@ class InMemoryDataset(DatasetBase):
         Examples:
             .. code-block:: python
 
-              import paddle.fluid as fluid
-              dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
-              dataset.set_queue_num(12)
+              import paddle
+              dataset = paddle.distributed.InMemoryDataset()
+              dataset._set_queue_num(12)
 
         """
         self.is_user_set_queue_num = True
         self.queue_num = queue_num
 
-    def set_parse_ins_id(self, parse_ins_id):
+    def _set_parse_ins_id(self, parse_ins_id):
         """
-        Set id Dataset need to parse insid
+        Set if Dataset need to parse insid
 
         Args:
             parse_ins_id(bool): if parse ins_id or not
@@ -415,14 +549,14 @@ class InMemoryDataset(DatasetBase):
         Examples:
             .. code-block:: python
 
-              import paddle.fluid as fluid
-              dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
-              dataset.set_parse_ins_id(True)
+              import paddle
+              dataset = paddle.distributed.InMemoryDataset()
+              dataset._set_parse_ins_id(True)
 
         """
         self.parse_ins_id = parse_ins_id
 
-    def set_parse_content(self, parse_content):
+    def _set_parse_content(self, parse_content):
         """
         Set if Dataset need to parse content
 
@@ -432,120 +566,14 @@ class InMemoryDataset(DatasetBase):
         Examples:
             .. code-block:: python
 
-              import paddle.fluid as fluid
-              dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
-              dataset.set_parse_content(True)
+              import paddle
+              dataset = paddle.distributed.InMemoryDataset()
+              dataset._set_parse_content(True)
 
         """
         self.parse_content = parse_content
 
-    def set_parse_logkey(self, parse_logkey):
-        """
-        Set if Dataset need to parse logkey
-
-        Args:
-            parse_content(bool): if parse logkey or not
-
-        Examples:
-            .. code-block:: python
-
-              import paddle.fluid as fluid
-              dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
-              dataset.set_parse_logkey(True)
-
-        """
-        self.parse_logkey = parse_logkey
-
-    def set_merge_by_sid(self, merge_by_sid):
-        """
-        Set if Dataset need to merge sid. If not, one ins means one Pv.
-
-        Args:
-            merge_by_sid(bool): if merge sid or not
-
-        Examples:
-            .. code-block:: python
-
-              import paddle.fluid as fluid
-              dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
-              dataset.set_merge_by_sid(True)
-
-        """
-        self.merge_by_sid = merge_by_sid
-
-    def set_enable_pv_merge(self, enable_pv_merge):
-        """
-        Set if Dataset need to merge pv.
-
-        Args:
-            enable_pv_merge(bool): if enable_pv_merge or not
-
-        Examples:
-            .. code-block:: python
-
-              import paddle.fluid as fluid
-              dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
-              dataset.set_enable_pv_merge(True)
-
-        """
-        self.enable_pv_merge = enable_pv_merge
-
-    def preprocess_instance(self):
-        """
-        Merge pv instance and convey it from input_channel to input_pv_channel. 
-        It will be effective when enable_pv_merge_ is True.
-
-        Examples:
-            .. code-block:: python
-
-              import paddle.fluid as fluid
-              dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
-              filelist = ["a.txt", "b.txt"]
-              dataset.set_filelist(filelist)
-              dataset.load_into_memory()
-              dataset.preprocess_instance()
-
-        """
-        self.dataset.preprocess_instance()
-
-    def set_current_phase(self, current_phase):
-        """
-        Set current phase in train. It is useful for untest.
-        current_phase : 1 for join, 0 for update.
-
-        Examples:
-            .. code-block:: python
-
-              import paddle.fluid as fluid
-              dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
-              filelist = ["a.txt", "b.txt"]
-              dataset.set_filelist(filelist)
-              dataset.load_into_memory()
-              dataset.set_current_phase(1)
-
-        """
-        self.dataset.set_current_phase(current_phase)
-
-    def postprocess_instance(self):
-        """
-        Divide pv instance and convey it to input_channel.
-
-        Examples:
-            .. code-block:: python
-
-              import paddle.fluid as fluid
-              dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
-              filelist = ["a.txt", "b.txt"]
-              dataset.set_filelist(filelist)
-              dataset.load_into_memory()
-              dataset.preprocess_instance()
-              exe.train_from_dataset(dataset)
-              dataset.postprocess_instance()
-
-        """
-        self.dataset.postprocess_instance()
-
-    def set_fleet_send_batch_size(self, fleet_send_batch_size=1024):
+    def _set_fleet_send_batch_size(self, fleet_send_batch_size=1024):
         """
         Set fleet send batch size, default is 1024
 
@@ -555,14 +583,14 @@ class InMemoryDataset(DatasetBase):
         Examples:
             .. code-block:: python
 
-              import paddle.fluid as fluid
-              dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
-              dataset.set_fleet_send_batch_size(800)
+              import paddle
+              dataset = paddle.distributed.InMemoryDataset()
+              dataset._set_fleet_send_batch_size(800)
 
         """
         self.fleet_send_batch_size = fleet_send_batch_size
 
-    def set_fleet_send_sleep_seconds(self, fleet_send_sleep_seconds=0):
+    def _set_fleet_send_sleep_seconds(self, fleet_send_sleep_seconds=0):
         """
         Set fleet send sleep time, default is 0
 
@@ -572,14 +600,14 @@ class InMemoryDataset(DatasetBase):
         Examples:
             .. code-block:: python
 
-              import paddle.fluid as fluid
-              dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
-              dataset.set_fleet_send_sleep_seconds(2)
+              import paddle
+              dataset = paddle.distributed.InMemoryDataset()
+              dataset._set_fleet_send_sleep_seconds(2)
 
         """
         self.fleet_send_sleep_seconds = fleet_send_sleep_seconds
 
-    def set_merge_by_lineid(self, merge_size=2):
+    def _set_merge_by_lineid(self, merge_size=2):
         """
         Set merge by line id, instances of same line id will be merged after
         shuffle, you should parse line id in data generator.
@@ -590,21 +618,21 @@ class InMemoryDataset(DatasetBase):
         Examples:
             .. code-block:: python
 
-              import paddle.fluid as fluid
-              dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
-              dataset.set_merge_by_lineid()
+              import paddle
+              dataset = paddle.distributed.InMemoryDataset()
+              dataset._set_merge_by_lineid()
 
         """
         self.dataset.set_merge_by_lineid(merge_size)
         self.merge_by_lineid = True
         self.parse_ins_id = True
 
-    def set_generate_unique_feasigns(self, generate_uni_feasigns, shard_num):
+    def _set_generate_unique_feasigns(self, generate_uni_feasigns, shard_num):
         self.dataset.set_generate_unique_feasigns(generate_uni_feasigns)
         self.gen_uni_feasigns = generate_uni_feasigns
         self.local_shard_num = shard_num
 
-    def generate_local_tables_unlock(self, table_id, fea_dim, read_thread_num,
+    def _generate_local_tables_unlock(self, table_id, fea_dim, read_thread_num,
                                       consume_thread_num, shard_num):
         self.dataset.generate_local_tables_unlock(
             table_id, fea_dim, read_thread_num, consume_thread_num, shard_num)
@@ -616,8 +644,8 @@ class InMemoryDataset(DatasetBase):
         Examples:
             .. code-block:: python
 
-              import paddle.fluid as fluid
-              dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
+              import paddle
+              dataset = paddle.distributed.InMemoryDataset()
               filelist = ["a.txt", "b.txt"]
               dataset.set_filelist(filelist)
               dataset.load_into_memory()
@@ -635,8 +663,8 @@ class InMemoryDataset(DatasetBase):
         Examples:
             .. code-block:: python
 
-              import paddle.fluid as fluid
-              dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
+              import paddle
+              dataset = paddle.distributed.InMemoryDataset()
               filelist = ["a.txt", "b.txt"]
               dataset.set_filelist(filelist)
               dataset.preload_into_memory()
@@ -656,8 +684,8 @@ class InMemoryDataset(DatasetBase):
         Examples:
             .. code-block:: python
 
-              import paddle.fluid as fluid
-              dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
+              import paddle
+              dataset = paddle.distributed.InMemoryDataset()
               filelist = ["a.txt", "b.txt"]
               dataset.set_filelist(filelist)
               dataset.preload_into_memory()
@@ -673,8 +701,8 @@ class InMemoryDataset(DatasetBase):
         Examples:
             .. code-block:: python
 
-              import paddle.fluid as fluid
-              dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
+              import paddle
+              dataset = paddle.distributed.InMemoryDataset()
               filelist = ["a.txt", "b.txt"]
               dataset.set_filelist(filelist)
               dataset.load_into_memory()
@@ -692,9 +720,9 @@ class InMemoryDataset(DatasetBase):
         Examples:
             .. code-block:: python
 
-              import paddle.fluid as fluid
+              import paddle
               from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet
-              dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
+              dataset = paddle.distributed.InMemoryDataset()
               filelist = ["a.txt", "b.txt"]
               dataset.set_filelist(filelist)
               dataset.load_into_memory()
@@ -736,9 +764,9 @@ class InMemoryDataset(DatasetBase):
         Examples:
             .. code-block:: python
 
-              import paddle.fluid as fluid
+              import paddle
               from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet
-              dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
+              dataset = paddle.distributed.InMemoryDataset()
               filelist = ["a.txt", "b.txt"]
               dataset.set_filelist(filelist)
               dataset.load_into_memory()
@@ -751,30 +779,6 @@ class InMemoryDataset(DatasetBase):
         """
         self.dataset.release_memory()
 
-    def get_pv_data_size(self):
-        """
-        Get memory data size of Pv, user can call this function to know the pv num
-        of ins in all workers after load into memory.
-
-        Note:
-            This function may cause bad performance, because it has barrier
-
-        Returns:
-            The size of memory pv data.
-
-        Examples:
-            .. code-block:: python
-
-              import paddle.fluid as fluid
-              dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
-              filelist = ["a.txt", "b.txt"]
-              dataset.set_filelist(filelist)
-              dataset.load_into_memory()
-              print dataset.get_pv_data_size()
-
-        """
-        return self.dataset.get_pv_data_size()
-
     def get_memory_data_size(self, fleet=None):
         """
         Get memory data size, user can call this function to know the num
@@ -792,9 +796,9 @@ class InMemoryDataset(DatasetBase):
         Examples:
             .. code-block:: python
 
-              import paddle.fluid as fluid
+              import paddle
               from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet
-              dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
+              dataset = paddle.distributed.InMemoryDataset()
               filelist = ["a.txt", "b.txt"]
               dataset.set_filelist(filelist)
               dataset.load_into_memory()
@@ -829,9 +833,9 @@ class InMemoryDataset(DatasetBase):
         Examples:
             .. code-block:: python
 
-              import paddle.fluid as fluid
+              import paddle
               from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet
-              dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
+              dataset = paddle.distributed.InMemoryDataset()
               filelist = ["a.txt", "b.txt"]
               dataset.set_filelist(filelist)
               dataset.load_into_memory()
@@ -849,6 +853,51 @@ class InMemoryDataset(DatasetBase):
             return global_data_size[0]
         return local_data_size[0]
 
+    def _set_fea_eval(self, record_candidate_size, fea_eval=True):
+        """
+        set fea eval mode for slots shuffle to debug the importance level of
+        slots(features), fea_eval need to be set True for slots shuffle.
+        
+        Args:
+            record_candidate_size(int): size of instances candidate to shuffle 
+                                        one slot
+            fea_eval(bool): whether enable fea eval mode to enable slots shuffle.
+                            default is True.
+            
+        Examples:
+            .. code-block:: python
+
+            import paddle
+            dataset = paddle.distributed.InMemoryDataset()
+            dataset._set_fea_eval(1000000, True)
+
+        """
+        if fea_eval:
+            self.dataset.set_fea_eval(fea_eval, record_candidate_size)
+        self.fea_eval = fea_eval
+
+    def slots_shuffle(self, slots):
+        """
+        Slots Shuffle 
+        Slots Shuffle is a shuffle method in slots level, which is usually used 
+        in sparse feature with large scale of instances. To compare the metric, i.e.
+        auc while doing slots shuffle on one or several slots with baseline to 
+        evaluate the importance level of slots(features).
+        
+        Args:
+            slots(list[string]): the set of slots(string) to do slots shuffle.
+
+        Examples:
+            import paddle
+            dataset = paddle.distributed.InMemoryDataset()
+            dataset.set_merge_by_lineid()
+            #suppose there is a slot 0
+            dataset.slots_shuffle(['0'])
+        """
+        if self.fea_eval:
+            slots_set = set(slots)
+            self.dataset.slots_shuffle(slots_set)
+
 
 class QueueDataset(DatasetBase):
     """
@@ -857,19 +906,24 @@ class QueueDataset(DatasetBase):
     Examples:
         .. code-block:: python
 
-          import paddle.fluid as fluid
-          dataset = fluid.DatasetFactory().create_dataset("QueueDataset")
+          import paddle
+          dataset = paddle.distributed.QueueDataset()
 
     """
 
     def __init__(self):
         """
         Initialize QueueDataset
-        This class should be created by DatasetFactory
         """
         super(QueueDataset, self).__init__()
         self.proto_desc.name = "MultiSlotDataFeed"
 
+    def init(self, **kwargs):
+        """
+        should be called only once in user's python scripts to initialize setings of dataset instance
+        """
+        super(QueueDataset, self).init(**kwargs)
+
     def _prepare_to_run(self):
         """
         Set data_feed_desc/thread num/filelist before run,
@@ -881,115 +935,154 @@ class QueueDataset(DatasetBase):
             self.thread_num = 1
         self.dataset.set_thread_num(self.thread_num)
         self.dataset.set_filelist(self.filelist)
-        self.dataset.set_data_feed_desc(self.desc())
+        self.dataset.set_data_feed_desc(self._desc())
         self.dataset.create_readers()
 
-    def local_shuffle(self):
-        """
-        Local shuffle data.
 
-        Local shuffle is not supported in QueueDataset
-        NotImplementedError will be raised
+class FileInstantDataset(DatasetBase):
+    """
+    FileInstantDataset, it will process data streamly.
 
     Examples:
         .. code-block:: python
 
-              import paddle.fluid as fluid
-              dataset = fluid.DatasetFactory().create_dataset("QueueDataset")
-              dataset.local_shuffle()
-
-        Raises:
-            NotImplementedError: QueueDataset does not support local shuffle
+          import paddle
+          dataset = paddle.distributed.fleet.FileInstantDataset()
+    """
 
+    def __init__(self):
         """
-        raise NotImplementedError(
-            "QueueDataset does not support local shuffle, "
-            "please use InMemoryDataset for local_shuffle")
+        Initialize FileInstantDataset
+        """
+        super(FileInstantDataset, self).__init__()
+        self.proto_desc.name = "MultiSlotFileInstantDataFeed"
 
-    def global_shuffle(self, fleet=None):
+    def init(self, **kwargs):
         """
-        Global shuffle data.
+        should be called only once in user's python scripts to initialize setings of dataset instance
+        """
+        super(FileInstantDataset, self).init(**kwargs)
 
-        Global shuffle is not supported in QueueDataset
-        NotImplementedError will be raised
 
-        Args:
-            fleet(Fleet): fleet singleton. Default None.
+class BoxPSDataset(InMemoryDataset):
+    """
+    BoxPSDataset: derived from InMemoryDataset.
 
     Examples:
         .. code-block:: python
 
-              import paddle.fluid as fluid
-              from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet
-              dataset = fluid.DatasetFactory().create_dataset("QueueDataset")
-              dataset.global_shuffle(fleet)
+          import paddle
+          dataset = paddle.distributed.fleet.BoxPSDataset()
+    """
 
-        Raises:
-            NotImplementedError: QueueDataset does not support global shuffle
+    def __init__(self):
+        """
+        Initialize BoxPSDataset
+        """
+        super(BoxPSDataset, self).__init__()
+        self.boxps = core.BoxPS(self.dataset)
+        self.proto_desc.name = "PaddleBoxDataFeed"
 
+    def init(self, **kwargs):
+        """
+        should be called only once in user's python scripts to initialize setings of dataset instance
         """
-        raise NotImplementedError(
-            "QueueDataset does not support global shuffle, "
-            "please use InMemoryDataset for global_shuffle")
+        super(BoxPSDataset, self).init(**kwargs)
 
+        rank_offset = kwargs.get("rank_offset", "")
+        self._set_rank_offset(rank_offset)
+        pv_batch_size = kwargs.get("pv_batch_size", 1)
+        self._set_pv_batch_size(pv_batch_size)
+        parse_logkey = kwargs.get("parse_logkey", False)
+        self._set_parse_logkey(parse_logkey)
+        merge_by_sid = kwargs.get("merge_by_sid", False)
+        self._set_merge_by_sid(merge_by_sid)
+        enable_pv_merge = kwargs.get("enable_pv_merge", False)
+        self._set_enable_pv_merge(enable_pv_merge)
 
-class FileInstantDataset(DatasetBase):
+    def _set_rank_offset(self, rank_offset):
         """
-    FileInstantDataset, it will process data streamly.
+        Set rank_offset for merge_pv. It set the message of Pv.
 
         Examples:
             .. code-block:: python
 
-          import paddle.fluid as fluid
-          dataset = fluid.DatasetFactory.create_dataset("FileInstantDataset")
-    """
+              import paddle
+              dataset = paddle.distributed.fleet.BoxPSDataset()
+              dataset._set_rank_offset("rank_offset")
 
-    def __init__(self):
-        """
-        Initialize FileInstantDataset
-        This class should be created by DatasetFactory
-        """
-        super(FileInstantDataset, self).__init__()
-        self.proto_desc.name = "MultiSlotFileInstantDataFeed"
+        Args:
+            rank_offset(str): rank_offset's name
 
-    def local_shuffle(self):
         """
-        Local shuffle
-        FileInstantDataset does not support local shuffle
+        self.proto_desc.rank_offset = rank_offset
+
+    def _set_pv_batch_size(self, pv_batch_size):
         """
-        raise NotImplementedError(
-            "FileInstantDataset does not support local shuffle, "
-            "please use InMemoryDataset for local_shuffle")
+        Set pv batch size. It will be effective during enable_pv_merge
+
+        Examples:
+            .. code-block:: python
+
+              import paddle
+              dataset = paddle.distributed.fleet.BoxPSDataset()
+              dataset._set_pv_batch_size(128)
+        Args:
+            pv_batch_size(int): pv batch size
 
-    def global_shuffle(self, fleet=None):
         """
-        Global shuffle
-        FileInstantDataset does not support global shuffle
+        self.proto_desc.pv_batch_size = pv_batch_size
+
+    def _set_parse_logkey(self, parse_logkey):
         """
-        raise NotImplementedError(
-            "FileInstantDataset does not support global shuffle, "
-            "please use InMemoryDataset for global_shuffle")
+        Set if Dataset need to parse logkey
 
+        Args:
+            parse_content(bool): if parse logkey or not
+
+        Examples:
+            .. code-block:: python
+
+              import paddle
+              dataset = paddle.distributed.fleet.BoxPSDataset()
+              dataset._set_parse_logkey(True)
 
-class BoxPSDataset(InMemoryDataset):
         """
-    BoxPSDataset: derived from InMemoryDataset.
+        self.parse_logkey = parse_logkey
+
+    def _set_merge_by_sid(self, merge_by_sid):
+        """
+        Set if Dataset need to merge sid. If not, one ins means one Pv.
+
+        Args:
+            merge_by_sid(bool): if merge sid or not
 
         Examples:
             .. code-block:: python
 
-          import paddle.fluid as fluid
-          dataset = fluid.DatasetFactory().create_dataset("BoxPSDataset")
+              import paddle
+              dataset = paddle.distributed.fleet.BoxPSDataset()
+              dataset._set_merge_by_sid(True)
+
         """
+        self.merge_by_sid = merge_by_sid
 
-    def __init__(self):
+    def _set_enable_pv_merge(self, enable_pv_merge):
         """
-        Initialize BoxPSDataset
-        This class should be created by DatasetFactory
+        Set if Dataset need to merge pv.
+
+        Args:
+            enable_pv_merge(bool): if enable_pv_merge or not
+
+        Examples:
+            .. code-block:: python
+
+              import paddle
+              dataset = paddle.distributed.fleet.BoxPSDataset()
+              dataset._set_enable_pv_merge(True)
+
         """
-        super(BoxPSDataset, self).__init__()
-        self.boxps = core.BoxPS(self.dataset)
-        self.proto_desc.name = "PaddleBoxDataFeed"
+        self.enable_pv_merge = enable_pv_merge
 
     def set_date(self, date):
         """
@@ -1008,8 +1101,8 @@ class BoxPSDataset(InMemoryDataset):
         Examples:
             .. code-block:: python
 
-              import paddle.fluid as fluid
-              dataset = fluid.DatasetFactory().create_dataset("BoxPSDataset")
+              import paddle
+              dataset = paddle.distributed.fleet.BoxPSDataset()
               dataset.begin_pass()
         """
         self.boxps.begin_pass()
@@ -1021,8 +1114,8 @@ class BoxPSDataset(InMemoryDataset):
         Examples:
             .. code-block:: python
 
-              import paddle.fluid as fluid
-              dataset = fluid.DatasetFactory().create_dataset("BoxPSDataset")
+              import paddle
+              dataset = paddle.distributed.fleet.BoxPSDataset()
               dataset.end_pass(True)
         """
         self.boxps.end_pass(need_save_delta)
@@ -1034,8 +1127,8 @@ class BoxPSDataset(InMemoryDataset):
         Examples:
             .. code-block:: python
 
-              import paddle.fluid as fluid
-              dataset = fluid.DatasetFactory().create_dataset("BoxPSDataset")
+              import paddle
+              dataset = paddle.distributed.fleet.BoxPSDataset()
               filelist = ["a.txt", "b.txt"]
               dataset.set_filelist(filelist)
               dataset.preload_into_memory()
@@ -1049,8 +1142,8 @@ class BoxPSDataset(InMemoryDataset):
         Examples:
             .. code-block:: python
 
-              import paddle.fluid as fluid
-              dataset = fluid.DatasetFactory().create_dataset("BoxPSDataset")
+              import paddle
+              dataset = paddle.distributed.fleet.BoxPSDataset()
               filelist = ["a.txt", "b.txt"]
               dataset.set_filelist(filelist)
               dataset.load_into_memory()
@@ -1064,8 +1157,8 @@ class BoxPSDataset(InMemoryDataset):
         Examples:
             .. code-block:: python
 
-              import paddle.fluid as fluid
-              dataset = fluid.DatasetFactory().create_dataset("BoxPSDataset")
+              import paddle
+              dataset = paddle.distributed.fleet.BoxPSDataset()
               filelist = ["a.txt", "b.txt"]
               dataset.set_filelist(filelist)
               dataset.preload_into_memory()
@@ -1093,11 +1186,90 @@ class BoxPSDataset(InMemoryDataset):
             slots(list[string]): the set of slots(string) to do slots shuffle.
 
         Examples:
-            import paddle.fluid as fluid
-            dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
+            import paddle
+            dataset = paddle.distributed.fleet.BoxPSDataset()
             dataset.set_merge_by_lineid()
             #suppose there is a slot 0
             dataset.slots_shuffle(['0'])
         """
         slots_set = set(slots)
         self.boxps.slots_shuffle(slots_set)
+
+    def set_current_phase(self, current_phase):
+        """
+        Set current phase in train. It is useful for untest.
+        current_phase : 1 for join, 0 for update.
+
+        Examples:
+            .. code-block:: python
+
+              import paddle
+              dataset = paddle.distributed.fleet.BoxPSDataset()
+              filelist = ["a.txt", "b.txt"]
+              dataset.set_filelist(filelist)
+              dataset.load_into_memory()
+              dataset.set_current_phase(1)
+
+        """
+        self.dataset.set_current_phase(current_phase)
+
+    def get_pv_data_size(self):
+        """
+        Get memory data size of Pv, user can call this function to know the pv num
+        of ins in all workers after load into memory.
+
+        Note:
+            This function may cause bad performance, because it has barrier
+
+        Returns:
+            The size of memory pv data.
+
+        Examples:
+            .. code-block:: python
+
+              import paddle
+              dataset = paddle.distributed.fleet.BoxPSDataset()
+              filelist = ["a.txt", "b.txt"]
+              dataset.set_filelist(filelist)
+              dataset.load_into_memory()
+              print dataset.get_pv_data_size()
+
+        """
+        return self.dataset.get_pv_data_size()
+
+    def preprocess_instance(self):
+        """
+        Merge pv instance and convey it from input_channel to input_pv_channel. 
+        It will be effective when enable_pv_merge_ is True.
+
+        Examples:
+            .. code-block:: python
+
+              import paddle
+              dataset = paddle.distributed.fleet.BoxPSDataset()
+              filelist = ["a.txt", "b.txt"]
+              dataset.set_filelist(filelist)
+              dataset.load_into_memory()
+              dataset.preprocess_instance()
+
+        """
+        self.dataset.preprocess_instance()
+
+    def postprocess_instance(self):
+        """
+        Divide pv instance and convey it to input_channel.
+
+        Examples:
+            .. code-block:: python
+
+              import paddle
+              dataset = paddle.distributed.fleet.BoxPSDataset()
+              filelist = ["a.txt", "b.txt"]
+              dataset.set_filelist(filelist)
+              dataset.load_into_memory()
+              dataset.preprocess_instance()
+              exe.train_from_dataset(dataset)
+              dataset.postprocess_instance()
+
+        """
+        self.dataset.postprocess_instance()

python/paddle/distributed/fleet/launch.py

@@ -157,17 +157,20 @@ def get_cluster_from_args(args, gpus):
 
         free_ports = [x for x in range(start_port, start_port + len(gpus))]
 
-    return get_cluster(node_ips, node_ip, free_ports, gpus)
+    trainer_endpoints = []
+    for ip in node_ips:
+        trainer_endpoints.append(["%s:%d" % (ip, port) for port in free_ports])
+    return get_cluster(node_ips, node_ip, trainer_endpoints, gpus)
 
 
 def get_gpus(gpus):
     if gpus is None:
         gpus_num = fluid.core.get_cuda_device_count()
-        gpus = [str(x) for x in range(0, gpus_num)]
+        res_gpus = [str(x) for x in range(0, gpus_num)]
     else:
         cuda_visible_devices = os.getenv("CUDA_VISIBLE_DEVICES")
         if cuda_visible_devices is None or cuda_visible_devices == "":
-            gpus = [x.strip() for x in gpus.split(',')]
+            res_gpus = [x.strip() for x in gpus.split(',')]
         else:
             # change gpus into relative values
             # e.g. CUDA_VISIBLE_DEVICES=4,5,6,7; args.gpus=4,5,6,7;
@@ -177,12 +180,16 @@ def get_gpus(gpus):
                 assert x in cuda_visible_devices_list, "Can't find "\
                 "your gpus %s in CUDA_VISIBLE_DEVICES[%s]."\
                 % (x, cuda_visible_devices)
-            gpus = [
+            res_gpus = [
                 cuda_visible_devices_list.index(x.strip())
                 for x in gpus.split(',')
             ]
+            logger.info("Change selected_gpus into reletive values. --ips:{} "
+                        "will change into relative_ips:{} according to your "
+                        "CUDA_VISIBLE_DEVICES:{}".format(
+                            gpus, res_gpus, cuda_visible_devices_list))
 
-    return gpus
+    return res_gpus
 
 
 def launch_collective(args):

python/paddle/distributed/fleet/launch_utils.py

@@ -227,18 +227,23 @@ def get_logger(log_level=20, name="root"):
     return logger
 
 
-def get_cluster(node_ips, node_ip, paddle_ports, selected_gpus):
-    assert type(paddle_ports) is list, "paddle_ports must be list"
+def get_cluster(node_ips, node_ip, trainer_endpoints, selected_gpus):
+    assert type(trainer_endpoints) is list, "trainer_endpoints must be list"
     cluster = Cluster(hdfs=None)
     trainer_rank = 0
     for node_rank, ip in enumerate(node_ips):
         pod = Pod()
         pod.rank = node_rank
         pod.addr = ip
+        cur_node_endpoints = trainer_endpoints[node_rank]
+        # when use paddlecloud, endpoints may > selected_gpus(user_defined)
+        assert len(cur_node_endpoints) >= len(
+            selected_gpus
+        ), "current trainer_endpoints size should be greater equal than selected_gpus size."
         for i in range(len(selected_gpus)):
             trainer = Trainer()
             trainer.gpus.append(selected_gpus[i])
-            trainer.endpoint = "%s:%d" % (ip, paddle_ports[i])
+            trainer.endpoint = "%s" % (cur_node_endpoints[i])
             trainer.rank = trainer_rank
             trainer_rank += 1
 
@@ -424,10 +429,6 @@ def start_local_trainers(cluster,
                             len(pod.trainers),
                             pretty_print_envs(proc_env, ("Distributed Envs",
                                                          "Value"))))
-            logger.info(
-                "More details for debug about commands and environments are written in {}/run.sh".
-                format(log_dir))
-
         fn = None
         if log_dir is not None:
             os.system("mkdir -p {}".format(log_dir))

python/paddle/distributed/fleet/meta_optimizers/recompute_optimizer.py

@@ -38,7 +38,7 @@ class RecomputeOptimizer(MetaOptimizerBase):
             list(user_defined_strategy.recompute_configs["checkpoints"]))
 
     def _can_apply(self):
-        if self.role_maker._is_collective:
+        if not self.role_maker._is_collective:
             return False
 
         if self.user_defined_strategy.recompute == True:

python/paddle/distributed/launch.py

@@ -160,18 +160,21 @@ def get_cluster_from_args(args, selected_gpus):
             x for x in range(started_port, started_port + len(selected_gpus))
         ]
 
-    return get_cluster(node_ips, node_ip, free_ports, selected_gpus)
+    trainer_endpoints = []
+    for ip in node_ips:
+        trainer_endpoints.append(["%s:%d" % (ip, port) for port in free_ports])
+    return get_cluster(node_ips, node_ip, trainer_endpoints, selected_gpus)
 
 
 def get_gpus(selected_gpus):
     if selected_gpus is None:
         from paddle.fluid import core
         gpus_num = core.get_cuda_device_count()
-        selected_gpus = [str(x) for x in range(0, gpus_num)]
+        gpus = [str(x) for x in range(0, gpus_num)]
     else:
         cuda_visible_devices = os.getenv("CUDA_VISIBLE_DEVICES")
         if cuda_visible_devices is None or cuda_visible_devices == "":
-            selected_gpus = [x.strip() for x in selected_gpus.split(',')]
+            gpus = [x.strip() for x in selected_gpus.split(',')]
         else:
             # change selected_gpus into relative values
             # e.g. CUDA_VISIBLE_DEVICES=4,5,6,7; args.selected_gpus=4,5,6,7;
@@ -181,12 +184,16 @@ def get_gpus(selected_gpus):
                 assert x in cuda_visible_devices_list, "Can't find "\
                 "your selected_gpus %s in CUDA_VISIBLE_DEVICES[%s]."\
                 % (x, cuda_visible_devices)
-            selected_gpus = [
+            gpus = [
                 cuda_visible_devices_list.index(x.strip())
                 for x in selected_gpus.split(',')
             ]
+            logger.info("Change selected_gpus into reletive values. --ips:{} "
+                        "will change into relative_ips:{} according to your "
+                        "CUDA_VISIBLE_DEVICES:{}".format(
+                            selected_gpus, gpus, cuda_visible_devices_list))
 
-    return selected_gpus
+    return gpus
 
 
 def get_cluster_and_pod(args):

python/paddle/distributed/utils.py

@@ -227,18 +227,23 @@ def get_logger(log_level, name="root"):
     return logger
 
 
-def get_cluster(node_ips, node_ip, paddle_ports, selected_gpus):
-    assert type(paddle_ports) is list, "paddle_ports must be list"
+def get_cluster(node_ips, node_ip, trainer_endpoints, selected_gpus):
+    assert type(trainer_endpoints) is list, "trainer_endpoints must be list"
     cluster = Cluster(hdfs=None)
     trainer_rank = 0
     for node_rank, ip in enumerate(node_ips):
         pod = Pod()
         pod.rank = node_rank
         pod.addr = ip
+        cur_node_endpoints = trainer_endpoints[node_rank]
+        # when use paddlecloud, endpoints may > selected_gpus(user_defined)
+        assert len(cur_node_endpoints) >= len(
+            selected_gpus
+        ), "current trainer_endpoints size should be greater equal than selected_gpus size."
         for i in range(len(selected_gpus)):
             trainer = Trainer()
             trainer.gpus.append(selected_gpus[i])
-            trainer.endpoint = "%s:%d" % (ip, paddle_ports[i])
+            trainer.endpoint = "%s" % (cur_node_endpoints[i])
             trainer.rank = trainer_rank
             trainer_rank += 1
 
@@ -253,6 +258,7 @@ def terminate_local_procs(procs):
     for p in procs:
         if p.proc.poll() is None:
             p.proc.terminate()
+            if p.log_fn:
                 p.log_fn.close()
             logger.debug("terminate process id:{}".format(p.proc.pid))
 

python/paddle/fluid/contrib/slim/quantization/post_training_quantization.py

@@ -143,7 +143,7 @@ class PostTrainingQuantization(object):
                  weight_quantize_type='channel_wise_abs_max',
                  optimize_model=False,
                  is_use_cache_file=False,
-                 cache_dir="./temp_post_training"):
+                 cache_dir=None):
         '''
         Constructor.
 
@@ -206,13 +206,8 @@ class PostTrainingQuantization(object):
                 `conv2d/depthwise_conv2d + bn`, the weights scale for all channel will
                 be different. In address this problem, fuse the pattern before
                 quantization. Default False.
-            is_use_cache_file(bool, optional): If set is_use_cache_file as False,
-                all temp data will be saved in memory. If set is_use_cache_file as True,
-                it will save temp data to disk. When the fp32 model is complex or
-                the number of calibrate data is large, we should set is_use_cache_file
-                as True. Defalut is False.
-            cache_dir(str, optional): When is_use_cache_file is True, set cache_dir as
-                the directory for saving temp data. Default is ./temp_post_training.
+            is_use_cache_file(bool, optional): This param is deprecated.
+            cache_dir(str, optional): This param is deprecated.
         Returns:
             None
 
@@ -302,10 +297,6 @@ class PostTrainingQuantization(object):
                 assert op_type in self._support_quantize_op_type, \
                     op_type + " is not supported for quantization."
         self._optimize_model = optimize_model
-        self._is_use_cache_file = is_use_cache_file
-        self._cache_dir = cache_dir
-        if self._is_use_cache_file and not os.path.exists(self._cache_dir):
-            os.mkdir(self._cache_dir)
 
         # Define variables
         self._place = self._executor.place
@@ -317,11 +308,17 @@ class PostTrainingQuantization(object):
         self._out_scale_op_list = _out_scale_op_list
         self._quantized_weight_var_name = set()
         self._quantized_act_var_name = set()
-        self.weight_op_pairs = {}
+        self._weight_op_pairs = {}
+        # The vars for alog = KL
+        self._sampling_act_abs_min_max = {}
+        self._sampling_act_histogram = {}
         self._sampling_data = {}
         self._quantized_var_kl_threshold = {}
+        self._histogram_bins = 2048
+        # The vars for algo = min_max
         self._quantized_var_min = {}
         self._quantized_var_max = {}
+        # The vars for algo = abs_max
         self._quantized_var_abs_max = {}
 
     def quantize(self):
@@ -339,6 +336,8 @@ class PostTrainingQuantization(object):
         self._collect_target_varnames()
         self._set_activation_persistable()
 
+        if self._algo == "KL":
+            _logger.info("Preparation stage ...")
             batch_id = 0
             for data in self._data_loader():
                 self._executor.run(program=self._program,
@@ -346,17 +345,30 @@ class PostTrainingQuantization(object):
                                    fetch_list=self._fetch_list,
                                    return_numpy=False,
                                    scope=self._scope)
-            if self._algo == "KL":
-                self._sample_data(batch_id)
-            else:
-                self._sample_threshold()
+                self._collect_activation_abs_min_max()
+                if batch_id % 5 == 0:
+                    _logger.info("Run batch: " + str(batch_id))
+                batch_id += 1
+                if self._batch_nums and batch_id >= self._batch_nums:
+                    break
+            _logger.info("Finish preparation stage, all batch:" + str(batch_id))
+            self._init_sampling_act_histogram()
 
+        _logger.info("Sampling stage ...")
+        batch_id = 0
+        for data in self._data_loader():
+            self._executor.run(program=self._program,
+                               feed=data,
+                               fetch_list=self._fetch_list,
+                               return_numpy=False,
+                               scope=self._scope)
+            self._sampling()
             if batch_id % 5 == 0:
                 _logger.info("Run batch: " + str(batch_id))
             batch_id += 1
             if self._batch_nums and batch_id >= self._batch_nums:
                 break
-        _logger.info("Finish all batch: " + str(batch_id))
+        _logger.info("Finish sampling stage, all batch: " + str(batch_id))
 
         self._reset_activation_persistable()
 
@@ -397,6 +409,7 @@ class PostTrainingQuantization(object):
             target_vars=self._fetch_list,
             executor=self._executor,
             main_program=self._program)
+        _logger.info("The quantized model is saved in " + save_model_path)
 
     def _load_model_data(self):
         '''
@@ -454,7 +467,7 @@ class PostTrainingQuantization(object):
             for var_name in var_name_list:
                 if var_name in persistable_var_names:
                     self._quantized_weight_var_name.add(var_name)
-                    self.weight_op_pairs[var_name] = op_type
+                    self._weight_op_pairs[var_name] = op_type
                 else:
                     self._quantized_act_var_name.add(var_name)
 
@@ -494,20 +507,18 @@ class PostTrainingQuantization(object):
             if var.name in self._quantized_act_var_name:
                 var.persistable = False
 
-    def _sample_threshold(self):
+    def _sampling(self):
         '''
-        Sample the input threshold(min, max, or abs_max) in every iterations.
+        Sample the min/max, abs_max or histogram in every iterations.
         '''
-        assert self._algo in ["abs_max", "min_max"], \
-            "The algo should be abs_max or min_max for _sample_threshold."
         if self._algo == "abs_max":
-            self._sample_threshold_abs_max()
+            self._sample_abs_max()
         elif self._algo == "min_max":
-            self._sample_threshold_min_max()
+            self._sample_min_max()
+        elif self._algo == "KL":
+            self._sample_histogram()
 
-    def _sample_threshold_abs_max(self):
-        assert self._algo == "abs_max", \
-            "The algo should be abs_max for _sample_threshold_abs_max."
+    def _sample_abs_max(self):
         # Only calculate abs_max value for weight for once
         if self._quantized_var_abs_max == {}:
             for var_name in self._quantized_weight_var_name:
@@ -516,7 +527,7 @@ class PostTrainingQuantization(object):
                     abs_max_value = float(np.max(np.abs(var_tensor)))
                 elif self._weight_quantize_type == "channel_wise_abs_max":
                     abs_max_value = []
-                    if self.weight_op_pairs[
+                    if self._weight_op_pairs[
                             var_name] in _channelwise_quant_axis1_ops:
                         for i in range(var_tensor.shape[1]):
                             abs_max_value.append(
@@ -534,9 +545,7 @@ class PostTrainingQuantization(object):
                 (abs_max_value > self._quantized_var_abs_max[var_name]):
                 self._quantized_var_abs_max[var_name] = abs_max_value
 
-    def _sample_threshold_min_max(self):
-        assert self._algo == "min_max", \
-            "The algo should be min_max for _sample_threshold_min_max."
+    def _sample_min_max(self):
         if self._quantized_var_min == {} and self._quantized_var_max == {}:
             for var_name in self._quantized_weight_var_name:
                 var_tensor = _load_variable_data(self._scope, var_name)
@@ -546,7 +555,7 @@ class PostTrainingQuantization(object):
                 elif self._weight_quantize_type == "channel_wise_abs_max":
                     min_value = []
                     max_value = []
-                    if self.weight_op_pairs[
+                    if self._weight_op_pairs[
                             var_name] in _channelwise_quant_axis1_ops:
                         for i in range(var_tensor.shape[1]):
                             min_value.append(float(np.min(var_tensor[:, i])))
@@ -569,6 +578,14 @@ class PostTrainingQuantization(object):
                 (max_value > self._quantized_var_max[var_name]):
                 self._quantized_var_max[var_name] = max_value
 
+    def _sample_histogram(self):
+        for var_name in self._quantized_act_var_name:
+            var_tensor = _load_variable_data(self._scope, var_name)
+            var_tensor_abs = np.abs(var_tensor)
+            bins = self._sampling_act_histogram[var_name][1]
+            hist, _ = np.histogram(var_tensor_abs, bins=bins)
+            self._sampling_act_histogram[var_name][0] += hist
+
     def _save_input_threhold(self):
         '''
         Save input threshold to the quantized op.
@@ -585,27 +602,36 @@ class PostTrainingQuantization(object):
                     op._set_attr(var_name + ".max",
                                  self._quantized_var_max[var_name])
 
-    def _sample_data(self, iter):
+    def _collect_activation_abs_min_max(self):
         '''
-        Sample the tensor data of quantized variables, 
-        applied in every iteration.
+        Collect the abs_min and abs_max for all activation. When algo = KL,
+        get the min and max value, and then calculate the threshold.
         '''
-        assert self._algo == "KL", "The algo should be KL to sample data."
-        if self._is_use_cache_file:
         for var_name in self._quantized_act_var_name:
             var_tensor = _load_variable_data(self._scope, var_name)
-                var_tensor = var_tensor.ravel()
-                save_path = os.path.join(
-                    self._cache_dir,
-                    var_name.replace("/", ".") + "_" + str(iter) + ".npy")
-                np.save(save_path, var_tensor)
+            var_tensor = np.abs(var_tensor)
+            min_value = float(np.min(var_tensor))
+            max_value = float(np.max(var_tensor))
+            if var_name not in self._sampling_act_abs_min_max:
+                self._sampling_act_abs_min_max[
+                    var_name] = [min_value, max_value]
             else:
+                if min_value < self._sampling_act_abs_min_max[var_name][0]:
+                    self._sampling_act_abs_min_max[var_name][0] = min_value
+                if max_value > self._sampling_act_abs_min_max[var_name][1]:
+                    self._sampling_act_abs_min_max[var_name][1] = max_value
+
+    def _init_sampling_act_histogram(self):
+        '''
+        Based on the min/max value, init the sampling_act_histogram.
+        '''
         for var_name in self._quantized_act_var_name:
-                if var_name not in self._sampling_data:
-                    self._sampling_data[var_name] = []
-                var_tensor = _load_variable_data(self._scope, var_name)
-                var_tensor = var_tensor.ravel()
-                self._sampling_data[var_name].append(var_tensor)
+            if var_name not in self._sampling_act_histogram:
+                min_val = self._sampling_act_abs_min_max[var_name][0]
+                max_val = self._sampling_act_abs_min_max[var_name][1]
+                hist, hist_edeges = np.histogram(
+                    [], bins=self._histogram_bins, range=(min_val, max_val))
+                self._sampling_act_histogram[var_name] = [hist, hist_edeges]
 
     def _calculate_kl_threshold(self):
         '''
@@ -621,7 +647,7 @@ class PostTrainingQuantization(object):
                 weight_threshold = float(np.max(np.abs(weight_data)))
             elif self._weight_quantize_type == "channel_wise_abs_max":
                 weight_threshold = []
-                if self.weight_op_pairs[
+                if self._weight_op_pairs[
                         var_name] in _channelwise_quant_axis1_ops:
                     for i in range(weight_data.shape[1]):
                         weight_threshold.append(
@@ -632,25 +658,10 @@ class PostTrainingQuantization(object):
                             float(np.max(np.abs(weight_data[i]))))
             self._quantized_var_kl_threshold[var_name] = weight_threshold
 
-        # KL threshold for activations
-        if self._is_use_cache_file:
         for var_name in self._quantized_act_var_name:
-                sampling_data = []
-                filenames = [f for f in os.listdir(self._cache_dir) \
-                    if re.match(var_name.replace("/", ".")  + '_[0-9]+.npy', f)]
-                for filename in filenames:
-                    file_path = os.path.join(self._cache_dir, filename)
-                    sampling_data.append(np.load(file_path))
-                    os.remove(file_path)
-                sampling_data = np.concatenate(sampling_data)
+            hist, hist_edeges = self._sampling_act_histogram[var_name]
             self._quantized_var_kl_threshold[var_name] = \
-                    self._get_kl_scaling_factor(np.abs(sampling_data))
-        else:
-            for var_name in self._quantized_act_var_name:
-                self._sampling_data[var_name] = np.concatenate(
-                    self._sampling_data[var_name])
-                self._quantized_var_kl_threshold[var_name] = \
-                    self._get_kl_scaling_factor(np.abs(self._sampling_data[var_name]))
+                self._get_kl_scaling_factor(hist, hist_edeges)
 
     def _update_program(self):
         '''
@@ -765,22 +776,15 @@ class PostTrainingQuantization(object):
                 for var_name in out_var_names:
                     analysis_and_save_info(op, var_name)
 
-    def _get_kl_scaling_factor(self, activation_blob, num_quantized_bins=255):
+    def _get_kl_scaling_factor(self, hist, hist_edeges, num_quantized_bins=255):
         '''
         Using the KL-divergenc method to get the more precise scaling factor.
         '''
-        max_val = np.max(activation_blob)
-        min_val = np.min(activation_blob)
-        if min_val >= 0:
-            hist, hist_edeges = np.histogram(
-                activation_blob, bins=2048, range=(min_val, max_val))
-            ending_iter = 2047
+        ending_iter = self._histogram_bins - 1
         starting_iter = int(ending_iter * 0.7)
-        else:
-            _logger.error("Please first apply abs to activation_blob.")
         bin_width = hist_edeges[1] - hist_edeges[0]
 
-        P_sum = len(np.array(activation_blob).ravel())
+        P_sum = np.sum(np.array(hist).ravel())
         min_kl_divergence = 0
         min_kl_index = 0
         kl_inited = False

python/paddle/fluid/dygraph/io.py

@@ -19,6 +19,7 @@ import six
 import pickle
 import numpy as np
 
+import paddle
 from paddle import compat as cpt
 from paddle.fluid import core
 from paddle.fluid import framework
@@ -182,9 +183,9 @@ class _ProgramHolder(object):
         super(_ProgramHolder, self).__init__()
 
         # input, output, persistable var info
-        self._input_names = []
-        self._persistable_names = []
+        self._input_descs = []
         self._output_descs = []
+        self._persistable_names = []
 
         # execution scope
         self._inner_scope = core.Scope()
@@ -207,11 +208,11 @@ class _ProgramHolder(object):
         return self._train_program_desc
 
     @property
-    def input_names(self):
-        return self._input_names
+    def input_descs(self):
+        return self._input_descs
 
     @property
-    def output_decs(self):
+    def output_descs(self):
         return self._output_descs
 
     @property
@@ -233,7 +234,8 @@ class _ProgramHolder(object):
                 ops_to_remove.append(i)
                 feed_var_name = cpt.to_bytes(op.input('X')[0])
                 root_block._remove_var(feed_var_name)
-                self._input_names.append(cpt.to_bytes(op.output('Out')[0]))
+                self._input_descs.append(
+                    root_block.find_var(cpt.to_bytes(op.output('Out')[0])))
             elif op.type() == 'scale' and op.output('Out')[0].startswith(
                     'save_infer_model/scale_'):
                 ops_to_remove.append(i)
@@ -257,7 +259,7 @@ class _ProgramHolder(object):
             root_block._remove_op(op_idx, op_idx + 1)
 
         # 2. Input processing, reverse feed vars
-        self._input_names.reverse()
+        self._input_descs.reverse()
 
         # 3. Output processing, add scale for outputs
         tmp_program = _build_program_by_desc(program_desc)
@@ -738,7 +740,7 @@ class TranslatedLayer(layers.Layer):
                 if isinstance(value, np.ndarray):
                     var = core.VarBase(
                         value=value,
-                        name=program_holder.input_names[i],
+                        name=program_holder.input_descs[i].name(),
                         persistable=False,
                         place=framework._current_expected_place(),
                         zero_copy=True)
@@ -746,7 +748,7 @@ class TranslatedLayer(layers.Layer):
                     var = value
                     # NOTE: we changed var name here, 
                     # but it may be an important name set by user
-                    var.name = program_holder.input_names[i]
+                    var.name = program_holder.input_descs[i].name()
                 input_vars.append(var)
 
             persistable_vars = []
@@ -762,7 +764,7 @@ class TranslatedLayer(layers.Layer):
                         % var_name)
 
             output_vars = []
-            for var_desc in program_holder.output_decs:
+            for var_desc in program_holder.output_descs:
                 var = core.VarBase(var_desc.dtype(),
                                    var_desc.shape(),
                                    var_desc.name(), var_desc.type(), False)
@@ -913,11 +915,7 @@ class TranslatedLayer(layers.Layer):
                 program = translated_layer.program()
         """
         # 1. get program holder
-        program_holder = self._program_holder_dict.get(method_name, None)
-        if program_holder is None:
-            raise ValueError(
-                "The method `%s` is not exists in loaded TranslatedLayer." %
-                method_name)
+        program_holder = self._get_program_holder(method_name)
 
         # 2. get inference program desc
         program_desc = program_holder.infer_program
@@ -925,3 +923,44 @@ class TranslatedLayer(layers.Layer):
         # 3. construct program
         program = _build_program_by_desc(program_desc)
         return program
+
+    def _get_program_holder(self, method_name='forward'):
+        program_holder = self._program_holder_dict.get(method_name, None)
+        if program_holder is None:
+            raise ValueError(
+                "The method `%s` does not exist in loaded TranslatedLayer." %
+                method_name)
+        return program_holder
+
+    def _input_spec(self, method_name='forward'):
+        # 1. get program holder
+        program_holder = self._get_program_holder(method_name)
+
+        # 2. build input spec by input desc
+        input_spec = []
+        for var_desc in program_holder.input_descs:
+            spec = paddle.static.InputSpec(
+                shape=var_desc.shape(),
+                dtype=var_desc.dtype(),
+                name=var_desc.name())
+            input_spec.append(spec)
+
+        return input_spec
+
+    def _output_spec(self, method_name='forward'):
+        # 1. get program holder
+        program_holder = self._get_program_holder(method_name)
+
+        # 2. build output spec by output desc
+        output_spec = []
+        for var_desc in program_holder.output_descs:
+            # NOTE(chenweihang): InputSpec describes a tensor, not just input. 
+            # Maybe the name is not good enough. Here we use InputSpec to 
+            # construct the description of Output tensor
+            spec = paddle.static.InputSpec(
+                shape=var_desc.shape(),
+                dtype=var_desc.dtype(),
+                name=var_desc.name())
+            output_spec.append(spec)
+
+        return output_spec

python/paddle/fluid/reader.py

@@ -1726,13 +1726,13 @@ class DatasetLoader(DataLoaderBase):
             logging.warn('thread_num {} which is set in Dataset is ignored'.
                          format(dataset.thread_num))
 
-        dataset.set_thread(thread_num)
+        dataset._set_thread(thread_num)
 
         if isinstance(dataset, paddle.distributed.fleet.dataset.
                       InMemoryDataset) and dataset.queue_num > thread_num:
             logging.warn("queue_num {} which is set in Dataset is ignored".
                          format(dataset.queue_num))
-            dataset.set_queue_num(thread_num)
+            dataset._set_queue_num(thread_num)
 
         self._dataset = dataset
         use_slots = [

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

@@ -102,6 +102,7 @@ if(WIN32)
 endif()
 
 
+LIST(REMOVE_ITEM TEST_OPS test_fleet_rolemaker_new)
 LIST(REMOVE_ITEM TEST_OPS test_auto_checkpoint)
 LIST(REMOVE_ITEM TEST_OPS test_auto_checkpoint1)
 LIST(REMOVE_ITEM TEST_OPS test_auto_checkpoint2)
@@ -399,17 +400,17 @@ py_test_modules(test_bilinear_interp_op MODULES test_bilinear_interp_op ENVS ${G
 py_test_modules(test_nearest_interp_op MODULES test_nearest_interp_op ENVS ${GC_ENVS})
 py_test_modules(test_imperative_resnet MODULES test_imperative_resnet ENVS
     FLAGS_cudnn_deterministic=1 SERIAL)
-set_tests_properties(test_imperative_resnet PROPERTIES LABELS "RUN_TYPE=EXCLUSIVE")
+set_tests_properties(test_imperative_resnet PROPERTIES LABELS "RUN_TYPE=EXCLUSIVE:NIGHTLY")
 py_test_modules(test_imperative_resnet_sorted_gradient MODULES test_imperative_resnet_sorted_gradient ENVS
         FLAGS_cudnn_deterministic=1 SERIAL)
-set_tests_properties(test_imperative_resnet_sorted_gradient PROPERTIES LABELS "RUN_TYPE=EXCLUSIVE")
+set_tests_properties(test_imperative_resnet_sorted_gradient PROPERTIES LABELS "RUN_TYPE=EXCLUSIVE:NIGHTLY")
 py_test_modules(test_imperative_mnist MODULES test_imperative_mnist ENVS
     FLAGS_cudnn_deterministic=1)
 py_test_modules(test_imperative_mnist_sorted_gradient MODULES test_imperative_mnist_sorted_gradient ENVS
         FLAGS_cudnn_deterministic=1)
 py_test_modules(test_imperative_se_resnext MODULES test_imperative_se_resnext ENVS
     FLAGS_cudnn_deterministic=1 SERIAL)
-set_tests_properties(test_imperative_se_resnext PROPERTIES LABELS "RUN_TYPE=EXCLUSIVE")
+set_tests_properties(test_imperative_se_resnext PROPERTIES LABELS "RUN_TYPE=EXCLUSIVE:NIGHTLY")
 py_test_modules(test_imperative_ocr_attention_model MODULES test_imperative_ocr_attention_model ENVS
         FLAGS_cudnn_deterministic=1 SERIAL)
 py_test_modules(test_install_check MODULES test_install_check ENVS

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

@@ -208,14 +208,16 @@ class TestDistCTR2x2(FleetDistRunnerBase):
         filelist = train_file_list
 
         # config dataset
-        dataset = paddle.distributed.fleet.DatasetFactory().create_dataset()
-        dataset.set_batch_size(batch_size)
-        dataset.set_use_var(self.feeds)
+        dataset = paddle.distributed.QueueDataset()
         pipe_command = 'python ctr_dataset_reader.py'
-        dataset.set_pipe_command(pipe_command)
+
+        dataset.init(
+            batch_size=batch_size,
+            use_var=self.feeds,
+            pipe_command=pipe_command,
+            thread_num=thread_num)
 
         dataset.set_filelist(filelist)
-        dataset.set_thread(thread_num)
 
         for epoch_id in range(1):
             pass_start = time.time()

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

@@ -114,14 +114,14 @@ class TestDistGpuPsCTR2x2(TestDistCTR2x2):
             filelist.append(train_file_path)
 
         # config dataset
-        dataset = paddle.fleet.DatasetFactory().create_dataset()
-        dataset.set_batch_size(batch_size)
-        dataset.set_use_var(self.feeds)
+        dataset = paddle.distributed.QueueDataset()
+        dataset._set_batch_size(batch_size)
+        dataset._set_use_var(self.feeds)
         pipe_command = 'python ctr_dataset_reader.py'
-        dataset.set_pipe_command(pipe_command)
+        dataset._set_pipe_command(pipe_command)
 
         dataset.set_filelist(filelist)
-        dataset.set_thread(thread_num)
+        dataset._set_thread(thread_num)
 
         for epoch_id in range(1):
             pass_start = time.time()

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

@@ -183,14 +183,14 @@ class TestHeterPsCTR2x2(FleetDistHeterRunnerBase):
         print("filelist: {}".format(filelist))
 
         # config dataset
-        dataset = paddle.distributed.fleet.DatasetFactory().create_dataset()
-        dataset.set_batch_size(batch_size)
-        dataset.set_use_var(self.feeds)
+        dataset = paddle.distributed.QueueDataset()
+        dataset._set_batch_size(batch_size)
+        dataset._set_use_var(self.feeds)
         pipe_command = 'python ctr_dataset_reader.py'
-        dataset.set_pipe_command(pipe_command)
+        dataset._set_pipe_command(pipe_command)
 
         dataset.set_filelist(filelist)
-        dataset.set_thread(thread_num)
+        dataset._set_thread(thread_num)
 
         for epoch_id in range(1):
             pass_start = time.time()

python/paddle/fluid/tests/unittests/ir/inference/test_transpose_flatten_concat_fuse_pass.py

@@ -17,6 +17,7 @@ import numpy as np
 from inference_pass_test import InferencePassTest
 import paddle.fluid as fluid
 import paddle.fluid.core as core
+from paddle.fluid.core import PassVersionChecker
 
 
 class TransposeFlattenConcatFusePassTest(InferencePassTest):
@@ -45,6 +46,37 @@ class TransposeFlattenConcatFusePassTest(InferencePassTest):
             use_gpu = True
             self.check_output_with_option(use_gpu)
 
+        PassVersionChecker.IsCompatible('transpose_flatten_concat_fuse_pass')
+
+
+class TransposeFlattenConcatFusePassWithAxisTest(InferencePassTest):
+    def setUp(self):
+        with fluid.program_guard(self.main_program, self.startup_program):
+            data1 = fluid.data(name="data1", shape=[5, 5, 5], dtype="float32")
+            data2 = fluid.data(name="data2", shape=[5, 5, 5], dtype="float32")
+            trans1 = fluid.layers.transpose(data1, perm=[2, 1, 0])
+            trans2 = fluid.layers.transpose(data2, perm=[2, 1, 0])
+            flatt1 = fluid.layers.flatten(trans1, axis=2)
+            flatt2 = fluid.layers.flatten(trans2, axis=2)
+            concat_out = fluid.layers.concat([flatt1, flatt2], axis=1)
+            # There is no parameters for above structure. 
+            # Hence, append a batch_norm to avoid failure caused by load_combined. 
+            out = fluid.layers.batch_norm(concat_out, is_test=True)
+
+        self.feeds = {
+            "data1": np.random.random([5, 5, 5]).astype("float32"),
+            "data2": np.random.random([5, 5, 5]).astype("float32")
+        }
+        self.fetch_list = [out]
+
+    def test_check_output(self):
+        # There is no cpu pass for transpose_flatten_concat_fuse
+        if core.is_compiled_with_cuda():
+            use_gpu = True
+            self.check_output_with_option(use_gpu)
+
+        PassVersionChecker.IsCompatible('transpose_flatten_concat_fuse_pass')
+
 
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/ir/inference/test_trt_pad_op.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+from inference_pass_test import InferencePassTest
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+from paddle.fluid.core import AnalysisConfig
+
+
+class PadOpTRTTest(InferencePassTest):
+    def setUp(self):
+        with fluid.program_guard(self.main_program, self.startup_program):
+            data = fluid.data(
+                name="data", shape=[1, 3, 128, 128], dtype="float32")
+            pad_out = fluid.layers.pad(x=data,
+                                       paddings=[0, 0, 0, 0, 0, 1, 1, 2],
+                                       pad_value=0.0)
+            out = fluid.layers.batch_norm(pad_out, is_test=True)
+
+        self.feeds = {
+            "data": np.random.random((1, 3, 128, 128)).astype("float32")
+        }
+        self.enable_trt = True
+        self.trt_parameters = PadOpTRTTest.TensorRTParam(
+            1 << 30, 32, 1, AnalysisConfig.Precision.Float32, False, False)
+        self.fetch_list = [out]
+
+    def test_check_output(self):
+        use_gpu = [False]
+        if core.is_compiled_with_cuda():
+            use_gpu.append(True)
+
+        for i in range(len(use_gpu)):
+            self.check_output_with_option(use_gpu[i])
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/ir/inference/test_trt_slice_plugin.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+from inference_pass_test import InferencePassTest
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+from paddle.fluid.core import AnalysisConfig
+
+
+#normal starts && ends
+class SlicePluginTRTTest1(InferencePassTest):
+    def setUp(self):
+        with fluid.program_guard(self.main_program, self.startup_program):
+            data = fluid.data(name="data", shape=[3, 3, 3, 3], dtype="float32")
+            axes = [1, 3]
+            starts = [0, 1]
+            ends = [2, 3]
+            slice_out = fluid.layers.slice(
+                data, axes=axes, starts=starts, ends=ends)
+            out = fluid.layers.batch_norm(slice_out, is_test=True)
+
+        self.feeds = {
+            "data": np.random.random((3, 3, 3, 3)).astype("float32"),
+        }
+        # Diff occurred between GPU and TRT. 
+        # In order to provide TRT CI ASAP, this test for trt part 
+        # is disabled temporarily. 
+        self.enable_trt = True
+        self.trt_parameters = SlicePluginTRTTest1.TensorRTParam(
+            1 << 30, 32, 1, AnalysisConfig.Precision.Float32, False, False)
+        self.fetch_list = [out]
+
+    def test_check_output(self):
+        use_gpu = [False]
+        if core.is_compiled_with_cuda():
+            use_gpu.append(True)
+        for i in range(len(use_gpu)):
+            self.check_output_with_option(use_gpu[i])
+
+
+#negative starts && ends
+class SlicePluginTRTTest2(InferencePassTest):
+    def setUp(self):
+        with fluid.program_guard(self.main_program, self.startup_program):
+            data = fluid.data(name="data", shape=[3, 3, 3, 3], dtype="float32")
+            axes = [2, 3]
+            starts = [-3, -2]
+            ends = [-1, 3]
+            slice_out = fluid.layers.slice(
+                data, axes=axes, starts=starts, ends=ends)
+            out = fluid.layers.batch_norm(slice_out, is_test=True)
+
+        self.feeds = {
+            "data": np.random.random((3, 3, 3, 3)).astype("float32"),
+        }
+        # Diff occurred between GPU and TRT. 
+        # In order to provide TRT CI ASAP, this test for trt part 
+        # is disabled temporarily. 
+        self.enable_trt = True
+        self.trt_parameters = SlicePluginTRTTest2.TensorRTParam(
+            1 << 30, 32, 1, AnalysisConfig.Precision.Float32, False, False)
+        self.fetch_list = [out]
+
+    def test_check_output(self):
+        use_gpu = [False]
+        if core.is_compiled_with_cuda():
+            use_gpu.append(True)
+        for i in range(len(use_gpu)):
+            self.check_output_with_option(use_gpu[i])
+
+
+#exceeded bound starts && ends
+class SlicePluginTRTTest3(InferencePassTest):
+    def setUp(self):
+        with fluid.program_guard(self.main_program, self.startup_program):
+            data = fluid.data(name="data", shape=[3, 3, 3, 3], dtype="float32")
+            axes = [2, 3]
+            starts = [-5, -2]
+            ends = [-1, 8]
+            slice_out = fluid.layers.slice(
+                data, axes=axes, starts=starts, ends=ends)
+            out = fluid.layers.batch_norm(slice_out, is_test=True)
+
+        self.feeds = {
+            "data": np.random.random((3, 3, 3, 3)).astype("float32"),
+        }
+        # Diff occurred between GPU and TRT. 
+        # In order to provide TRT CI ASAP, this test for trt part 
+        # is disabled temporarily. 
+        self.enable_trt = True
+        self.trt_parameters = SlicePluginTRTTest3.TensorRTParam(
+            1 << 30, 32, 1, AnalysisConfig.Precision.Float32, False, False)
+        self.fetch_list = [out]
+
+    def test_check_output(self):
+        use_gpu = [False]
+        if core.is_compiled_with_cuda():
+            use_gpu.append(True)
+        for i in range(len(use_gpu)):
+            self.check_output_with_option(use_gpu[i])
+
+
+#fp16
+class SlicePluginTRTTest4(InferencePassTest):
+    def setUp(self):
+        with fluid.program_guard(self.main_program, self.startup_program):
+            data = fluid.data(name="data", shape=[3, 3, 3, 3], dtype="float32")
+            axes = [2, 3]
+            starts = [-5, -2]
+            ends = [-1, 8]
+            slice_out = fluid.layers.slice(
+                data, axes=axes, starts=starts, ends=ends)
+            out = fluid.layers.batch_norm(slice_out, is_test=True)
+
+        self.feeds = {
+            "data": np.random.random((3, 3, 3, 3)).astype("float32"),
+        }
+        # Diff occurred between GPU and TRT. 
+        # In order to provide TRT CI ASAP, this test for trt part 
+        # is disabled temporarily. 
+        self.enable_trt = True
+        self.trt_parameters = SlicePluginTRTTest3.TensorRTParam(
+            1 << 30, 32, 1, AnalysisConfig.Precision.Half, False, False)
+        self.fetch_list = [out]
+
+    def test_check_output(self):
+        use_gpu = [False]
+        if core.is_compiled_with_cuda():
+            use_gpu.append(True)
+        for i in range(len(use_gpu)):
+            self.check_output_with_option(use_gpu[i])
+
+
+if __name__ == "__main__":
+    unittest.main()

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

+# Copyright (c) 2020 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
+import paddle.fluid.core as core
+
+
+class TestBroadcastOpCpu(OpTest):
+    def setUp(self):
+        self.op_type = "broadcast"
+        input = np.random.random((100, 2)).astype("float32")
+        np_out = input[:]
+        self.inputs = {"X": input}
+        self.attrs = {"sync_mode": False, "root": 0}
+        self.outputs = {"Out": np_out}
+
+    def test_check_output_cpu(self):
+        try:
+            self.check_output_with_place(place=core.CPUPlace())
+        except:
+            print("do not support cpu test, skip")
+
+
+if __name__ == "__main__":
+    unittest.main()

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

@@ -38,26 +38,22 @@ class TestDataset(unittest.TestCase):
     def test_dataset_create(self):
         """ Testcase for dataset create. """
         try:
-            dataset = paddle.distributed.fleet.DatasetFactory().create_dataset(
-                "InMemoryDataset")
+            dataset = paddle.distributed.InMemoryDataset()
         except:
             self.assertTrue(False)
 
         try:
-            dataset = paddle.distributed.fleet.DatasetFactory().create_dataset(
-                "QueueDataset")
+            dataset = paddle.distributed.QueueDataset()
         except:
             self.assertTrue(False)
 
         try:
-            dataset = paddle.distributed.fleet.DatasetFactory().create_dataset(
-                "FileInstantDataset")
+            dataset = paddle.distributed.fleet.dataset.FileInstantDataset()
         except:
             self.assertTrue(False)
 
         try:
-            dataset = paddle.distributed.fleet.DatasetFactory().create_dataset(
-                "MyOwnDataset")
+            dataset = paddle.distributed.fleet.dataset.MyOwnDataset()
             self.assertTrue(False)
         except:
             self.assertTrue(True)
@@ -95,18 +91,18 @@ class TestDataset(unittest.TestCase):
                 name=slot, shape=[1], dtype="int64", lod_level=1)
             slots_vars.append(var)
 
-        dataset = paddle.distributed.fleet.DatasetFactory().create_dataset(
-            "InMemoryDataset")
-        dataset.set_batch_size(32)
-        dataset.set_thread(3)
+        dataset = paddle.distributed.InMemoryDataset()
+        dataset.init(
+            batch_size=32, thread_num=3, pipe_command="cat", use_var=slots_vars)
+        dataset.update_settings(pipe_command="cat1")
+        dataset._init_distributed_settings(
+            parse_ins_id=True,
+            parse_content=True,
+            fea_eval=True,
+            candidate_size=10000)
         dataset.set_filelist(
             ["test_run_with_dump_a.txt", "test_run_with_dump_b.txt"])
-        dataset.set_parse_ins_id(True)
-        dataset.set_parse_content(True)
-        dataset.set_pipe_command("cat")
-        dataset.set_use_var(slots_vars)
         dataset.load_into_memory()
-        dataset.set_fea_eval(10000, True)
         dataset.local_shuffle()
 
         exe = fluid.Executor(fluid.CPUPlace())
@@ -176,14 +172,14 @@ class TestDataset(unittest.TestCase):
                 name=slot, shape=[1], dtype="int64", lod_level=1)
             slots_vars.append(var)
 
-        dataset = paddle.distributed.fleet.DatasetFactory().create_dataset(
-            "InMemoryDataset")
-        dataset.set_batch_size(32)
-        dataset.set_thread(3)
+        dataset = paddle.distributed.InMemoryDataset()
+        dataset.init(
+            batch_size=32,
+            thread_num=3,
+            pipe_command="cat",
+            download_cmd="cat",
+            use_var=slots_vars)
         dataset.set_filelist([filename1, filename2])
-        dataset.set_pipe_command("cat")
-        dataset.set_download_cmd("cat")
-        dataset.set_use_var(slots_vars)
         dataset.load_into_memory()
         exe = fluid.Executor(fluid.CPUPlace())
         exe.run(fluid.default_startup_program())
@@ -228,22 +224,19 @@ class TestDataset(unittest.TestCase):
                 name=slot, shape=[1], dtype="int64", lod_level=1)
             slots_vars.append(var)
 
-        dataset = paddle.distributed.fleet.DatasetFactory().create_dataset(
-            "InMemoryDataset")
-        dataset.set_batch_size(32)
-        dataset.set_thread(3)
+        dataset = paddle.distributed.InMemoryDataset()
+        dataset.init(
+            batch_size=32, thread_num=3, pipe_command="cat", use_var=slots_vars)
+        dataset._init_distributed_settings(fea_eval=True, candidate_size=1)
         dataset.set_filelist([
             "test_in_memory_dataset_run_a.txt",
             "test_in_memory_dataset_run_b.txt"
         ])
-        dataset.set_pipe_command("cat")
-        dataset.set_use_var(slots_vars)
         dataset.load_into_memory()
-        dataset.set_fea_eval(1, True)
         dataset.slots_shuffle(["slot1"])
         dataset.local_shuffle()
-        dataset.set_generate_unique_feasigns(True, 15)
-        dataset.generate_local_tables_unlock(0, 11, 1, 25, 15)
+        dataset._set_generate_unique_feasigns(True, 15)
+        dataset._generate_local_tables_unlock(0, 11, 1, 25, 15)
         exe = fluid.Executor(fluid.CPUPlace())
         exe.run(fluid.default_startup_program())
         if self.use_data_loader:
@@ -300,17 +293,14 @@ class TestDataset(unittest.TestCase):
                     name=slot, shape=[1], dtype="float32", lod_level=1)
                 slots_vars.append(var)
 
-        dataset = paddle.distributed.fleet.DatasetFactory().create_dataset(
-            "InMemoryDataset")
-        dataset.set_batch_size(32)
-        dataset.set_thread(1)
-        dataset.set_parse_ins_id(True)
+        dataset = paddle.distributed.InMemoryDataset()
+        dataset.init(
+            batch_size=32, thread_num=1, pipe_command="cat", use_var=slots_vars)
+        dataset._init_distributed_settings(parse_ins_id=True)
         dataset.set_filelist([
             "test_in_memory_dataset_masterpatch_a.txt",
             "test_in_memory_dataset_masterpatch_b.txt"
         ])
-        dataset.set_pipe_command("cat")
-        dataset.set_use_var(slots_vars)
         dataset.load_into_memory()
         dataset.local_shuffle()
 
@@ -325,7 +315,8 @@ class TestDataset(unittest.TestCase):
             except Exception as e:
                 self.assertTrue(False)
 
-        dataset.set_merge_by_lineid(2)
+        #dataset._set_merge_by_lineid(2)
+        dataset.update_settings(merge_size=2)
         dataset.dataset.merge_by_lineid()
 
         os.remove("./test_in_memory_dataset_masterpatch_a.txt")
@@ -367,17 +358,14 @@ class TestDataset(unittest.TestCase):
                 name="slot4", shape=[1], dtype="float32", lod_level=0)
             slots_vars = [var1, var2, var3, var4]
 
-        dataset = paddle.distributed.fleet.DatasetFactory().create_dataset(
-            "InMemoryDataset")
-        dataset.set_batch_size(32)
-        dataset.set_thread(1)
-        dataset.set_parse_ins_id(True)
+        dataset = paddle.distributed.InMemoryDataset()
+        dataset.init(
+            batch_size=32, thread_num=1, pipe_command="cat", use_var=slots_vars)
+        dataset._init_distributed_settings(parse_ins_id=True)
         dataset.set_filelist([
             "test_in_memory_dataset_masterpatch1_a.txt",
             "test_in_memory_dataset_masterpatch1_b.txt"
         ])
-        dataset.set_pipe_command("cat")
-        dataset.set_use_var(slots_vars)
         dataset.load_into_memory()
         dataset.local_shuffle()
 
@@ -392,7 +380,7 @@ class TestDataset(unittest.TestCase):
             except Exception as e:
                 self.assertTrue(False)
 
-        dataset.set_merge_by_lineid(2)
+        dataset._set_merge_by_lineid(2)
         dataset.dataset.merge_by_lineid()
 
         os.remove("./test_in_memory_dataset_masterpatch1_a.txt")
@@ -423,16 +411,13 @@ class TestDataset(unittest.TestCase):
                 name=slot, shape=[1], dtype="float32", lod_level=1)
             slots_vars.append(var)
 
-        dataset = paddle.distributed.fleet.DatasetFactory().create_dataset(
-            "InMemoryDataset")
-        dataset.set_batch_size(32)
-        dataset.set_thread(3)
+        dataset = paddle.distributed.InMemoryDataset()
+        dataset.init(
+            batch_size=32, thread_num=3, pipe_command="cat", use_var=slots_vars)
         dataset.set_filelist([
             "test_in_memory_dataset_run_a.txt",
             "test_in_memory_dataset_run_b.txt"
         ])
-        dataset.set_pipe_command("cat")
-        dataset.set_use_var(slots_vars)
         dataset.load_into_memory()
         dataset.local_shuffle()
 
@@ -473,9 +458,9 @@ class TestDataset(unittest.TestCase):
                 except Exception as e:
                     self.assertTrue(False)
 
-        dataset.set_merge_by_lineid(2)
-        dataset.set_parse_ins_id(False)
-        dataset.set_fleet_send_sleep_seconds(2)
+        dataset._set_merge_by_lineid(2)
+        dataset._set_parse_ins_id(False)
+        dataset._set_fleet_send_sleep_seconds(2)
         dataset.preload_into_memory()
         dataset.wait_preload_done()
         dataset.release_memory()
@@ -483,10 +468,25 @@ class TestDataset(unittest.TestCase):
         dataset.wait_preload_done()
         dataset.dataset.merge_by_lineid()
         dataset.release_memory()
-        dataset.set_merge_by_lineid(30)
-        dataset.set_parse_ins_id(False)
+        dataset._set_merge_by_lineid(30)
+        dataset._set_parse_ins_id(False)
         dataset.load_into_memory()
         dataset.dataset.merge_by_lineid()
+        dataset.update_settings(
+            batch_size=1,
+            thread_num=2,
+            input_type=1,
+            pipe_command="cat",
+            use_var=[],
+            fs_name="",
+            fs_ugi="",
+            download_cmd="cat",
+            merge_size=-1,
+            parse_ins_id=False,
+            parse_content=False,
+            fleet_send_batch_size=2,
+            fleet_send_sleep_seconds=2,
+            fea_eval=True)
         fleet_ptr = fluid.core.Fleet()
         fleet_ptr.set_client2client_config(1, 1, 1)
         fleet_ptr.get_cache_threshold(0)
@@ -517,14 +517,11 @@ class TestDataset(unittest.TestCase):
                 name=slot, shape=[1], dtype="int64", lod_level=1)
             slots_vars.append(var)
 
-        dataset = paddle.distributed.fleet.DatasetFactory().create_dataset(
-            "QueueDataset")
-        dataset.set_batch_size(32)
-        dataset.set_thread(3)
+        dataset = paddle.distributed.QueueDataset()
+        dataset.init(
+            batch_size=32, thread_num=3, pipe_command="cat", use_var=slots_vars)
         dataset.set_filelist(
             ["test_queue_dataset_run_a.txt", "test_queue_dataset_run_b.txt"])
-        dataset.set_pipe_command("cat")
-        dataset.set_use_var(slots_vars)
 
         exe = fluid.Executor(fluid.CPUPlace())
         exe.run(fluid.default_startup_program())
@@ -543,12 +540,9 @@ class TestDataset(unittest.TestCase):
                 except Exception as e:
                     self.assertTrue(False)
 
-        dataset2 = paddle.distributed.fleet.DatasetFactory().create_dataset(
-            "QueueDataset")
-        dataset2.set_use_var(slots_vars)
-        dataset2.set_batch_size(32)
-        dataset2.set_thread(3)
-        dataset2.set_pipe_command("cat")
+        dataset2 = paddle.distributed.QueueDataset()
+        dataset2.init(
+            batch_size=32, thread_num=3, pipe_command="cat", use_var=slots_vars)
         dataset.set_filelist([])
         try:
             exe.train_from_dataset(fluid.default_main_program(), dataset2)
@@ -585,14 +579,11 @@ class TestDataset(unittest.TestCase):
                 name=slot, shape=[1], dtype="float32", lod_level=1)
             slots_vars.append(var)
 
-        dataset = paddle.distributed.fleet.DatasetFactory().create_dataset(
-            "QueueDataset")
-        dataset.set_batch_size(32)
-        dataset.set_thread(3)
+        dataset = paddle.distributed.QueueDataset()
+        dataset.init(
+            batch_size=32, thread_num=3, pipe_command="cat", use_var=slots_vars)
         dataset.set_filelist(
             ["test_queue_dataset_run_a.txt", "test_queue_dataset_run_b.txt"])
-        dataset.set_pipe_command("cat")
-        dataset.set_use_var(slots_vars)
 
         exe = fluid.Executor(fluid.CPUPlace() if not core.is_compiled_with_cuda(
         ) else fluid.CUDAPlace(0))
@@ -641,15 +632,15 @@ class TestDataset(unittest.TestCase):
                 name=slot, shape=[None, 1], dtype="int64", lod_level=1)
             slots_vars.append(var)
 
-        dataset = paddle.distributed.fleet.DatasetFactory().create_dataset(
-            "InMemoryDataset")
-        dataset.set_input_type(1)
-        dataset.set_batch_size(1)
-        dataset.set_thread(2)
+        dataset = paddle.distributed.InMemoryDataset()
+        dataset.init(
+            batch_size=1,
+            thread_num=2,
+            input_type=1,
+            pipe_command="cat",
+            use_var=slots_vars)
         dataset.set_filelist(
             ["test_queue_dataset_run_a.txt", "test_queue_dataset_run_b.txt"])
-        dataset.set_pipe_command("cat")
-        dataset.set_use_var(slots_vars)
         dataset.load_into_memory()
 
         exe = fluid.Executor(fluid.CPUPlace() if not core.is_compiled_with_cuda(
@@ -721,13 +712,10 @@ class TestDatasetWithFetchHandler(unittest.TestCase):
             inputs(list): inputs of get_dataset
             files(list): files of  get_dataset
         """
-        dataset = paddle.distributed.fleet.DatasetFactory().create_dataset(
-            "QueueDataset")
-        dataset.set_batch_size(32)
-        dataset.set_thread(3)
+        dataset = paddle.distributed.QueueDataset()
+        dataset.init(
+            batch_size=32, thread_num=3, pipe_command="cat", use_var=inputs)
         dataset.set_filelist(files)
-        dataset.set_pipe_command("cat")
-        dataset.set_use_var(inputs)
         return dataset
 
     def setUp(self):
@@ -879,16 +867,17 @@ class TestDataset2(unittest.TestCase):
             except ImportError as e:
                 print("warning: no mpi4py")
             exe.run(startup_program)
-            dataset = paddle.distributed.fleet.DatasetFactory().create_dataset(
-                "InMemoryDataset")
-            dataset.set_batch_size(32)
-            dataset.set_thread(3)
+            dataset = paddle.distributed.InMemoryDataset()
+
+            dataset.init(
+                batch_size=32,
+                thread_num=3,
+                pipe_command="cat",
+                use_var=slots_vars)
             dataset.set_filelist([
                 "test_in_memory_dataset2_run_a.txt",
                 "test_in_memory_dataset2_run_b.txt"
             ])
-            dataset.set_pipe_command("cat")
-            dataset.set_use_var(slots_vars)
             dataset.load_into_memory()
             fleet._opt_info = None
             fleet._fleet_ptr = None
@@ -949,16 +938,16 @@ class TestDataset2(unittest.TestCase):
             except ImportError as e:
                 print("warning: no mpi4py")
             exe.run(startup_program)
-            dataset = paddle.distributed.fleet.DatasetFactory().create_dataset(
-                "InMemoryDataset")
-            dataset.set_batch_size(32)
-            dataset.set_thread(3)
+            dataset = paddle.distributed.InMemoryDataset()
+            dataset.init(
+                batch_size=32,
+                thread_num=3,
+                pipe_command="cat",
+                use_var=slots_vars)
             dataset.set_filelist([
                 "test_in_memory_dataset2_run2_a.txt",
                 "test_in_memory_dataset2_run2_b.txt"
             ])
-            dataset.set_pipe_command("cat")
-            dataset.set_use_var(slots_vars)
             dataset.load_into_memory()
             try:
                 dataset.global_shuffle(fleet)
@@ -966,14 +955,11 @@ class TestDataset2(unittest.TestCase):
                 print("warning: catch expected error")
             fleet._opt_info = None
             fleet._fleet_ptr = None
-            dataset = paddle.distributed.fleet.DatasetFactory().create_dataset(
-                "InMemoryDataset")
-            dataset.set_rank_offset("")
-            dataset.set_pv_batch_size(1)
-            dataset.set_hdfs_config("", "")
+            dataset = paddle.distributed.InMemoryDataset()
+            dataset.init(fs_name="", fs_ugi="")
             d = paddle.distributed.fleet.DatasetBase()
             try:
-                dataset.set_feed_type("MultiSlotInMemoryDataFeed")
+                dataset._set_feed_type("MultiSlotInMemoryDataFeed")
             except:
                 print("warning: catch expected error")
             dataset.thread_num = 0
@@ -981,9 +967,6 @@ class TestDataset2(unittest.TestCase):
                 dataset._prepare_to_run()
             except:
                 print("warning: catch expected error")
-            dataset.set_parse_logkey(True)
-            dataset.set_merge_by_sid(True)
-            dataset.set_enable_pv_merge(True)
             try:
                 dataset.preprocess_instance()
             except:
@@ -996,16 +979,15 @@ class TestDataset2(unittest.TestCase):
                 dataset.postprocess_instance()
             except:
                 print("warning: catch expected error")
-            dataset.set_fleet_send_batch_size(1024)
+            dataset._set_fleet_send_batch_size(1024)
             try:
                 dataset.global_shuffle()
             except:
                 print("warning: catch expected error")
-            dataset.get_pv_data_size()
+            #dataset.get_pv_data_size()
             dataset.get_memory_data_size()
             dataset.get_shuffle_data_size()
-            dataset = paddle.distributed.fleet.DatasetFactory().create_dataset(
-                "QueueDataset")
+            dataset = paddle.distributed.QueueDataset()
             try:
                 dataset.local_shuffle()
             except:
@@ -1027,6 +1009,120 @@ class TestDataset2(unittest.TestCase):
         os.remove("./test_in_memory_dataset2_run2_a.txt")
         os.remove("./test_in_memory_dataset2_run2_b.txt")
 
+    def test_bosps_dataset_fleet2(self):
+        """
+        Testcase for InMemoryDataset from create to run.
+        """
+        with open("test_in_memory_dataset2_run2_a.txt", "w") as f:
+            data = "1 1 2 3 3 4 5 5 5 5 1 1\n"
+            data += "1 2 2 3 4 4 6 6 6 6 1 2\n"
+            data += "1 3 2 3 5 4 7 7 7 7 1 3\n"
+            f.write(data)
+        with open("test_in_memory_dataset2_run2_b.txt", "w") as f:
+            data = "1 4 2 3 3 4 5 5 5 5 1 4\n"
+            data += "1 5 2 3 4 4 6 6 6 6 1 5\n"
+            data += "1 6 2 3 5 4 7 7 7 7 1 6\n"
+            data += "1 7 2 3 6 4 8 8 8 8 1 7\n"
+            f.write(data)
+
+        train_program = fluid.Program()
+        startup_program = fluid.Program()
+        scope = fluid.Scope()
+        from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet
+        with fluid.program_guard(train_program, startup_program):
+            slots = ["slot1_ff", "slot2_ff", "slot3_ff", "slot4_ff"]
+            slots_vars = []
+            for slot in slots:
+                var = fluid.layers.data(\
+                    name=slot, shape=[1], dtype="float32", lod_level=1)
+                slots_vars.append(var)
+            fake_cost = \
+                fluid.layers.elementwise_sub(slots_vars[0], slots_vars[-1])
+            fake_cost = fluid.layers.mean(fake_cost)
+        with fluid.scope_guard(scope):
+            place = fluid.CPUPlace()
+            exe = fluid.Executor(place)
+            try:
+                fleet.init()
+            except ImportError as e:
+                print("warning: no mpi4py")
+            adam = fluid.optimizer.Adam(learning_rate=0.000005)
+            try:
+                adam = fleet.distributed_optimizer(
+                    adam,
+                    strategy={
+                        "fs_uri": "fs_uri_xxx",
+                        "fs_user": "fs_user_xxx",
+                        "fs_passwd": "fs_passwd_xxx",
+                        "fs_hadoop_bin": "fs_hadoop_bin_xxx"
+                    })
+                adam.minimize([fake_cost], [scope])
+            except AttributeError as e:
+                print("warning: no mpi")
+            except ImportError as e:
+                print("warning: no mpi4py")
+            exe.run(startup_program)
+            dataset = paddle.distributed.fleet.BoxPSDataset()
+            dataset.init(
+                batch_size=32,
+                thread_num=3,
+                pipe_command="cat",
+                use_var=slots_vars)
+            dataset.set_filelist([
+                "test_in_memory_dataset2_run2_a.txt",
+                "test_in_memory_dataset2_run2_b.txt"
+            ])
+            dataset.load_into_memory()
+            try:
+                dataset.global_shuffle(fleet)
+            except:
+                print("warning: catch expected error")
+            fleet._opt_info = None
+            fleet._fleet_ptr = None
+            dataset = paddle.distributed.fleet.BoxPSDataset()
+            dataset.init(
+                rank_offset="",
+                pv_batch_size=1,
+                fs_name="",
+                fs_ugi="",
+                data_feed_type="MultiSlotInMemoryDataFeed",
+                parse_logkey=True,
+                merge_by_sid=True,
+                enable_pv_merge=True)
+            d = paddle.distributed.fleet.DatasetBase()
+            try:
+                dataset._set_feed_type("MultiSlotInMemoryDataFeed")
+            except:
+                print("warning: catch expected error")
+            dataset.thread_num = 0
+            try:
+                dataset._prepare_to_run()
+            except:
+                print("warning: catch expected error")
+            dataset._set_parse_logkey(True)
+            dataset._set_merge_by_sid(True)
+            dataset._set_enable_pv_merge(True)
+            try:
+                dataset.preprocess_instance()
+            except:
+                print("warning: catch expected error")
+            try:
+                dataset.set_current_phase(1)
+            except:
+                print("warning: catch expected error")
+            try:
+                dataset.postprocess_instance()
+            except:
+                print("warning: catch expected error")
+            dataset._set_fleet_send_batch_size(1024)
+            try:
+                dataset.global_shuffle()
+            except:
+                print("warning: catch expected error")
+            #dataset.get_pv_data_size()
+            dataset.get_memory_data_size()
+            dataset.get_shuffle_data_size()
+
 
 if __name__ == '__main__':
     unittest.main()

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

@@ -97,9 +97,11 @@ class DatasetLoaderTestBase(unittest.TestCase):
 
     def check_batch_number(self, place, randomize_batch_num=False):
         main_prog, startup_prog, feeds = self.build_network()
-        dataset = paddle.distributed.fleet.DatasetFactory().create_dataset(
-            self.dataset_name)
-        dataset.set_batch_size(BATCH_SIZE)
+        if self.dataset_name == "QueueDataset":
+            dataset = paddle.distributed.QueueDataset()
+        else:
+            dataset = paddle.distributed.InMemoryDataset()
+        dataset._set_batch_size(BATCH_SIZE)
 
         if isinstance(place, fluid.CPUPlace):
             file_num = 10
@@ -128,8 +130,8 @@ class DatasetLoaderTestBase(unittest.TestCase):
                 fake_reader(batch_num=BATCH_NUM + random_delta_batch_size[i]))
 
         dataset.set_filelist(filelist)
-        dataset.set_use_var(feeds)
-        dataset.set_pipe_command("cat")
+        dataset._set_use_var(feeds)
+        dataset._set_pipe_command("cat")
         if self.dataset_name == 'InMemoryDataset':
             dataset.load_into_memory()
 

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

@@ -141,7 +141,7 @@ class TestFleetLambMetaOptimizer(unittest.TestCase):
         ops = [op.type for op in avg_cost.block.ops]
         self.assertIn('lamb', ops)
         self.assertIn('cast', ops)
-        self.assertIn('isfinite', ops)
+        self.assertIn('check_finite_and_unscale', ops)
 
 
 if __name__ == "__main__":

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

@@ -145,7 +145,7 @@ class TestFleetLarsMetaOptimizer(unittest.TestCase):
         ops = [op.type for op in avg_cost.block.ops]
         self.assertIn('lars_momentum', ops)
         self.assertIn('cast', ops)
-        self.assertIn('isfinite', ops)
+        self.assertIn('check_finite_and_unscale', ops)
 
 
 if __name__ == "__main__":

python/paddle/fluid/tests/unittests/test_fleet_launch.sh

@@ -79,9 +79,9 @@ if [ -f $file_1 ]; then
     rm $file_1
 fi
 
-
+# test use DISTRIBUTED_TRAINER_ENDPOINTS env in paddlecloud
 unset PADDLE_PORT
-unset TRAINER_PORTS_NUM
+export DISTRIBUTED_TRAINER_ENDPOINTS=127.0.0.1:6170,127.0.0.1:6171,127.0.0.2:6170,127.0.0.2:6171
 
 echo ""
 echo "paddle.distributed.launch async poll process test"

python/paddle/fluid/tests/unittests/test_fleet_rolemaker_2.py

@@ -163,10 +163,9 @@ class TestCloudRoleMaker2(unittest.TestCase):
             data = "1 1 1 1\n"
             f.write(data)
 
-        dataset = paddle.distributed.fleet.DatasetFactory().create_dataset(
-            "InMemoryDataset")
+        dataset = paddle.distributed.InMemoryDataset()
         dataset.set_filelist(["test_fleet_gloo_role_maker_1.txt"])
-        dataset.set_use_var([show, label])
+        dataset._set_use_var([show, label])
         dataset.load_into_memory()
         dataset.get_memory_data_size(fleet)
         dataset.get_shuffle_data_size(fleet)

python/paddle/fluid/tests/unittests/test_launch.sh

@@ -48,9 +48,9 @@ if [ -f $file_1 ]; then
     rm $file_1
 fi
 
-
+# test use DISTRIBUTED_TRAINER_ENDPOINTS env in paddlecloud
 unset PADDLE_PORT
-unset TRAINER_PORTS_NUM
+export DISTRIBUTED_TRAINER_ENDPOINTS=127.0.0.1:6170,127.0.0.1:6171,127.0.0.2:6170,127.0.0.2:6171
 
 echo ""
 echo "paddle.distributed.launch async poll process test"

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

@@ -400,7 +400,8 @@ class TestCUDNNLstmOp(OpTest):
             'Input': input,
             'W': flat_w,
             'InitH': init_h,
-            'InitC': init_c
+            'InitC': init_c,
+            'SequenceLength': self.sequence_length
         }
         self.attrs = {
             'dropout_prob': 0.0,
@@ -408,7 +409,6 @@ class TestCUDNNLstmOp(OpTest):
             'input_size': input_size,
             'hidden_size': hidden_size,
             'num_layers': 1,
-            'sequence_length': self.sequence_length.tolist()
         }
         self.outputs = {
             'Out': output,
@@ -436,13 +436,6 @@ class TestCUDNNLstmOp(OpTest):
 @unittest.skipIf(not core.is_compiled_with_cuda(),
                  "core is not compiled with CUDA")
 class TestCUDNNLstmOp2(TestCUDNNLstmOp):
-    def set_attrs(self):
-        self.sequence_length = np.array([], dtype=np.int32)
-
-
-@unittest.skipIf(not core.is_compiled_with_cuda(),
-                 "core is not compiled with CUDA")
-class TestCUDNNLstmOp3(TestCUDNNLstmOp):
     def set_attrs(self):
         self.num_layers = 2
 

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

@@ -52,18 +52,17 @@ class TestDatasetWithStat(unittest.TestCase):
                 name=slot, shape=[1], dtype="int64", lod_level=1)
             slots_vars.append(var)
 
-        dataset = paddle.distributed.fleet.DatasetFactory().create_dataset(
-            "InMemoryDataset")
-        dataset.set_batch_size(32)
-        dataset.set_thread(3)
+        dataset = paddle.distributed.InMemoryDataset()
+        dataset._set_batch_size(32)
+        dataset._set_thread(3)
         dataset.set_filelist([
             "test_in_memory_dataset_run_a.txt",
             "test_in_memory_dataset_run_b.txt"
         ])
-        dataset.set_pipe_command("cat")
-        dataset.set_use_var(slots_vars)
+        dataset._set_pipe_command("cat")
+        dataset._set_use_var(slots_vars)
         dataset.load_into_memory()
-        dataset.set_fea_eval(1, True)
+        dataset._set_fea_eval(1, True)
         dataset.slots_shuffle(["slot1"])
 
         exe = fluid.Executor(fluid.CPUPlace())

python/paddle/fluid/tests/unittests/test_regularizer_api.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
+from functools import partial
+import contextlib
+import numpy as np
+import paddle
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+import paddle.fluid.framework as framework
+import paddle.fluid.optimizer as optimizer
+import paddle.regularizer as regularizer
+from paddle.fluid.backward import append_backward
+
+
+def bow_net(data,
+            label,
+            dict_dim,
+            is_sparse=False,
+            emb_dim=8,
+            hid_dim=8,
+            hid_dim2=6,
+            class_dim=2):
+    """
+    BOW net
+    This model is from https://github.com/PaddlePaddle/models:
+    fluid/PaddleNLP/text_classification/nets.py
+    """
+    emb = fluid.layers.embedding(
+        input=data, is_sparse=is_sparse, size=[dict_dim, emb_dim])
+    bow = fluid.layers.sequence_pool(input=emb, pool_type='sum')
+    bow_tanh = fluid.layers.tanh(bow)
+    fc_1 = fluid.layers.fc(input=bow_tanh, size=hid_dim, act="tanh")
+    fc_2 = fluid.layers.fc(input=fc_1, size=hid_dim2, act="tanh")
+    prediction = fluid.layers.fc(input=[fc_2], size=class_dim, act="softmax")
+    cost = fluid.layers.cross_entropy(input=prediction, label=label)
+    avg_cost = fluid.layers.mean(x=cost)
+
+    return avg_cost
+
+
+class TestRegularizer(unittest.TestCase):
+    def setUp(self):
+        self.word_dict = paddle.dataset.imdb.word_dict()
+        reader = paddle.batch(
+            paddle.dataset.imdb.train(self.word_dict), batch_size=1)()
+        self.train_data = [next(reader) for _ in range(1)]
+
+    def get_places(self):
+        places = [core.CPUPlace()]
+        if core.is_compiled_with_cuda():
+            places.append(core.CUDAPlace(0))
+        return places
+
+    @contextlib.contextmanager
+    def scope_prog_guard(self, main_prog, startup_prog):
+        scope = fluid.core.Scope()
+        with fluid.unique_name.guard():
+            with fluid.scope_guard(scope):
+                with fluid.program_guard(main_prog, startup_prog):
+                    yield
+
+    def run_program(self, place, feed_list):
+        exe = fluid.Executor(place)
+        feeder = fluid.DataFeeder(feed_list=feed_list, place=place)
+        exe.run(fluid.default_startup_program())
+
+        main_prog = fluid.default_main_program()
+        param_list = [var.name for var in main_prog.block(0).all_parameters()]
+
+        param_sum = []
+        for data in self.train_data:
+            out = exe.run(main_prog,
+                          feed=feeder.feed(data),
+                          fetch_list=param_list)
+            p_sum = 0
+            for v in out:
+                p_sum += np.sum(np.abs(v))
+            param_sum.append(p_sum)
+        return param_sum
+
+    def check_l2decay_regularizer(self, place, model):
+        paddle.manual_seed(1)
+        paddle.framework.random._manual_program_seed(1)
+        main_prog = fluid.framework.Program()
+        startup_prog = fluid.framework.Program()
+        with self.scope_prog_guard(
+                main_prog=main_prog, startup_prog=startup_prog):
+            data = fluid.layers.data(
+                name="words", shape=[1], dtype="int64", lod_level=1)
+            label = fluid.layers.data(name="label", shape=[1], dtype="int64")
+
+            avg_cost = model(data, label, len(self.word_dict))
+
+            optimizer = fluid.optimizer.Adagrad(
+                learning_rate=0.1,
+                regularization=paddle.regularizer.L2Decay(1.0))
+            optimizer.minimize(avg_cost)
+            param_sum = self.run_program(place, [data, label])
+        return param_sum
+
+    def check_l2decay(self, place, model):
+        paddle.manual_seed(1)
+        paddle.framework.random._manual_program_seed(1)
+        main_prog = fluid.framework.Program()
+        startup_prog = fluid.framework.Program()
+
+        with self.scope_prog_guard(
+                main_prog=main_prog, startup_prog=startup_prog):
+            data = fluid.layers.data(
+                name="words", shape=[1], dtype="int64", lod_level=1)
+            label = fluid.layers.data(name="label", shape=[1], dtype="int64")
+
+            avg_cost_l2 = model(data, label, len(self.word_dict))
+
+            param_list = fluid.default_main_program().block(0).all_parameters()
+            para_sum = []
+            for para in param_list:
+                para_mul = fluid.layers.square(x=para)
+                para_sum.append(fluid.layers.reduce_sum(input=para_mul))
+            avg_cost_l2 += fluid.layers.sums(para_sum) * .5
+
+            optimizer = fluid.optimizer.Adagrad(learning_rate=0.1)
+            optimizer.minimize(avg_cost_l2)
+            param_sum = self.run_program(place, [data, label])
+        return param_sum
+
+    def test_l2(self):
+        for place in self.get_places():
+            dense_sparse_p_sum = []
+            for sparse in [True, False]:
+                model = partial(bow_net, is_sparse=sparse)
+                framework_l2 = self.check_l2decay_regularizer(place, model)
+                l2 = self.check_l2decay(place, model)
+                assert len(l2) == len(framework_l2)
+                for i in range(len(l2)):
+                    assert np.isclose(a=framework_l2[i], b=l2[i], rtol=5e-5)
+                dense_sparse_p_sum.append(framework_l2)
+
+            assert len(dense_sparse_p_sum[0]) == len(dense_sparse_p_sum[1])
+            for i in range(len(dense_sparse_p_sum[0])):
+                assert np.isclose(
+                    a=dense_sparse_p_sum[0][i],
+                    b=dense_sparse_p_sum[1][i],
+                    rtol=5e-5)
+
+    def test_repeated_regularization(self):
+        l1 = paddle.regularizer.L1Decay(0.1)
+        l2 = paddle.regularizer.L2Decay(0.01)
+        fc_param_attr = fluid.ParamAttr(regularizer=l1)
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            x = fluid.layers.uniform_random([2, 2, 3])
+            out = fluid.layers.fc(x, 5, param_attr=fc_param_attr)
+            loss = fluid.layers.reduce_sum(out)
+            sgd = fluid.optimizer.SGD(learning_rate=0.1, regularization=l2)
+            sgd.minimize(loss)
+        with fluid.dygraph.guard():
+            input = fluid.dygraph.to_variable(
+                np.random.randn(3, 2).astype('float32'))
+            paddle.manual_seed(1)
+            paddle.framework.random._manual_program_seed(1)
+
+            linear1 = fluid.dygraph.Linear(
+                2, 2, param_attr=fc_param_attr, bias_attr=fc_param_attr)
+            linear2 = fluid.dygraph.Linear(
+                2, 2, param_attr=fc_param_attr, bias_attr=fc_param_attr)
+
+            loss1 = linear1(input)
+            loss1.backward()
+            # set l2 regularizer in optimizer, but l1 in fluid.ParamAttr
+
+            fluid.optimizer.SGD(parameter_list=linear1.parameters(),
+                                learning_rate=1e-2,
+                                regularization=l2).minimize(loss1)
+            # only set l1 in fluid.ParamAttr
+            loss2 = linear2(input)
+            loss2.backward()
+            fluid.optimizer.SGD(parameter_list=linear2.parameters(),
+                                learning_rate=1e-2).minimize(loss2)
+            # they should both be applied by l1, and keep the same
+            self.assertTrue(
+                np.allclose(linear1.weight.numpy(), linear2.weight.numpy()),
+                "weight should use the regularization in fluid.ParamAttr!")
+            self.assertTrue(
+                np.allclose(linear1.bias.numpy(), linear2.bias.numpy()),
+                "bias should use the regularization in fluid.ParamAttr!")
+
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -49,7 +49,10 @@ class LinearNet(nn.Layer):
         super(LinearNet, self).__init__()
         self._linear = nn.Linear(IMAGE_SIZE, CLASS_NUM)
 
-    @paddle.jit.to_static
+    @paddle.jit.to_static(input_spec=[
+        paddle.static.InputSpec(
+            shape=[None, IMAGE_SIZE], dtype='float32', name='x')
+    ])
     def forward(self, x):
         return self._linear(x)
 
@@ -152,6 +155,34 @@ class TestTranslatedLayer(unittest.TestCase):
         with self.assertRaises(ValueError):
             program = translated_layer.program('not_exists')
 
+    def test_get_input_spec(self):
+        # load
+        translated_layer = paddle.jit.load(self.model_path)
+
+        expect_spec = [
+            paddle.static.InputSpec(
+                shape=[None, IMAGE_SIZE], dtype='float32', name='x')
+        ]
+        actual_spec = translated_layer._input_spec()
+
+        for spec_x, spec_y in zip(expect_spec, actual_spec):
+            self.assertEqual(spec_x, spec_y)
+
+    def test_get_output_spec(self):
+        # load
+        translated_layer = paddle.jit.load(self.model_path)
+
+        expect_spec = [
+            paddle.static.InputSpec(
+                shape=[None, CLASS_NUM],
+                dtype='float32',
+                name='translated_layer/scale_0.tmp_1')
+        ]
+        actual_spec = translated_layer._output_spec()
+
+        for spec_x, spec_y in zip(expect_spec, actual_spec):
+            self.assertEqual(spec_x, spec_y)
+
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/white_list/check_shape_white_list.py

@@ -26,4 +26,5 @@ NEED_TO_FIX_OP_LIST = [
     'squared_l2_distance',
     'tree_conv',
     'cvm',
+    'cudnn_lstm',
 ]

python/paddle/regularizer.py

@@ -12,8 +12,134 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# TODO: define the regularizer functions 
-# __all__ = ['L1Decay',
-#            'L1DecayRegularizer',
-#            'L2Decay',
-#            'L2DecayRegularizer']
+__all__ = ['L1Decay', 'L2Decay']
+
+import paddle.fluid as fluid
+
+
+class L1Decay(fluid.regularizer.L1Decay):
+    """
+    Implement the L1 Weight Decay Regularization, which encourages the weights to be sparse.
+    
+    It can be set in :ref:`api_fluid_ParamAttr` or ``optimizer`` (such as :ref:`api_paddle_optimizer_Momentum` ). 
+    When set in ``ParamAttr`` , it only takes effect for trainable parameters in this layer. When set in 
+    ``optimizer`` , it takes effect for all trainable parameters. When set together, ``ParamAttr`` has 
+    higher priority than ``optimizer`` , which means that for a trainable parameter, if regularizer is defined 
+    in its ParamAttr, then the regularizer in Optimizer will be ignored. Otherwise the  regularizer
+    in Optimizer will be used.
+    
+    In the implementation, the formula of L1 Weight Decay Regularization is as follows:
+	
+    .. math::
+
+        L1WeightDecay = reg\_coeff * sign(parameter)
+
+    Args:
+        coeff(float, optional): regularization coeff. Default:0.0.
+	
+    Examples:
+        .. code-block:: python
+
+            # Example1: set Regularizer in optimizer
+            import paddle
+            from paddle.regularizer import L1Decay
+            import numpy as np
+            paddle.disable_static()
+            inp = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
+            linear = paddle.nn.Linear(10, 10)
+            inp = paddle.to_tensor(inp)
+            out = linear(inp)
+            loss = paddle.mean(out)
+            beta1 = paddle.to_tensor([0.9], dtype="float32")
+            beta2 = paddle.to_tensor([0.99], dtype="float32")
+            momentum = paddle.optimizer.Momentum(
+                learning_rate=0.1,
+                parameters=linear.parameters(),
+                weight_decay=L1Decay(0.0001))
+            back = out.backward()
+            momentum.step()
+            momentum.clear_grad()
+
+            # Example2: set Regularizer in parameters
+            # Set L1 regularization in parameters.
+            # Global regularizer does not take effect on my_conv2d for this case.
+            from paddle.nn import Conv2d
+            from paddle import ParamAttr
+            from paddle.regularizer import L2Decay
+
+            my_conv2d = Conv2d(
+                    in_channels=10,
+                    out_channels=10,
+                    kernel_size=1,
+                    stride=1,
+                    padding=0,
+                    weight_attr=ParamAttr(regularizer=L2Decay(coeff=0.01)),
+                    bias_attr=False)
+    """
+
+    def __init__(self, coeff=0.0):
+        super(L1Decay, self).__init__(coeff)
+
+
+class L2Decay(fluid.regularizer.L2Decay):
+    """
+    Implement the L2 Weight Decay Regularization, which helps to prevent the model over-fitting.
+    
+    It can be set in :ref:`api_fluid_ParamAttr` or ``optimizer`` (such as :ref:`api_paddle_optimizer_Momentum` ). 
+    When set in ``ParamAttr`` , it only takes effect for trainable parameters in this layer. When set in 
+    ``optimizer`` , it takes effect for all trainable parameters. When set together, ``ParamAttr`` has 
+    higher priority than ``optimizer`` , which means that for a trainable parameter, if regularizer is defined 
+    in its ParamAttr, then the regularizer in Optimizer will be ignored. Otherwise the  regularizer
+    in Optimizer will be used.
+    
+    In the implementation, the formula of L2 Weight Decay Regularization is as follows:
+
+    .. math::
+
+        L2WeightDecay = reg\_coeff * parameter
+
+    Args:
+        regularization_coeff(float, optional): regularization coeff. Default:0.0
+	
+    Examples:
+        .. code-block:: python
+
+            # Example1: set Regularizer in optimizer
+            import paddle
+            from paddle.regularizer import L2Decay
+            import numpy as np
+            paddle.disable_static()
+            inp = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
+            linear = paddle.nn.Linear(10, 10)
+            inp = paddle.to_tensor(inp)
+            out = linear(inp)
+            loss = paddle.mean(out)
+            beta1 = paddle.to_tensor([0.9], dtype="float32")
+            beta2 = paddle.to_tensor([0.99], dtype="float32")
+            momentum = paddle.optimizer.Momentum(
+                learning_rate=0.1,
+                parameters=linear.parameters(),
+                weight_decay=L2Decay(0.0001))
+            back = out.backward()
+            momentum.step()
+            momentum.clear_grad()
+
+            # Example2: set Regularizer in parameters
+            # Set L2 regularization in parameters.
+            # Global regularizer does not take effect on my_conv2d for this case.
+            from paddle.nn import Conv2d
+            from paddle import ParamAttr
+            from paddle.regularizer import L2Decay
+
+            my_conv2d = Conv2d(
+                    in_channels=10,
+                    out_channels=10,
+                    kernel_size=1,
+                    stride=1,
+                    padding=0,
+                    weight_attr=ParamAttr(regularizer=L2Decay(coeff=0.01)),
+                    bias_attr=False)
+    """
+
+    def __init__(self, coeff=0.0):
+        super(L2Decay, self).__init__(coeff)

python/paddle/tests/test_dist_hapi_model.py

@@ -37,7 +37,11 @@ def get_cluster_from_args(selected_gpus):
     free_ports = find_free_ports(len(selected_gpus))
     if free_ports is not None:
         free_ports = list(free_ports)
-    return get_cluster(node_ips, node_ip, free_ports, selected_gpus)
+
+    trainer_endpoints = []
+    for ip in node_ips:
+        trainer_endpoints.append(["%s:%d" % (ip, port) for port in free_ports])
+    return get_cluster(node_ips, node_ip, trainer_endpoints, selected_gpus)
 
 
 def get_gpus(selected_gpus):

python/paddle/utils/__init__.py

@@ -16,12 +16,13 @@ from .profiler import ProfilerOptions
 from .profiler import Profiler
 from .profiler import get_profiler
 from .deprecated import deprecated
+from ..fluid.framework import unique_name
+from ..fluid.framework import load_op_library
+from ..fluid.framework import require_version
 
 from . import download
 
 __all__ = ['dump_config', 'deprecated', 'download']
 
 #TODO: define new api under this directory
-# __all__ = ['unique_name',
-#            'load_op_library',
-#            'require_version']
+__all__ += ['unique_name', 'load_op_library', 'require_version']

tools/windows/build_compile_environment.bat

+:: Copyright (c) 2020 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.
+::
+:: ===============================
+:: Build Paddle compile enviroment
+:: ===============================
+:: Description:
+::   
+::   Install compile enviroment for xly CI.
+::
+::   Include:
+::     1. CMake 3.17.0
+::     2. Git 2.28.0
+::     3. Python 3.7.8
+::     4. Visual Studio 2015 with update 3
+::     5. CUDA 10 [miss cudnn]
+::     6. java jre [not complete]
+::     7. xly agent [not complete]
+
+:: Echo command is not required.
+@echo off
+
+:: ===== start step 0: wget tool =====
+:: Download wget for windows when there is not wget tool.
+echo ">>>>>>>> step [0/7]: wget tool"
+wget --help > nul 2> nul || call:install_wget
+goto cmake
+
+:install_wget
+echo There is not wget in this PC, will download wget 1.20.
+echo Download package from https://eternallybored.org/misc/wget/1.20/64/wget.exe ...
+certutil -urlcache -split -f https://eternallybored.org/misc/wget/1.20/64/wget.exe > nul 2> nul
+if %errorlevel% == 0 (
+  echo Download wget tool into %cd% success.
+) else (
+  echo Error***** Download wget tool failed, please download it before rerun.
+  exit /b 1
+) 
+goto :eof
+:: ===== end step 0: wget tool =====
+
+:: ===== start step 1: cmake =====
+:: Download CMake-3.17.0 and add in PATH when it not installed.
+:: TODO: limit version >= 3.17.0
+:cmake
+echo ">>>>>>>> step [1/7]: CMake 3.17.0"
+cmake --help > nul 2> nul || call :install_cmake
+goto git
+
+:install_cmake
+echo There is not cmake in this PC, will install cmake-3.17.0.
+echo Download package from https://cmake.org/files/v3.17/cmake-3.17.0-win64-x64.msi ...
+wget -O cmake-3.17.0-win64-x64.msi https://cmake.org/files/v3.17/cmake-3.17.0-win64-x64.msi
+echo Install cmake-3.17.0 ...
+:: /passive [silent installation]
+:: /norestart [do not restart]
+:: ADD_CMAKE_TO_PATH = System [add CMake to the system PATH for all users]
+start /wait cmake-3.17.0-win64-x64.msi /passive /norestart ADD_CMAKE_TO_PATH=System
+if %errorlevel% == 0 (
+  echo Install CMake-3.17.0 success!
+) else (
+  echo Error***** Install Cmake-3.17.0 failed, please re-install it manually.
+)
+del cmake-3.17.0-win64-x64.msi
+goto :eof
+:: ===== end step 1: cmake =====
+
+:: ===== start step 2: Git =====
+:: Download Git-2.28.0 and add in PATH when it not installed.
+:: TODO: limit version >= 2.28.0
+:git
+echo ">>>>>>>> step [2/8]: Git 2.28.0"
+git --help > nul 2> nul || call :install_git
+goto python
+
+:install_git
+echo There is not git in this PC, will install Git-2.28.0.
+echo Download package from https://github.com/git-for-windows/git/releases/download/v2.28.0.windows.1/Git-2.28.0-64-bit.exe ...
+wget -O Git-2.28.0-64-bit.exe https://github.com/git-for-windows/git/releases/download/v2.28.0.windows.1/Git-2.28.0-64-bit.exe
+echo Install Git-2.28.0 ...
+:: /SILENT [silent install]
+:: /ALLUSERS [add path for all users]
+:: /NORESTART [do not restart]
+start /wait Git-2.28.0-64-bit.exe /SILENT /ALLUSERS /NORESTART
+if %errorlevel% == 0 (
+  echo Install Git-2.28.0 success!
+) else (
+  echo Error***** Install Git-2.28.0 failed, please re-install it manually.
+)
+del Git-2.28.0-64-bit.exe
+goto :eof
+:: ===== end step 2: Git =====
+
+:: ===== start step 3: Python =====
+:: Download Python-3.7.8 and add in PATH when it not installed.
+:: TODO: limit version >= 3.7.8
+:python
+echo ">>>>>>>> step [3/7]: Python 3.7.8"
+python -V 2>&1 | findstr /C:"Python 3.7.8" > nul 2> nul || call :install_python
+goto vs2015
+
+:install_python
+echo There is not Python in this PC, will install Python-3.7.8.
+echo Download package from https://npm.taobao.org/mirrors/python/3.7.8/python-3.7.8-amd64.exe ...
+wget -O python-3.7.8-amd64.exe https://npm.taobao.org/mirrors/python/3.7.8/python-3.7.8-amd64.exe
+echo Install Python-3.7.8 ...
+:: /passive [silent install]
+:: InstallAllUsers [add path for all users]
+:: PrependPath [add script/install into PATH]
+:: TargetDir [install directory]
+start /wait python-3.7.8-amd64.exe /passive InstallAllUsers=1 PrependPath=1 TargetDir=C:\Python37
+if %errorlevel% == 0 (
+  echo Install python-3.7.8 success!
+) else (
+  echo Error***** Install python-3.7.8 failed, please re-install it manually.
+)
+del python-3.7.8-amd64.exe
+goto :eof
+:: ===== end step 3: Python =====
+
+:: ===== start step 4: Visual Studio 2015 =====
+:: Download Visual Studio 2015 when it not installed.
+:vs2015
+echo ">>>>>>>> step [4/7]: Visual Studio 2015"
+cmd /C "C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\vcvarsall.bat" amd64 > nul 2> nul || call :install_visual_studio
+goto :cuda10
+
+:install_visual_studio
+echo There is not Visual Studio in this PC, will install VS2015.
+echo Download package from "https://download.my.visualstudio.com/pr/en_visual_studio_professional_2015_with_update_3_x86_x64_web_installer_8922978.exe"
+wget -O vs_installer.exe "https://download.my.visualstudio.com/pr/en_visual_studio_professional_2015_with_update_3_x86_x64_web_installer_8922978.exe?t=9ee7a96d-ca80-4b84-af2c-7dd86996a0aa&e=1600103404&h=3cdea1e81c04aa4e846f5314972c46eb&su=1"
+echo Install Visual Studio 2015 ...
+:: /passive [silent install]
+:: /norestart [no restart]
+:: /NoRefresh [no refresh]
+:: /InstallSelectableItems NativeLanguageSupport_Group [select Visual C++ for installing]
+start /wait visual_installer.exe /passive /norestart /NoRefresh /InstallSelectableItems NativeLanguageSupport_Group
+if %errorlevel% == 0 (
+  echo Install Visual Studio 2015 success!
+) else (
+  echo Error***** Install Visual Studio 2015 failed, please re-install it manually.
+)
+del vs_installer.exe
+goto :eof
+:: ===== end step 4: Visual Studio 2015 =====
+
+:: ===== start step 5: CUDA 10 =====
+:cuda10
+echo ">>>>>>>> step [5/7]: CUDA 10.0"
+nvcc --version > nul 2> nul || call :install_cuda
+goto java-jre
+
+:install_cuda
+echo There is not CUDA in this PC, will install CUDA-10.0.
+echo Download package from "https://developer.download.nvidia.cn/compute/cuda/10.0/secure/Prod/network_installers/cuda_10.0.130_win10_network.exe"
+wget -O cuda_installer.exe "https://developer.download.nvidia.cn/compute/cuda/10.0/secure/Prod/network_installers/cuda_10.0.130_win10_network.exe?hG7oBtA2CnxZG7d39onmBdtzrIa2cOukrmW8I0qk3h36vb2Sj0yYGjMElJlxlNhjx8Xu5RlbmdBhCWvP2QcEqMjCoKCXe5lOgr5uIIso_7LqrotgQHbZRZSVBYRT4bIAHPVSPrr4_4KczKvI9Nf3mbO9RJ2Vj6ECD5QphRMJBus0KKNVxO1gsplVL5qaCnE"
+echo Install CUDA-10.0 ...
+:: -s [silent install]
+start /wait cuda_installer.exe -s
+if %errorlevel% == 0 (
+  echo Install CUDA-10.0 success!
+) else (
+  echo Error***** Install CUDA-10.0 failed, please re-install it manually.
+)
+del cuda_installer.exe
+goto :eof
+:: ===== end step 5: CUDA 10 =====
+
+:: ===== start step 6: java jre =====
+:java-jre
+echo ">>>>>>>> step [6/7]: java jre"
+goto xly-agent
+:: ===== end step 6: java jre =====
+
+:: ===== start step 7: xly agent =====
+:xly-agent
+echo ">>>>>>>> step [7/7]: xly agent"
+goto :eof
+:: ===== end step 8: xly agent =====
\ No newline at end of file