Merge pull request #13998 from tensor-tang/fea/fusion_seqconv_add

Fea/fusion seqconv eltadd relu

paddle/fluid/framework/ir/CMakeLists.txt

@@ -37,6 +37,7 @@ pass_library(embedding_fc_lstm_fuse_pass inference)
 pass_library(fc_gru_fuse_pass inference)
 pass_library(seq_concat_fc_fuse_pass inference)
 pass_library(conv_bn_fuse_pass inference)
+pass_library(seqconv_eltadd_relu_fuse_pass inference)
 if(WITH_MKLDNN)
     pass_library(mkldnn_placement_pass base)
     pass_library(conv_bias_mkldnn_fuse_pass inference)

paddle/fluid/framework/ir/graph_pattern_detector.cc

@@ -761,6 +761,51 @@ PDNode *patterns::ConvReLU::operator()(
   return relu_out_var;
 }
 
+PDNode *patterns::SeqConvEltAddRelu::operator()(
+    paddle::framework::ir::PDNode *seqconv_input) {
+  // Create Operators
+  seqconv_input->assert_is_op_input("sequence_conv", "X");
+  auto *seqconv_op = pattern->NewNode(seqconv_repr())
+                         ->assert_is_op("sequence_conv")
+                         ->assert_op_attr<bool>("paddingTrainable", false)
+                         ->assert_op_attr<int>("contextStride", 1);
+
+  auto *eltadd_op =
+      pattern->NewNode(eltadd_repr())->assert_is_op("elementwise_add");
+  auto *relu_op = pattern->NewNode(relu_repr())->assert_is_op("relu");
+  // Create variables
+  // Filter
+  auto *seqconv_weight_var =
+      pattern->NewNode(seqconv_weight_repr())
+          ->AsInput()
+          ->assert_is_persistable_var()
+          ->assert_is_op_input("sequence_conv", "Filter");
+  // Bias
+  auto *eltadd_bias_var = pattern->NewNode(eltadd_bias_repr())
+                              ->AsInput()
+                              ->assert_is_op_input("elementwise_add");
+  // intermediate variable, will be removed in the IR after fuse.
+  auto *seqconv_out_var = pattern->NewNode(seqconv_out_repr())
+                              ->AsIntermediate()
+                              ->assert_is_only_output_of_op("sequence_conv")
+                              ->assert_is_op_input("elementwise_add");
+  auto *eltadd_out_var = pattern->NewNode(eltadd_out_repr())
+                             ->AsIntermediate()
+                             ->assert_is_only_output_of_op("elementwise_add")
+                             ->assert_is_only_input_of_op("relu");
+  // output
+  auto *relu_out_var = pattern->NewNode(relu_out_repr())
+                           ->AsOutput()
+                           ->assert_is_op_output("relu");
+
+  seqconv_op->LinksFrom({seqconv_input, seqconv_weight_var})
+      .LinksTo({seqconv_out_var});
+  eltadd_op->LinksFrom({seqconv_out_var, eltadd_bias_var})
+      .LinksTo({eltadd_out_var});
+  relu_op->LinksFrom({eltadd_out_var}).LinksTo({relu_out_var});
+  return relu_out_var;
+}
+
 PDNode *patterns::FC::operator()(paddle::framework::ir::PDNode *x,
                                  bool with_bias) {
   // Create shared nodes.

paddle/fluid/framework/ir/graph_pattern_detector.h

@@ -128,6 +128,15 @@ struct PDNode {
       const std::unordered_set<std::string>& op_types,
       const std::string& argument, int nth);
 
+  template <typename T>
+  PDNode* assert_op_attr(const std::string& attr_name, const T& attr) {
+    asserts_.emplace_back([=](Node* x) {
+      return x && x->IsOp() && x->Op()->HasAttr(attr_name) &&
+             boost::get<T>(x->Op()->GetAttr(attr_name)) == attr;
+    });
+    return this;
+  }
+
  private:
   PDNode(PDPattern* pattern, const std::string& name = "",
          Type type = Type::kVar)
@@ -434,6 +443,31 @@ struct ConvReLU : public PatternBase {
   PATTERN_DECL_NODE(relu_out);
 };
 
+// SEQCONV with Elementwise_Add ReLU
+// op: seqconv + elementwise_add + relu
+// named nodes:
+// seqconv_input, seqconv_weight,
+// seqconv_out, seqconv,
+// elementwise_add_bias, elementwise_add_out, elementwise_add
+// relu_out, relu
+struct SeqConvEltAddRelu : public PatternBase {
+  SeqConvEltAddRelu(PDPattern* pattern, const std::string& name_scope)
+      : PatternBase(pattern, name_scope, "seqconv_eltadd_relu") {}
+
+  PDNode* operator()(PDNode* seqconv_input);
+
+  // declare operator node's name
+  PATTERN_DECL_NODE(seqconv);
+  PATTERN_DECL_NODE(eltadd);
+  PATTERN_DECL_NODE(relu);
+  // declare variable node's name
+  PATTERN_DECL_NODE(seqconv_weight);
+  PATTERN_DECL_NODE(seqconv_out);
+  PATTERN_DECL_NODE(eltadd_bias);
+  PATTERN_DECL_NODE(eltadd_out);
+  PATTERN_DECL_NODE(relu_out);
+};
+
 // FC with bias
 // op: mul + elementwise_add
 // named nodes:

paddle/fluid/framework/ir/seqconv_eltadd_relu_fuse_pass.cc

+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/fluid/framework/ir/seqconv_eltadd_relu_fuse_pass.h"
+#include <string>
+#include "paddle/fluid/framework/lod_tensor.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+int BuildFusion(Graph* graph, const std::string& name_scope, Scope* scope) {
+  GraphPatternDetector gpd;
+  auto* pattern = gpd.mutable_pattern();
+
+  PDNode* x = pattern->NewNode(patterns::PDNodeName(name_scope, "X"))
+                  ->assert_is_op_input("sequence_conv")
+                  ->assert_var_not_persistable();
+  patterns::SeqConvEltAddRelu fuse_pattern(pattern, name_scope);
+  fuse_pattern(x);
+
+  // Create New OpDesc
+  auto fuse_creator = [&](Node* seqconv, Node* input, Node* seqconv_weight,
+                          Node* eltadd_bias, Node* relu_out) {
+    OpDesc op_desc;
+    op_desc.SetType("fusion_seqconv_eltadd_relu");
+    op_desc.SetInput("X", {input->Name()});
+    op_desc.SetInput("Filter", {seqconv_weight->Name()});
+    op_desc.SetInput("Bias", {eltadd_bias->Name()});
+    op_desc.SetAttr("contextLength", seqconv->Op()->GetAttr("contextLength"));
+    op_desc.SetAttr("contextStart", seqconv->Op()->GetAttr("contextStart"));
+    op_desc.SetAttr("contextStride", seqconv->Op()->GetAttr("contextStride"));
+    PADDLE_ENFORCE(graph->Has(kParamScopeAttr));
+    auto* scope = graph->Get<Scope*>(kParamScopeAttr);
+    const std::string ColMat = patterns::UniqueKey("SeqConvColMat");
+    op_desc.SetOutput("ColMat", {ColMat});
+    op_desc.SetOutput("Out", {relu_out->Name()});
+    scope->Var(ColMat)->GetMutable<LoDTensor>();
+
+    auto* op = graph->CreateOpNode(&op_desc);
+    IR_NODE_LINK_TO(input, op);
+    IR_NODE_LINK_TO(seqconv_weight, op);
+    IR_NODE_LINK_TO(eltadd_bias, op);
+    IR_NODE_LINK_TO(op, relu_out);
+    return op;
+  };
+
+  int fusion_count{0};
+
+  auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
+                     Graph* g) {
+    VLOG(4) << "handle SeqConv EltAdd Relu fuse";
+    GET_IR_NODE_FROM_SUBGRAPH(seqconv, seqconv, fuse_pattern);
+    GET_IR_NODE_FROM_SUBGRAPH(seqconv_weight, seqconv_weight, fuse_pattern);
+    GET_IR_NODE_FROM_SUBGRAPH(seqconv_out, seqconv_out, fuse_pattern);
+    GET_IR_NODE_FROM_SUBGRAPH(eltadd, eltadd, fuse_pattern);
+    GET_IR_NODE_FROM_SUBGRAPH(eltadd_bias, eltadd_bias, fuse_pattern);
+    GET_IR_NODE_FROM_SUBGRAPH(eltadd_out, eltadd_out, fuse_pattern);
+    GET_IR_NODE_FROM_SUBGRAPH(relu, relu, fuse_pattern);
+    GET_IR_NODE_FROM_SUBGRAPH(relu_out, relu_out, fuse_pattern);
+
+    fuse_creator(seqconv, subgraph.at(x), seqconv_weight, eltadd_bias,
+                 relu_out);
+    std::unordered_set<const Node*> marked_nodes(
+        {seqconv, seqconv_out, eltadd, eltadd_out, relu});
+    GraphSafeRemoveNodes(graph, marked_nodes);
+    ++fusion_count;
+  };
+
+  gpd(graph, handler);
+
+  return fusion_count;
+}
+
+std::unique_ptr<ir::Graph> SeqConvEltAddReluFusePass::ApplyImpl(
+    std::unique_ptr<ir::Graph> graph) const {
+  FusePassBase::Init(name_scope_, graph.get());
+
+  int fusion_count = BuildFusion(graph.get(), name_scope_, param_scope());
+  AddStatis(fusion_count);
+
+  return graph;
+}
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+REGISTER_PASS(seqconv_eltadd_relu_fuse_pass,
+              paddle::framework::ir::SeqConvEltAddReluFusePass);

paddle/fluid/framework/ir/seqconv_eltadd_relu_fuse_pass.h

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

paddle/fluid/inference/analysis/analyzer.h

@@ -69,6 +69,7 @@ class Analyzer : public OrderedRegistry<PassManager> {
       // Manual update the passes here.
       "infer_clean_graph_pass",         //
       "attention_lstm_fuse_pass",       //
+      "seqconv_eltadd_relu_fuse_pass",  //
       "embedding_fc_lstm_fuse_pass",    //
       "fc_lstm_fuse_pass",              //
       "mul_lstm_fuse_pass",             //

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

@@ -183,7 +183,13 @@ TEST(Analyzer_seq_conv1, fuse_statis) {
   SetConfig(&cfg);
   int num_ops;
   auto predictor = CreatePaddlePredictor<AnalysisConfig>(cfg);
-  GetFuseStatis(predictor.get(), &num_ops);
+
+  auto fuse_statis = GetFuseStatis(predictor.get(), &num_ops);
+  ASSERT_TRUE(fuse_statis.count("fc_fuse"));
+  ASSERT_TRUE(fuse_statis.count("seqconv_eltadd_relu_fuse"));
+  EXPECT_EQ(fuse_statis.at("fc_fuse"), 2);
+  EXPECT_EQ(fuse_statis.at("seqconv_eltadd_relu_fuse"), 6);
+  EXPECT_EQ(num_ops, 32);
 }
 
 // Compare result of NativeConfig and AnalysisConfig

paddle/fluid/operators/CMakeLists.txt

@@ -86,7 +86,7 @@ function(op_library TARGET)
     # remove windows unsupported op, because windows has no nccl, no warpctc such ops.
     foreach(windows_unsupport_op "nccl_op" "gen_nccl_id_op" "warpctc_op" "hierarchical_sigmoid_op"
      "crf_decoding_op" "select_op" "lstmp_op" "gru_op" "fusion_gru_op" "lstm_op" "fusion_lstm_op" "cumsum_op"
-     "channel_send_op" "channel_create_op" "channel_close_op" "channel_recv_op")
+      "fusion_seqconv_eltadd_relu_op" "channel_send_op" "channel_create_op" "channel_close_op" "channel_recv_op")
         if ("${TARGET}" STREQUAL "${windows_unsupport_op}")
           return()
         endif()

paddle/fluid/operators/fusion_seqconv_eltadd_relu_op.cc

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/fluid/operators/fusion_seqconv_eltadd_relu_op.h"
+#include <algorithm>  // for min, max
+#include <string>
+#include "paddle/fluid/operators/math/blas.h"
+#include "paddle/fluid/operators/math/fc_compute.h"
+
+namespace paddle {
+namespace operators {
+
+void FusionSeqConvEltAddReluOp::InferShape(
+    framework::InferShapeContext* ctx) const {
+  PADDLE_ENFORCE(ctx->HasInput("X"),
+                 "Input(X) of FusionSeqConvEltAddReluOp should not be null.");
+  PADDLE_ENFORCE(
+      ctx->HasInput("Filter"),
+      "Input(Filter) of FusionSeqConvEltAddReluOp should not be null.");
+  PADDLE_ENFORCE(
+      ctx->HasInput("Bias"),
+      "Input(Bias) of FusionSeqConvEltAddReluOp should not be null.");
+  PADDLE_ENFORCE(
+      ctx->HasOutput("Out"),
+      "Output(Out) of FusionSeqConvEltAddReluOp should not be null.");
+  PADDLE_ENFORCE(
+      ctx->HasOutput("ColMat"),
+      "Output(ColMat) of FusionSeqConvEltAddReluOp should not be null.");
+
+  auto x_dims = ctx->GetInputDim("X");
+  auto w_dims = ctx->GetInputDim("Filter");
+  int context_length = ctx->Attrs().Get<int>("contextLength");
+  PADDLE_ENFORCE(
+      ctx->Attrs().Get<int>("contextStride") == 1,
+      "Currently, FusionSeqConvEltAddReluOp only supports contextStride=1.");
+  PADDLE_ENFORCE(x_dims.size() == 2 && w_dims.size() == 2,
+                 "Input(X, Filter) should be 2-D tensor.");
+  PADDLE_ENFORCE(x_dims.size() == 2 && w_dims.size() == 2,
+                 "Input(X, Filter) should be 2-D tensor.");
+  PADDLE_ENFORCE(w_dims[0] == context_length * x_dims[1],
+                 "Filter's height should be context_length * "
+                 "input_hidden_size .");
+  PADDLE_ENFORCE_GT(context_length + ctx->Attrs().Get<int>("contextStart"), 0,
+                    "contextStart size should be smaller than contextLength.");
+
+  ctx->SetOutputDim("Out", {x_dims[0], w_dims[1]});
+  ctx->SetOutputDim("ColMat", {x_dims[0], w_dims[0]});
+  ctx->ShareLoD("X", "Out");
+}
+
+framework::OpKernelType FusionSeqConvEltAddReluOp::GetExpectedKernelType(
+    const framework::ExecutionContext& ctx) const {
+  return framework::OpKernelType(
+      framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
+      ctx.device_context());
+}
+
+void FusionSeqConvEltAddReluOpMaker::Make() {
+  AddInput("X",
+           "(LoDTensor) the input is a LodTensor, which support "
+           "variable-time length input sequence. The underlying tensor in "
+           "this LoDTensor is a matrix with shape (T X M), where T is the "
+           "total time steps in this mini-batch, M is the dim size of x.");
+  // PaddingData only support false yet, should be ensured at pass.
+  AddInput("Filter",
+           "(Tensor) same as the input(Filter) of sequence conv op is an "
+           "learnable parameter."
+           "This is a tensor with shape (K, N), where K is the "
+           "context_length * dim size of x, N is the output feature size.");
+  AddInput("Bias",
+           "(Tensor) the learnable weights. shape (1, N), where N is the "
+           "output feature size");
+  AddOutput(
+      "Out",
+      "(LoDTensor) the output(Out) is a LodTensor, which support "
+      "variable-time length output sequence. The underlying tensor in "
+      "this LoDTensor is a matrix with shape (T, N), where, T is the "
+      "total time steps in this mini-batch, N is the output feature size.");
+  AddOutput("ColMat",
+            "(Tensor) (T, K), where T is where T is the "
+            "total time steps in this mini-batch, K is height of Filter")
+      .AsIntermediate();
+  AddAttr<int>("contextLength",
+               "(int) the contextLength of FusionSeqConvEltAddReluOp is the "
+               "height of the convolution kernel.")
+      .GreaterThan(0);
+  AddAttr<int>("contextStart",
+               "(int, default:0) the contextStart of FusionSeqConvEltAddReluOp "
+               "represents the beginning of the convolution of the number of "
+               "rows of sequence, which can be negative. The negative number "
+               "means to pad contextStart time-steps of zeros or learnable "
+               "parameters at the beginning of each instance. The positive "
+               "number means to skip contextStart time-steps of each "
+               "instance.")
+      .SetDefault(0);
+  AddAttr<int>(
+      "contextStride",
+      "(int, default:1) the contextStride of FusionSeqConvEltAddReluOp "
+      "represents the stride length of convolution kernel. "
+      "Currently, FusionSeqConvEltAddReluOp only supports"
+      "contextStride=1.")
+      .SetDefault(1)
+      .GreaterThan(0);
+  AddComment(R"DOC(
+Fusion Sequence Conv and ElementwiseAdd Operator.
+)DOC");
+}
+
+template <typename T>
+class FusionSeqConvEltAddReluKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    using DeviceContext = paddle::platform::CPUDeviceContext;
+    auto* x = ctx.Input<LoDTensor>("X");
+    auto* w = ctx.Input<Tensor>("Filter");
+    auto* b = ctx.Input<Tensor>("Bias");
+    auto* y = ctx.Output<LoDTensor>("Out");
+    auto* col = ctx.Output<Tensor>("ColMat");
+
+    auto x_lod = x->lod();
+    auto x_dims = x->dims();
+    auto w_dims = w->dims();
+    PADDLE_ENFORCE_EQ(b->numel(), w_dims[1],
+                      "bias size should be equal to output feature size.");
+    PADDLE_ENFORCE_EQ(x_lod.size(), 1UL,
+                      "Only support one level sequence now.");
+
+    const T* x_data = x->data<T>();
+    const T* w_data = w->data<T>();
+    const T* b_data = b->data<T>();
+    T* y_data = y->mutable_data<T>(ctx.GetPlace());
+    T* col_data = col->mutable_data<T>(ctx.GetPlace());
+
+    int context_start = ctx.Attr<int>("contextStart");
+    int context_length = ctx.Attr<int>("contextLength");
+    int up_pad = std::max(0, -context_start);
+    int down_pad = std::max(0, context_start + context_length - 1);
+    // im2col
+    int src_mat_w = static_cast<int>(x_dims[1]);
+    int src_mat_w_sz = src_mat_w * sizeof(T);
+    int col_mat_w = static_cast<int>(w_dims[0]);
+    int col_mat_w_sz = col_mat_w * sizeof(T);
+    for (int i = 0; i < static_cast<int>(x_lod[0].size()) - 1; ++i) {
+      int st = x_lod[0][i];
+      int ed = x_lod[0][i + 1];
+      const T* src_data = x_data + st * src_mat_w;
+      T* dst_data = col_data + st * col_mat_w;
+      int seq_len = ed - st;
+      if (seq_len > up_pad + down_pad) {
+        // zero all up_pad and fill data
+        std::memset(dst_data, 0, up_pad * col_mat_w_sz);
+        dst_data = dst_data + up_pad * src_mat_w;
+        int copy_size = col_mat_w_sz - up_pad * src_mat_w_sz;
+        for (int j = 0; j < up_pad; ++j) {
+          // blas.VCOPY?
+          std::memcpy(dst_data, src_data, copy_size);
+          dst_data += (col_mat_w - src_mat_w);
+          copy_size += src_mat_w_sz;
+        }
+        // fill data
+        for (int j = 0; j < seq_len - up_pad - down_pad; ++j) {
+          std::memcpy(dst_data, src_data, copy_size);
+          dst_data += col_mat_w;
+          src_data += src_mat_w;
+        }
+        // zero all down_pad and fill data
+        std::memset(dst_data, 0, down_pad * col_mat_w_sz);
+        copy_size -= src_mat_w_sz;
+        for (int j = 0; j < down_pad; ++j) {
+          std::memcpy(dst_data, src_data, copy_size);
+          dst_data += col_mat_w;
+          src_data += src_mat_w;
+          copy_size -= src_mat_w_sz;
+        }
+      } else {
+        PADDLE_ENFORCE_GE(context_length, up_pad + down_pad + 1);
+        std::memset(dst_data, 0, seq_len * col_mat_w_sz);
+        dst_data = dst_data + up_pad * src_mat_w;
+        int zero_sz = up_pad * src_mat_w_sz;
+        int cur_src_sz = seq_len * src_mat_w_sz;
+        for (int j = 0; j < std::min(up_pad, seq_len); ++j) {
+          int copy_size = std::min(cur_src_sz, col_mat_w_sz - zero_sz);
+          std::memcpy(dst_data, src_data, copy_size);
+          dst_data += (col_mat_w - src_mat_w);
+          zero_sz -= src_mat_w_sz;
+        }
+        // from bottom
+        dst_data = col_data + ed * col_mat_w;
+        src_data = x_data + st * src_mat_w;
+        zero_sz = down_pad * src_mat_w_sz;
+        for (int j = 1; j <= std::min(down_pad, seq_len); ++j) {
+          int copy_size = std::min(cur_src_sz, col_mat_w_sz - zero_sz);
+          std::memcpy(dst_data - (zero_sz + copy_size) / sizeof(T),
+                      src_data + std::max(seq_len - j - up_pad, 0) * src_mat_w,
+                      copy_size);
+          dst_data -= col_mat_w;
+          zero_sz -= src_mat_w_sz;
+        }
+      }
+    }
+    auto& dev_ctx = ctx.template device_context<DeviceContext>();
+    auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);
+    math::FCCompute<DeviceContext, T>(blas, x_dims[0], w_dims[1], w_dims[0],
+                                      col_data, w_data, y_data, b_data, true);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(fusion_seqconv_eltadd_relu, ops::FusionSeqConvEltAddReluOp,
+                  ops::FusionSeqConvEltAddReluOpMaker,
+                  paddle::framework::DefaultGradOpDescMaker<true>);
+
+REGISTER_OP_CPU_KERNEL(fusion_seqconv_eltadd_relu,
+                       ops::FusionSeqConvEltAddReluKernel<float>,
+                       ops::FusionSeqConvEltAddReluKernel<double>);

paddle/fluid/operators/fusion_seqconv_eltadd_relu_op.h

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+using LoDTensor = framework::LoDTensor;
+using Tensor = framework::Tensor;
+
+class FusionSeqConvEltAddReluOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override;
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override;
+};
+
+class FusionSeqConvEltAddReluOpMaker
+    : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override;
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/math/fc_compute.h

@@ -15,6 +15,7 @@ limitations under the License. */
 #pragma once
 
 #include "paddle/fluid/operators/math/blas.h"
+#include "paddle/fluid/operators/math/jit_kernel.h"
 
 DECLARE_int32(paddle_num_threads);
 
@@ -30,20 +31,25 @@ inline void FCCompute(const BlasT<DeviceContext, T>& blas, const int M,
   if (B == NULL) {
     return;
   }
+  if (relu) {
+    const auto& vaddrelu = jitkernel::KernelPool::Instance()
+                               .template Get<jitkernel::VAddReluKernel<T>>(N);
+    for (int i = 0; i < M; i++) {
+      T* dst = Y + i * N;
+      vaddrelu->Compute(B, dst, dst);
+    }
+  } else {
+    const auto& vadd = jitkernel::KernelPool::Instance()
+                           .template Get<jitkernel::VAddKernel<T>>(N);
 
 #ifdef PADDLE_WITH_MKLML
 #pragma omp parallel for if (FLAGS_paddle_num_threads > 1)
 #endif
     for (int i = 0; i < M; i++) {
-    blas.AXPY(N, static_cast<T>(1), B, Y + i * N);
+      T* dst = Y + i * N;
+      vadd->Compute(B, dst, dst);
     }
-
-  if (!relu) {
-    return;
   }
-
-  // TODO(TJ): fuse relu
-  LOG(FATAL) << "Not implemented!";
 }
 
 }  // namespace math

paddle/fluid/operators/math/jit_kernel.h

@@ -86,6 +86,12 @@ class VAddBiasKernel : public Kernel {
   virtual void Compute(const T a, const T *x, T *y) const = 0;
 };
 
+template <typename T>
+class VAddReluKernel : public Kernel {
+ public:
+  virtual void Compute(const T *x, const T *y, T *z) const = 0;
+};
+
 template <typename T>
 class VActKernel : public Kernel {
  public:

paddle/fluid/operators/math/jit_kernel_blas.cc

@@ -378,11 +378,99 @@ class VIdentityKernelImpl : public VIdentityKernel<T> {
   void Compute(const T* x, T* y) const override {}
 };
 
+/* VAddRelu JitKernel */
+template <typename T, platform::jit::cpu_isa_t isa, jit_block>
+class VAddReluKernelImpl : public VAddReluKernel<T> {
+ public:
+  explicit VAddReluKernelImpl(int d) : VAddReluKernel<T>() { this->num_ = d; }
+  void Compute(const T* x, const T* y, T* z) const override {
+    for (int i = 0; i < this->num_; ++i) {
+      z[i] = x[i] + y[i];
+      z[i] = z[i] > 0 ? z[i] : 0;
+    }
+  }
+};
+
+#define INTRI8_FLOAT(isa)                               \
+  template <>                                           \
+  void VAddReluKernelImpl<float, isa, kEQ8>::Compute(   \
+      const float* x, const float* y, float* z) const { \
+    __m256 tmpx = _mm256_loadu_ps(x);                   \
+    __m256 tmpy = _mm256_loadu_ps(y);                   \
+    tmpy = _mm256_add_ps(tmpx, tmpy);                   \
+    tmpy = _mm256_max_ps(tmpy, _mm256_setzero_ps());    \
+    _mm256_storeu_ps(z, tmpy);                          \
+  }
+
+#define INTRI16_FLOAT(isa)                              \
+  template <>                                           \
+  void VAddReluKernelImpl<float, isa, kEQ16>::Compute(  \
+      const float* x, const float* y, float* z) const { \
+    __m256 zeros = _mm256_setzero_ps();                 \
+    __m256 tmp0 = _mm256_loadu_ps(x);                   \
+    __m256 tmp1 = _mm256_loadu_ps(y);                   \
+    tmp0 = _mm256_add_ps(tmp0, tmp1);                   \
+    tmp0 = _mm256_max_ps(tmp0, zeros);                  \
+    tmp1 = _mm256_loadu_ps(x + 8);                      \
+    __m256 tmp2 = _mm256_loadu_ps(y + 8);               \
+    tmp1 = _mm256_add_ps(tmp1, tmp2);                   \
+    tmp1 = _mm256_max_ps(tmp1, zeros);                  \
+    _mm256_storeu_ps(z, tmp0);                          \
+    _mm256_storeu_ps(z + 8, tmp1);                      \
+  }
+
+#define INTRI_COMMON_FLOAT(isa, block)                             \
+  template <>                                                      \
+  VAddReluKernelImpl<float, isa, block>::VAddReluKernelImpl(int d) \
+      : VAddReluKernel<float>() {                                  \
+    this->num_ = d;                                                \
+    this->end_ = d - d % AVX_FLOAT_BLOCK;                          \
+    this->rest_ = d - this->end_;                                  \
+  }                                                                \
+  template <>                                                      \
+  void VAddReluKernelImpl<float, isa, block>::Compute(             \
+      const float* x, const float* y, float* z) const {            \
+    __m256 zeros = _mm256_setzero_ps();                            \
+    for (int i = 0; i < this->end_; i += AVX_FLOAT_BLOCK) {        \
+      __m256 tmpx = _mm256_loadu_ps(x + i);                        \
+      __m256 tmpy = _mm256_loadu_ps(y + i);                        \
+      tmpy = _mm256_add_ps(tmpx, tmpy);                            \
+      tmpy = _mm256_max_ps(tmpy, zeros);                           \
+      _mm256_storeu_ps(z + i, tmpy);                               \
+    }                                                              \
+    for (int i = this->end_; i < this->num_; ++i) {                \
+      z[i] = x[i] + y[i];                                          \
+      z[i] = z[i] > 0 ? z[i] : 0;                                  \
+    }                                                              \
+  }
+
+#ifdef __AVX__
+INTRI8_FLOAT(jit::avx);
+INTRI16_FLOAT(jit::avx);
+INTRI_COMMON_FLOAT(jit::avx, kGT16);
+#endif
+#ifdef __AVX2__
+INTRI8_FLOAT(jit::avx2);
+INTRI16_FLOAT(jit::avx2);
+INTRI_COMMON_FLOAT(jit::avx2, kGT16);
+#endif
+#ifdef __AVX512F__
+// TODO(TJ): refine avx512
+INTRI8_FLOAT(jit::avx512f);
+INTRI16_FLOAT(jit::avx512f);
+INTRI_COMMON_FLOAT(jit::avx512f, kGT16);
+#endif
+
+#undef INTRI8_FLOAT
+#undef INTRI16_FLOAT
+#undef INTRI_COMMON_FLOAT
+
 REGISTER_JITKERNEL(vmul, VMulKernel);
 REGISTER_JITKERNEL(vadd, VAddKernel);
 REGISTER_JITKERNEL(vscal, VScalKernel);
 REGISTER_JITKERNEL(vaddb, VAddBiasKernel);
 REGISTER_JITKERNEL(vrelu, VReluKernel);
+REGISTER_JITKERNEL(vaddrelu, VAddReluKernel);
 REGISTER_JITKERNEL(videntity, VIdentityKernel);
 
 }  // namespace jitkernel

paddle/fluid/operators/math/jit_kernel_test.cc

@@ -712,6 +712,63 @@ TEST(JitKernel, vadd) {
   }
 }
 
+void vaddrelu_ref(const int n, const float* x, const float* y, float* z) {
+  for (int i = 0; i < n; ++i) {
+    z[i] = x[i] + y[i];
+    z[i] = z[i] > 0 ? z[i] : 0;
+  }
+}
+void vaddrelu_better(
+    const std::shared_ptr<
+        const paddle::operators::math::jitkernel::VAddKernel<float>>& vadd,
+    const std::shared_ptr<
+        const paddle::operators::math::jitkernel::VReluKernel<float>>& vrelu,
+    const float* x, const float* y, float* z) {
+  vadd->Compute(x, y, z);
+  vrelu->Compute(z, z);
+}
+
+TEST(JitKernel, vaddrelu) {
+  namespace jit = paddle::operators::math::jitkernel;
+  for (int d : {7, 8, 15, 16, 30, 256, 512}) {
+    std::vector<float> x(d), y(d);
+    std::vector<float> zref(d), ztgt(d);
+    RandomVec<float>(d, x.data());
+    RandomVec<float>(d, y.data());
+    const auto& ker =
+        jit::KernelPool::Instance().template Get<jit::VAddReluKernel<float>>(d);
+    const auto& vadd =
+        jit::KernelPool::Instance().template Get<jit::VAddKernel<float>>(d);
+    const auto& vrelu =
+        jit::KernelPool::Instance().template Get<jit::VReluKernel<float>>(d);
+    const float* x_data = x.data();
+    const float* y_data = y.data();
+    float* ztgt_data = ztgt.data();
+    float* zref_data = zref.data();
+    auto trefs = GetCurrentUS();
+    for (int i = 0; i < repeat; ++i) {
+      vadd_ref(d, x_data, y_data, zref_data);
+    }
+    auto trefe = GetCurrentUS();
+    auto tmkls = GetCurrentUS();
+    for (int i = 0; i < repeat; ++i) {
+      vaddrelu_better(vadd, vrelu, x_data, y_data, zref_data);
+    }
+    auto tmkle = GetCurrentUS();
+    auto ttgts = GetCurrentUS();
+    for (int i = 0; i < repeat; ++i) {
+      ker->Compute(x_data, y_data, ztgt_data);
+    }
+    auto ttgte = GetCurrentUS();
+    VLOG(3) << "Vec size " << d << ": refer takes: " << (trefe - trefs) / repeat
+            << " us, better takes: " << (tmkle - tmkls) / repeat << " us, "
+            << "tgt takes: " << (ttgte - ttgts) / repeat;
+    for (int i = 0; i < d; ++i) {
+      EXPECT_NEAR(ztgt_data[i], zref_data[i], 1e-3);
+    }
+  }
+}
+
 TEST(JitKernel, pool) {
   namespace jit = paddle::operators::math::jitkernel;
   const int frame_size = 4;

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

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+import random
+from op_test import OpTest
+from test_seq_conv import seqconv
+
+
+class TestSeqConvEltAddRelu(OpTest):
+    def set_conf(self):
+        pass
+
+    def setUp(self):
+        self.op_type = 'fusion_seqconv_eltadd_relu'
+        self.lod = [[6, 4]]
+        self.in_fea_size = 16
+        self.out_fea_size = 8
+        self.context_length = 4
+        self.context_stride = 1
+        self.context_start = 0
+        self.set_conf()
+
+        assert self.context_stride == 1
+
+        T = sum(self.lod[0])
+        x = np.random.uniform(-1, 1, [T, self.in_fea_size]).astype('float32')
+        w = np.random.uniform(
+            -1, 1, [self.in_fea_size * self.context_length,
+                    self.out_fea_size]).astype('float32')
+        b = np.random.uniform(-2, 1, [1, self.out_fea_size]).astype('float32')
+        out = seqconv(x, self.lod, w, self.context_length, self.context_start)
+        out = np.maximum(out + b, 0)
+
+        self.inputs = {'X': (x, self.lod), 'Filter': w, 'Bias': b}
+        self.attrs = {
+            'contextStart': self.context_start,
+            'contextLength': self.context_length,
+            'contextStride': self.context_stride
+        }
+        self.outputs = {'Out': out}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestSeqConvEltAddReluBS1(TestSeqConvEltAddRelu):
+    def set_conf(self):
+        self.lod = [[10]]
+
+
+class TestSeqConvEltAddReluBS1Case2(TestSeqConvEltAddRelu):
+    def set_conf(self):
+        self.lod = [[2]]
+
+
+class TestSeqConvEltAddReluCase1(TestSeqConvEltAddRelu):
+    def set_conf(self):
+        self.lod = [[3, 5, 1, 6]]
+        self.context_length = 3
+        self.context_start = -2
+
+
+class TestSeqConvEltAddReluCase2(TestSeqConvEltAddRelu):
+    def set_conf(self):
+        self.lod = [[10, 1, 2, 4, 1, 5, 6]]
+        self.in_fea_size = 2
+        self.context_length = 4
+        self.context_start = -1
+
+
+class TestSeqConvEltAddReluCase3(TestSeqConvEltAddRelu):
+    def set_conf(self):
+        self.lod = [[10, 1, 2, 4, 1, 5, 6]]
+        self.context_length = 5
+        self.context_start = -4
+
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -20,6 +20,53 @@ import random
 from op_test import OpTest
 
 
+def seqconv(x,
+            lod,
+            filter,
+            context_length,
+            context_start,
+            padding_trainable=False,
+            padding_data=None):
+    [T, M] = x.shape
+    col = np.zeros((T, context_length * M)).astype('float32')
+    offset = [0]
+    for seq_len in lod[0]:
+        offset.append(offset[-1] + seq_len)
+    begin_pad = np.max([0, -context_start])
+    for i in range(len(offset) - 1):
+        for j in range(context_length):
+            in_begin = offset[i] + context_start + j
+            in_end = offset[i + 1] + context_start + j
+            out_begin = offset[i]
+            out_end = offset[i + 1]
+            if in_begin < offset[i]:
+                pad_size = np.min(
+                    [offset[i] - in_begin, offset[i + 1] - offset[i]])
+                if padding_trainable:
+                    sub_w = padding_data[j:j + pad_size, :]
+                    col[offset[i]:offset[i] + pad_size, j * M:(j + 1) *
+                        M] = sub_w
+                out_begin = offset[i] + pad_size
+                in_begin = offset[i]
+
+            if in_end > offset[i + 1]:
+                pad_size = np.min(
+                    [in_end - offset[i + 1], offset[i + 1] - offset[i]])
+                if padding_trainable:
+                    sub_w = padding_data[begin_pad + context_start + j -
+                                         pad_size:begin_pad + context_start +
+                                         j, :]
+                    col[offset[i + 1] - pad_size:offset[i + 1], j * M:(j + 1) *
+                        M] = sub_w
+                in_end = offset[i + 1]
+                out_end = offset[i + 1] - pad_size
+            if in_end <= in_begin:
+                continue
+            in_sub = x[in_begin:in_end, :]
+            col[out_begin:out_end, j * M:(j + 1) * M] += in_sub
+    return np.dot(col, filter)
+
+
 class TestSeqProject(OpTest):
     def setUp(self):
         self.init_test_case()
@@ -66,57 +113,9 @@ class TestSeqProject(OpTest):
             'paddingTrainable': self.padding_trainable,
             'contextStride': self.context_stride
         }
-        out = np.zeros(
-            (self.input_size[0], self.output_represention)).astype('float32')
+        out = seqconv(x, self.lod, w, self.context_length, self.context_start,
+                      self.padding_trainable, self.pad_data)
         self.outputs = {'Out': out}
-        self.compute()
-
-    def compute(self):
-        x, lod = self.inputs['X']
-        filter = self.inputs['Filter']
-        pading_data = self.pad_data
-        out = np.zeros((self.input_size[0], self.context_length *
-                        self.input_size[1])).astype('float32')
-        offset = [0]
-        for seq_len in lod[0]:
-            offset.append(offset[-1] + seq_len)
-        begin_pad = np.max([0, -self.context_start])
-
-        for i in range(len(offset) - 1):
-            for j in range(self.context_length):
-                in_begin = offset[i] + self.context_start + j
-                in_end = offset[i + 1] + self.context_start + j
-                out_begin = offset[i]
-                out_end = offset[i + 1]
-                if in_begin < offset[i]:
-                    pad_size = np.min(
-                        [offset[i] - in_begin, offset[i + 1] - offset[i]])
-                    if self.padding_trainable:
-                        sub_w = pading_data[j:j + pad_size, :]
-                        out[offset[i]:offset[i] + pad_size, j * self.input_size[
-                            1]:(j + 1) * self.input_size[1]] = sub_w
-                    out_begin = offset[i] + pad_size
-                    in_begin = offset[i]
-
-                if in_end > offset[i + 1]:
-                    pad_size = np.min(
-                        [in_end - offset[i + 1], offset[i + 1] - offset[i]])
-                    if self.padding_trainable:
-                        sub_w = pading_data[begin_pad + self.context_start + j -
-                                            pad_size:begin_pad +
-                                            self.context_start + j, :]
-                        out[offset[i + 1] - pad_size:offset[i + 1], j * self.
-                            input_size[1]:(j + 1) * self.input_size[1]] = sub_w
-                    in_end = offset[i + 1]
-                    out_end = offset[i + 1] - pad_size
-                if in_end <= in_begin:
-                    continue
-
-                in_sub = x[in_begin:in_end, :]
-                out[out_begin:out_end, j * self.input_size[1]:(j + 1) *
-                    self.input_size[1]] += in_sub
-
-        np.dot(out, filter, out=self.outputs['Out'])
 
     def test_check_output(self):
         self.check_output()