Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into add-async-ssa-graph-executor
Qiao Longfei
6 years ago
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e70b1727ef
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// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "paddle/fluid/framework/ir/mkldnn/mkldnn_placement_pass.h"
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#include <gtest/gtest.h>
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#include <boost/logic/tribool.hpp>
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namespace paddle {
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namespace framework {
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namespace ir {
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void SetOp(ProgramDesc* prog, const std::string& type, const std::string& name,
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const std::vector<std::string>& inputs,
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const std::vector<std::string>& outputs, boost::tribool use_mkldnn) {
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auto* op = prog->MutableBlock(0)->AppendOp();
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op->SetType(type);
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if (!boost::indeterminate(use_mkldnn)) op->SetAttr("use_mkldnn", use_mkldnn);
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if (type == "conv2d") {
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op->SetAttr("name", name);
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op->SetInput("Input", {inputs[0]});
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op->SetInput("Filter", {inputs[1]});
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op->SetInput("Bias", {inputs[2]});
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} else if (type == "relu") {
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op->SetInput("X", inputs);
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} else if (type == "concat") {
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op->SetAttr("axis", 1);
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op->SetInput("X", {inputs[0], inputs[1]});
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} else if (type == "pool2d") {
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op->SetInput("X", {inputs[0]});
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} else {
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FAIL() << "Unexpected operator type.";
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}
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op->SetOutput("Out", {outputs[0]});
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}
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// operator use_mkldnn
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// ---------------------------------------
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// (a,b)->concat->c none
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// (c,weights,bias)->conv->f none
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// f->relu->g false
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// g->pool->h false
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// (h,weights2,bias2)->conv->k true
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// k->relu->l true
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ProgramDesc BuildProgramDesc() {
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ProgramDesc prog;
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for (auto& v :
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std::vector<std::string>({"a", "b", "c", "weights", "bias", "f", "g",
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"h", "weights2", "bias2", "k", "l"})) {
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auto* var = prog.MutableBlock(0)->Var(v);
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var->SetType(proto::VarType::SELECTED_ROWS);
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if (v == "weights" || v == "bias") {
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var->SetPersistable(true);
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}
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}
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SetOp(&prog, "concat", "concat1", std::vector<std::string>({"a", "b"}),
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std::vector<std::string>({"c"}), boost::indeterminate);
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SetOp(&prog, "conv2d", "conv1",
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std::vector<std::string>({"c", "weights", "bias"}),
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std::vector<std::string>({"f"}), boost::indeterminate);
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SetOp(&prog, "relu", "relu1", std::vector<std::string>({"f"}),
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std::vector<std::string>({"g"}), false);
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SetOp(&prog, "pool2d", "pool1", std::vector<std::string>({"g"}),
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std::vector<std::string>({"h"}), false);
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SetOp(&prog, "conv2d", "conv2",
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std::vector<std::string>({"h", "weights2", "bias2"}),
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std::vector<std::string>({"k"}), true);
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SetOp(&prog, "relu", "relu2", std::vector<std::string>({"k"}),
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std::vector<std::string>({"l"}), true);
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return prog;
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}
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void MainTest(std::initializer_list<std::string> mkldnn_enabled_op_types,
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unsigned expected_use_mkldnn_true_count) {
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auto prog = BuildProgramDesc();
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std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
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auto pass = PassRegistry::Instance().Get("mkldnn_placement_pass");
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pass->Set("mkldnn_enabled_op_types",
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new std::unordered_set<std::string>(mkldnn_enabled_op_types));
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graph = pass->Apply(std::move(graph));
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unsigned use_mkldnn_true_count = 0;
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for (auto* node : graph->Nodes()) {
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if (node->IsOp()) {
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auto* op = node->Op();
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if (op->HasAttr("use_mkldnn") &&
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boost::get<bool>(op->GetAttr("use_mkldnn"))) {
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++use_mkldnn_true_count;
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}
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}
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}
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EXPECT_EQ(use_mkldnn_true_count, expected_use_mkldnn_true_count);
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}
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TEST(MKLDNNPlacementPass, enable_conv_relu) {
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// 1 conv (1 conv is always true) + 2 relu (1 relu is always true) + 0 pool
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MainTest({"conv2d", "relu"}, 3);
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}
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TEST(MKLDNNPlacementPass, enable_relu_pool) {
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// 1 conv (1 conv is always true) + 2 relu (1 relu is always true) + 1 pool
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MainTest({"relu", "pool2d"}, 4);
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}
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TEST(MKLDNNPlacementPass, enable_all) {
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// 1 conv (1 conv is always true) + 2 relu (1 relu is always true) + 1 pool
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MainTest({}, 4);
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}
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} // namespace ir
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} // namespace framework
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} // namespace paddle
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USE_PASS(mkldnn_placement_pass);
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@ -0,0 +1,4 @@
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cc_library(anakin_engine SRCS engine.cc)
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target_link_libraries(anakin_engine anakin anakin_saber_common)
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cc_test(test_anakin_engine SRCS test_anakin_engine.cc DEPS anakin_engine)
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add_subdirectory(convert)
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@ -0,0 +1,2 @@
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cc_library(anakin_op_converter SRCS fc.cc registrar.cc DEPS anakin_engine framework_proto scope)
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cc_test(test_anakin_fc SRCS test_fc_op.cc DEPS anakin_op_converter mul_op)
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@ -0,0 +1,73 @@
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// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "paddle/fluid/inference/anakin/convert/fc.h"
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#include <algorithm>
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using anakin::graph::GraphGlobalMem;
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using anakin::AK_FLOAT;
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using anakin::Precision;
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using anakin::saber::NV;
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using anakin::saber::X86;
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using anakin::saber::Shape;
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using anakin::PBlock;
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using anakin::PTuple;
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namespace paddle {
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namespace inference {
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namespace anakin {
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void FcOpConverter::operator()(const framework::proto::OpDesc &op,
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const framework::Scope &scope, bool test_mode) {
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framework::OpDesc op_desc(op, nullptr);
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PADDLE_ENFORCE_EQ(op_desc.Input("X").size(), 1);
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PADDLE_ENFORCE_EQ(op_desc.Input("Y").size(), 1);
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PADDLE_ENFORCE_EQ(op_desc.Output("Out").size(), 1);
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auto x_name = op_desc.Input("X").front();
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auto op_name = op_desc.Type() + ":" + op_desc.Output("Out").front();
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auto *y_v = scope.FindVar(op_desc.Input("Y").front());
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PADDLE_ENFORCE_NOT_NULL(y_v);
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auto *y_t = y_v->GetMutable<framework::LoDTensor>();
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auto input_name = op_desc.Input("X").front();
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auto output_name = op_desc.Output("Out").front();
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auto weight_shape = framework::vectorize2int(y_t->dims());
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engine_->AddOp(op_name, "Dense", {input_name}, {output_name});
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engine_->AddOpAttr(op_name, "bias_term", false);
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engine_->AddOpAttr(op_name, "axis", 1);
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int out_dim = weight_shape[1];
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engine_->AddOpAttr(op_name, "out_dim", out_dim);
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weight_shape.push_back(1);
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weight_shape.push_back(1);
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Shape anakin_shape(weight_shape);
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framework::LoDTensor weight_tensor;
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weight_tensor.Resize(y_t->dims());
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TensorCopySync((*y_t), platform::CPUPlace(), &weight_tensor);
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auto *weight1 =
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GraphGlobalMem<NV>::Global().template new_block<AK_FLOAT>(anakin_shape);
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float *cpu_data = static_cast<float *>(weight1->h_tensor().mutable_data());
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std::copy_n(weight_tensor.data<float>(), weight_tensor.numel(), cpu_data);
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weight1->d_tensor().set_shape(anakin_shape);
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weight1->d_tensor().copy_from(weight1->h_tensor());
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engine_->AddOpAttr(op_name, "weight_1", *weight1);
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}
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} // namespace anakin
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} // namespace inference
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} // namespace paddle
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