multithread executor

src/common/depCore.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
+#ifdef PADDLE_EXECUTOR_MULTITHREAD
+#include <string>
+#include <unordered_map>
+#include <vector>
+#include "framework/operator.h"
+
+namespace paddle_mobile {
+
+class depCore {
+ public:
+  template <typename Dtype>
+  void analysisDep(
+      const std::vector<std::shared_ptr<framework::OperatorBase<Dtype>>>& ops) {
+    std::unordered_map<std::string, int> vars;
+    size_t nop = ops.size();
+    for (size_t i = 0; i < nop; i++) {
+      const auto& op = ops[i];
+      for (const auto& kv : op->Outputs()) {
+        for (const auto& v : kv.second) {
+          vars[v] = i;
+        }
+      }
+    }
+    deps.resize(nop);
+    next.resize(nop);
+    for (size_t i = 0; i < nop; i++) {
+      const auto& op = ops[i];
+      for (const auto& kv : op->Inputs()) {
+        for (const auto& v : kv.second) {
+          if (vars.find(v) == vars.end()) {
+            continue;
+          }
+          int di = vars[v];
+          if (di == i) {
+            continue;
+          }
+          if (std::find(deps[i].begin(), deps[i].end(), di) != deps[i].end()) {
+            continue;
+          }
+          deps[i].push_back(di);
+          next[di].push_back(i);
+        }
+      }
+    }
+  }
+  const std::vector<int>& getNext(int i) { return next[i]; }
+  const std::vector<int>& getDeps(int i) { return deps[i]; }
+  std::vector<std::vector<int>> deps;
+  std::vector<std::vector<int>> next;
+};
+}  // namespace paddle_mobile
+
+#endif

src/common/threadpool.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 <condition_variable>
+#include <functional>
+#include <future>
+#include <memory>
+#include <mutex>
+#include <queue>
+#include <stdexcept>
+#include <thread>
+#include <vector>
+
+class ThreadPool {
+ public:
+  static ThreadPool& getThreadPool();
+  static int getThreadPoolThreadId();
+  explicit ThreadPool(size_t);
+  template <class F, class... Args>
+  auto enqueue(F&& f, Args&&... args)
+      -> std::future<typename std::result_of<F(Args...)>::type>;
+  ~ThreadPool();
+  int getTid(const std::thread::id& id) {
+    for (int i = 0; i < workers.size(); i++) {
+      if (workers[i].get_id() == id) {
+        return i;
+      }
+    }
+    return -1;
+  }
+
+ private:
+  // need to keep track of threads so we can join them
+  std::vector<std::thread> workers;
+  // the task queue
+  std::queue<std::function<void()>> tasks;
+
+  // synchronization
+  std::mutex queue_mutex;
+  std::condition_variable condition;
+  bool stop;
+};
+
+// the constructor just launches some amount of workers
+inline ThreadPool::ThreadPool(size_t threads) : stop(false) {
+  for (size_t i = 0; i < threads; ++i)
+    workers.emplace_back([this] {
+      for (;;) {
+        std::function<void()> task;
+        {
+          std::unique_lock<std::mutex> lock(this->queue_mutex);
+          this->condition.wait(
+              lock, [this] { return this->stop || !this->tasks.empty(); });
+          // for (;;) {
+          //     if (this->stop || !this->tasks.empty()) {
+          //         break;
+          //     }
+          //     lock.unlock();
+          //     lock.lock();
+          // }
+          if (this->stop && this->tasks.empty()) return;
+          task = std::move(this->tasks.front());
+          this->tasks.pop();
+        }
+
+        task();
+      }
+    });
+}
+
+// add new work item to the pool
+template <class F, class... Args>
+auto ThreadPool::enqueue(F&& f, Args&&... args)
+    -> std::future<typename std::result_of<F(Args...)>::type> {
+  using return_type = typename std::result_of<F(Args...)>::type;
+
+  auto task = std::make_shared<std::packaged_task<return_type()>>(
+      std::bind(std::forward<F>(f), std::forward<Args>(args)...));
+
+  std::future<return_type> res = task->get_future();
+  {
+    std::unique_lock<std::mutex> lock(queue_mutex);
+
+    // don't allow enqueueing after stopping the pool
+    // if(stop)
+    //     throw std::runtime_error("enqueue on stopped ThreadPool");
+
+    tasks.emplace([task]() { (*task)(); });
+  }
+  condition.notify_one();
+  return res;
+}
+
+// the destructor joins all threads
+inline ThreadPool::~ThreadPool() {
+  {
+    std::unique_lock<std::mutex> lock(queue_mutex);
+    stop = true;
+  }
+  condition.notify_all();
+  for (std::thread& worker : workers) worker.join();
+}
+
+ThreadPool& ThreadPool::getThreadPool() {
+  static ThreadPool threadPool(3);
+  return threadPool;
+}
+
+int ThreadPool::getThreadPoolThreadId() {
+  return getThreadPool().getTid(std::this_thread::get_id());
+}

src/io/io.cpp

@@ -12,15 +12,8 @@ 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 "io.h"
+#include "io/io.h"
 #include <vector>
-#define PADDLE_MOBILE_PROFILE
-#ifdef PADDLE_MOBILE_PROFILE
-#include <algorithm>
-#include <ctime>
-#include <unordered_map>
-#endif
-
 #include "common/enforce.h"
 #include "common/log.h"
 #include "framework/framework.pb-c.h"
@@ -31,6 +24,12 @@ limitations under the License. */
 #include "framework/program/var_desc.h"
 #include "framework/scope.h"
 #include "framework/tensor.h"
+#ifdef PADDLE_EXECUTOR_MULTITHREAD
+#include <algorithm>
+#include <queue>
+#include <utility>
+#include "common/threadpool.h"
+#endif
 
 namespace paddle_mobile {
 using framework::Variable;
@@ -142,8 +141,6 @@ const framework::Program<Dtype, P> Loader<Dtype, P>::LoadProgram(
     }
   }
 
-  //  originProgramDesc->Description("program: ");
-
   if (optimize) {
     framework::ProgramOptimize program_optimize;
     program.optimizeProgram =
@@ -164,7 +161,6 @@ template class Loader<FPGA, Precision::FP32>;
 template class Loader<GPU_MALI, Precision::FP32>;
 
 #pragma mark - executor
-
 template <typename Dtype, Precision P>
 Executor<Dtype, P>::Executor(const framework::Program<Dtype> p, int batch_size,
                              bool use_optimize)
@@ -178,6 +174,9 @@ Executor<Dtype, P>::Executor(const framework::Program<Dtype> p, int batch_size,
   variable_ptr[0].SetValue<int>(batch_size);
   const std::vector<std::shared_ptr<framework::BlockDesc>> blocks =
       to_predict_program_->Blocks();
+#ifdef PADDLE_EXECUTOR_MULTITHREAD
+  depManager.resize(blocks.size());
+#endif
   for (int i = 0; i < blocks.size(); ++i) {
     std::shared_ptr<framework::BlockDesc> block_desc = blocks[i];
     std::vector<std::shared_ptr<framework::OpDesc>> ops = block_desc->Ops();
@@ -188,8 +187,10 @@ Executor<Dtype, P>::Executor(const framework::Program<Dtype> p, int batch_size,
           op->Type(), op->GetInputs(), op->GetOutputs(), op->GetAttrMap(),
           program_.scope);
       op_base->InferShape();
-
       ops_of_block_[*block_desc.get()].push_back(op_base);
+#ifdef PADDLE_EXECUTOR_MULTITHREAD
+      depManager[i].analysisDep(ops_of_block_[*block_desc.get()]);
+#endif
     }
   }
   if (program_.is_commbine) {
@@ -350,48 +351,132 @@ std::shared_ptr<framework::Tensor> Executor<Dtype, P>::Predict(
   feed_tensor->ShareDataWith(t);
   std::shared_ptr<framework::BlockDesc> to_predict_block =
       to_predict_program_->Block(0);
+  auto &ops = ops_of_block_[*to_predict_block.get()];
 #ifdef PADDLE_MOBILE_PROFILE
-  std::unordered_map<std::string, clock_t> _profile;
+  std::vector<ProfInfo> profile(ops.size());
 #endif
-  for (int j = 0; j < ops_of_block_[*to_predict_block.get()].size(); ++j) {
-    auto op = ops_of_block_[*to_predict_block.get()][j];
+#ifdef PADDLE_EXECUTOR_MULTITHREAD
+  std::mutex m;
+  std::condition_variable cv;
+  std::queue<int> next;
+  next.push(0);
+  int rsize = ops.size();
+  std::vector<int> status(rsize, 0);
+  auto &threadPool = ThreadPool::getThreadPool();
+  auto &dep = depManager[0];
+  auto finishF = [&ops, &m, &cv, &next, &status, &rsize, &dep](int opi) {
+    std::lock_guard<std::mutex> lk(m);
+    rsize--;
+    status[opi] = 2;
+    for (int i : dep.getNext(opi)) {
+      bool ok = true;
+      for (int j : dep.getDeps(i)) {
+        if (status[j] != 2) {
+          ok = false;
+          break;
+        }
+      }
+      if (ok && (status[i] == 0)) {
+        next.push(i);
+      }
+    }
+    cv.notify_one();
+  };
+  for (;;) {
+    std::unique_lock<std::mutex> lk(m);
+    cv.wait(lk, [&next, &rsize] { return rsize == 0 || !next.empty(); });
+    if (rsize == 0) {
+      break;
+    }
+    while (next.size() > 0) {
+      int opi = next.front();
+      next.pop();
+      status[opi] = 1;
+      threadPool.enqueue([opi, &ops, &finishF, &profile] {
+        auto &op = ops[opi];
 #ifdef PADDLE_MOBILE_PROFILE
-    _profile[op->Type()] -= clock();
+        struct timespec ts;
+        clock_gettime(CLOCK_MONOTONIC, &ts);
+        profile[opi].runBegin = (uint64_t)ts.tv_sec * 1e9 + ts.tv_nsec;
+        profile[opi].tid = ThreadPool::getThreadPoolThreadId();
 #endif
-    op->Run();
+        ops[opi]->Run();
 #ifdef PADDLE_MOBILE_PROFILE
-    _profile[op->Type()] += clock();
+        clock_gettime(CLOCK_MONOTONIC, &ts);
+        profile[opi].runEnd = (uint64_t)ts.tv_sec * 1e9 + ts.tv_nsec;
 #endif
+        finishF(opi);
+      });
+    }
   }
+#else
+  for (int i = 0; i < ops.size(); i++) {
 #ifdef PADDLE_MOBILE_PROFILE
-  {
-    std::cout << "====================[ profile ]======================\n";
-    using prof_t = std::pair<std::string, clock_t>;
-    std::vector<prof_t> _tprofile(_profile.begin(), _profile.end());
-    clock_t _ptotal = 0;
-    for (auto const &p : _tprofile) {
-      _ptotal += p.second;
-    }
-    auto compf = [](const prof_t &a, const prof_t &b) {
-      return a.second > b.second;
-    };
-    std::sort(_tprofile.begin(), _tprofile.end(), compf);
-    _tprofile.push_back(std::make_pair("total", _ptotal));
-    for (auto const &p : _tprofile) {
-      printf("%-16s\t%-10.0f\t%-.4f\n", p.first.c_str(), (float)p.second,
-             (float)p.second / _ptotal * 100.0);
-    }
-    std::cout << "====================[---------]======================\n";
+    struct timespec ts;
+    clock_gettime(CLOCK_MONOTONIC, &ts);
+    profile[i].runBegin = (uint64_t)ts.tv_sec * 1e9 + ts.tv_nsec;
+#endif
+    ops[i]->Run();
+#ifdef PADDLE_MOBILE_PROFILE
+    clock_gettime(CLOCK_MONOTONIC, &ts);
+    profile[i].runEnd = (uint64_t)ts.tv_sec * 1e9 + ts.tv_nsec;
+#endif
   }
 #endif
-  auto ops = ops_of_block_[*to_predict_program_->Block(0)];
   auto last_op = ops.rbegin();
+
   auto output_map = (*last_op)->Outputs();
   std::vector<std::string> out_keys = (*last_op)->GetOutKeys();
   PADDLE_MOBILE_ENFORCE(out_keys.size() > 0, "the last op contains no output");
   framework::LoDTensor *output_tensor =
       framework::GetVarValue<framework::LoDTensor>(out_keys[0], output_map,
                                                    *(program_.scope));
+#ifdef PADDLE_MOBILE_PROFILE
+#ifdef PADDLE_EXECUTOR_MULTITHREAD
+  // TODO expose profile info as an interface, user can get them to analysis
+  //      the performance of their deepnet.
+  FILE *df = fopen("net.dot", "w");
+  fprintf(df, "digraph {\n");
+  for (int i = 0; i < ops.size(); i++) {
+    for (int j : dep.getNext(i)) {
+      fprintf(df, "op_%d -> op_%d\n", i, j);
+    }
+  }
+  for (int i = 0; i < ops.size(); i++) {
+    fprintf(df, "op_%d[label=\"%s (%d)\"]\n", i, ops[i]->Type().c_str(), i);
+  }
+  fprintf(df, "}\n");
+  fclose(df);
+#endif
+  FILE *pf = fopen("profile.out", "w");
+  std::unordered_map<std::string, uint64_t> _tp;
+  for (int i = 0; i < profile.size(); i++) {
+    const auto &pInfo = profile[i];
+    uint64_t timeCost = pInfo.runEnd - pInfo.runBegin;
+    _tp[ops[i]->Type()] += timeCost;
+    fprintf(pf, "%d\t%s\t%d\t%llu\t%llu\t%llu\n", i, ops[i]->Type().c_str(),
+            pInfo.tid, pInfo.runBegin, pInfo.runEnd, timeCost);
+  }
+  fclose(pf);
+  printf("====================[ profile ]======================\n");
+  using prof_t = std::pair<std::string, uint64_t>;
+  std::vector<prof_t> _tv(_tp.begin(), _tp.end());
+  uint64_t _ptotal = 0;
+  for (auto const &p : _tv) {
+    _ptotal += p.second;
+  }
+  auto compf = [](const prof_t &a, const prof_t &b) {
+    return a.second > b.second;
+  };
+  std::sort(_tv.begin(), _tv.end(), compf);
+  _tv.push_back(std::make_pair("total", _ptotal));
+  for (auto const &p : _tv) {
+    printf("%-16s\t%-10.0f\t%-2.4f\n", p.first.c_str(), (float)p.second,
+           (float)p.second / _ptotal * 100.0);
+  }
+  printf("====================[---------]======================\n");
+#endif
+
   return std::shared_ptr<framework::Tensor>(output_tensor);
 }
 template <typename Dtype, Precision P>

src/io/io.h

@@ -18,12 +18,17 @@ limitations under the License. */
 #include <memory>
 #include <string>
 #include <vector>
-
 #include "common/types.h"
 #include "framework/lod_tensor.h"
 #include "framework/operator.h"
 #include "framework/program/program.h"
 #include "framework/tensor.h"
+#ifdef PADDLE_EXECUTOR_MULTITHREAD
+#include <condition_variable>
+#include <mutex>
+#include <thread>
+#include "common/depCore.h"
+#endif
 
 namespace paddle_mobile {
 
@@ -92,6 +97,16 @@ class Executor {
            std::vector<std::shared_ptr<framework::OperatorBase<Dtype>>>>
       ops_of_block_;
   bool use_optimize_ = false;
+#ifdef PADDLE_EXECUTOR_MULTITHREAD
+  std::vector<depCore> depManager;
+#endif
+#ifdef PADDLE_MOBILE_PROFILE
+  struct ProfInfo {
+    int tid = 0;
+    uint64_t runBegin = 0UL;
+    uint64_t runEnd = 0UL;
+  };
+#endif
 };
 
 }  // namespace paddle_mobile

tools/profile_show.sh

+#!/usr/bin/env sh
+cat <<EOF
+<html>
+<head>
+<style>
+html, body {
+position: absolute;
+width: 100%;
+height: 100%;
+margin: 0;
+}
+div.timeview {
+width: 100%;
+position: relative;
+overflow: scroll;
+}
+ul {
+position: absolute;
+margin: 0;
+list-style:none;
+padding: 0;
+margin: 0;
+}
+li {
+height: 15px;
+position: absolute;
+background: blue;
+}
+li:nth-child(odd) {
+background: blue;
+}
+li:nth-child(even) {
+background: rebeccapurple;
+}
+ul.timeline {
+z-index: -1;
+}
+ul.timeline li {
+position: relative;
+height: 15px;
+width: 100%;
+}
+ul.timeline li:nth-child(odd) {
+background: beige;
+}
+ul.timeline li:nth-child(even) {
+background: antiquewhite;
+}
+</style>
+</head>
+<body>
+<div class="timeview">
+<ul>
+EOF
+
+min=$(awk 'NR==1{min=$4} NR>1{if($4 < min) min=$4} END{print min}' $1)
+max=$(awk 'NR==1{max=$5} NR>1{if($5 > max) max=$5} END{print max}' $1)
+sort $1 -k1,1n | awk -v max="$max" -v min="$min" '
+BEGIN {
+  total = max - min
+}
+{
+    opid = $1
+    optype = $2
+    tid = $3
+    cb = $4
+    ce = $5
+    cl = $6
+    sum += $4 - $3
+    print "<li class=\"timeline\"" \
+          " data-opid=\"" opid "\"" \
+          " data-optype=\"" optype "\"" \
+          " data-tid=\"" tid "\"" \
+          " data-begin=\"" cb "\"" \
+          " data-end=\"" ce "\"" \
+          "></li>"
+}
+'
+
+cat <<EOF
+</ul>
+</div>
+<pre>
+EOF
+
+echo "==================[ profile ]==================="
+cat $1 | awk '
+NR>1{
+    optype = $2
+    sum += $5 - $4
+    count[$2] += $6
+}
+END {
+for (t in count) {
+    msg = sprintf("%-16s\t%-10d\t%-.4f", t, count[t], count[t]*100 / sum);
+    print msg
+}
+}' | sort -k2,2nr
+cat $1 | awk '
+NR>1{
+    sum += $5 - $4
+}
+END {
+msg = sprintf("%-16s\t%-10d\t%-.4f", "total", sum, 100);
+print msg
+}'
+
+cat <<EOF
+</pre>
+<script>
+const min= $min;
+const max= $max;
+const px_per_nanosecond = 1/1000000;
+const scale = px_per_nanosecond;
+const li = document.querySelectorAll('li');
+const thread = new Set();
+for (let i = 0; i < li.length; i++) {
+    const prof = li[i].dataset;
+    li[i].style.width = (prof.end - prof.begin)*scale + 'px';
+    li[i].style.left = (prof.begin - min)*scale + 'px';
+    li[i].style.top = prof.tid * 15 + 'px';
+    thread.add(prof.tid);
+}
+const ul = document.createElement('ul');
+ul.classList.add('timeline');
+ul.style.width = (max - min)*scale + 'px';
+thread.forEach(i => {
+    const l = document.createElement('li');
+    ul.appendChild(l);
+});
+const timeview = document.querySelector('.timeview');
+timeview.appendChild(ul);
+timeview.style.height = thread.size * 15 + 'px';
+
+</script>
+</body>
+</html>
+EOF
+