xref: /aosp_15_r20/external/tensorflow/tensorflow/lite/simple_memory_arena_debug_dump.cc (revision b6fb3261f9314811a0f4371741dbb8839866f948)

/* Copyright 2021 The TensorFlow 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 <algorithm>
#include <cstdio>
#include <functional>
#include <limits>
#include <queue>
#include <string>
#include <vector>

#include "tensorflow/lite/simple_memory_arena.h"

namespace tflite {
namespace {
// Same w/ that defined in tensorflow/lite/arena_planner.cc.
constexpr int32_t kNodeNotAssigned = std::numeric_limits<int32_t>::max();

void PrintIntVector(const std::vector<int>& v) {
  if (v.empty()) {
    printf("[]");
    return;
  }

  int range_start = v[0];
  int range_end = range_start;
  std::function<void(const char*)> print_range = [&](const char* suffix) {
    if (range_end == range_start) {
      printf("%d%s", range_start, suffix);
    } else if (range_end == range_start + 1) {
      printf("%d,%d%s", range_start, range_end, suffix);
    } else {
      printf("%d-%d%s", range_start, range_end, suffix);
    }
  };

  printf("[");
  for (int i = 1; i < v.size(); ++i) {
    int current = v[i];
    if (current == range_end + 1) {
      range_end = current;
    } else {
      print_range(",");
      range_start = range_end = current;
    }
  }
  print_range("]");
}

struct PerLayerInfo {
  PerLayerInfo() {}
  PerLayerInfo(int id, size_t bytes, const std::vector<int>& tensors)
      : node_id(id), total_bytes(bytes), live_tensors(tensors) {}
  int node_id;
  size_t total_bytes = 0;
  std::vector<int> live_tensors;
};
struct PerLayerInfoGreater {
  bool operator()(const PerLayerInfo& l, const PerLayerInfo& r) {
    return l.total_bytes > r.total_bytes;
  }
};
class PerLayerMinHeap
    : public std::priority_queue<PerLayerInfo, std::vector<PerLayerInfo>,
                                 PerLayerInfoGreater> {
 public:
  // Just to expose iterators to simplify iterating over contained elements.
  std::vector<PerLayerInfo>::const_iterator begin() const { return c.begin(); }
  std::vector<PerLayerInfo>::const_iterator end() const { return c.end(); }
};

class TopKLayers {
 public:
  TopKLayers(size_t top_k, size_t arena_size)
      : top_k_(top_k), arena_size_(arena_size) {}

  void Add(int node_id, size_t total_bytes,
           const std::vector<int>& live_tensors) {
    if (topk_usage_.size() < top_k_) {
      topk_usage_.emplace(PerLayerInfo(node_id, total_bytes, live_tensors));
      return;
    }
    if (total_bytes < topk_usage_.top().total_bytes) return;
    topk_usage_.pop();
    topk_usage_.emplace(PerLayerInfo(node_id, total_bytes, live_tensors));
  }

  void Print() const {
    printf("\nTop %zu memory-consuming layers:\n",
           topk_usage_.size() < top_k_ ? topk_usage_.size() : top_k_);
    // As we use a min-heap but want to print out usage in decreasing order, we
    // use a temporary vector to hold pointers to top memory-consuming layers
    // and do a sorting on it.
    std::vector<const PerLayerInfo*> tops;
    for (const auto& usage : topk_usage_) tops.push_back(&usage);
    std::sort(tops.begin(), tops.end(),
              [](const PerLayerInfo* l, const PerLayerInfo* r) {
                return l->total_bytes > r->total_bytes;
              });
    for (const auto* usage : tops) {
      printf(
          "Node %d: %zu bytes (%.3f MB), utilization rate: %.3f%%, %zu live "
          "tensors: ",
          usage->node_id, usage->total_bytes,
          static_cast<float>(usage->total_bytes) / (1 << 20),
          static_cast<float>(usage->total_bytes) / arena_size_ * 100.0,
          usage->live_tensors.size());
      PrintIntVector(usage->live_tensors);
      printf("\n");
    }
    printf("\n");
  }

 private:
  const size_t top_k_;
  const size_t arena_size_;
  PerLayerMinHeap topk_usage_;
};
}  // namespace

// Corresponding weak declaration found in lite/simple_memory_arena.cc
void DumpArenaInfo(const std::string& name,
                   const std::vector<int>& execution_plan, size_t arena_size,
                   const std::vector<ArenaAllocWithUsageInterval>& allocs) {
  if (allocs.empty() || execution_plan.empty()) return;

  const int max_node_id =
      *std::max_element(execution_plan.begin(), execution_plan.end());

  printf("=== Beginning of %s ===\n", name.c_str());
  printf("Total size is %zu bytes (%.3f MB), holding %zu tensors.\n",
         arena_size, static_cast<float>(arena_size) / (1 << 20), allocs.size());
  std::vector<int> max_size_tensors;
  size_t max_tensor_size = 0;
  for (const auto& alloc_info : allocs) {
    printf("tensor %d: life_span: node [%d, %d], size:  %zu bytes (%.3f MB).\n",
           alloc_info.tensor, alloc_info.first_node,
           alloc_info.last_node == kNodeNotAssigned ? max_node_id
                                                    : alloc_info.last_node,
           alloc_info.size, static_cast<float>(alloc_info.size) / (1 << 20));
    if (alloc_info.size > max_tensor_size) {
      max_size_tensors.clear();
      max_size_tensors.push_back(alloc_info.tensor);
      max_tensor_size = alloc_info.size;
    } else if (alloc_info.size == max_tensor_size) {
      max_size_tensors.push_back(alloc_info.tensor);
    }
  }
  std::sort(max_size_tensors.begin(), max_size_tensors.end());
  printf("%zu tensors are of same max size (%zu B (%.3f MB)): ",
         max_size_tensors.size(), max_tensor_size,
         static_cast<float>(max_tensor_size) / (1 << 20));
  PrintIntVector(max_size_tensors);

  printf("\nPer-layer-info in the order of op execution:\n");
  // A straightforward way of computing per-op memory consumption
  // in the order of O(execution_plan.size() * allocs.size().
  std::vector<size_t> per_op_mem_bytes(execution_plan.size());
  // Track top 5 layers that consume most memory.
  TopKLayers top_usage(5, arena_size);
  for (int i = 0; i < execution_plan.size(); ++i) {
    const int node_id = execution_plan[i];
    size_t total_bytes = 0;
    std::vector<int> live_tensors;
    for (const auto& alloc_info : allocs) {
      if (node_id >= alloc_info.first_node && node_id <= alloc_info.last_node) {
        total_bytes += alloc_info.size;
        live_tensors.push_back(alloc_info.tensor);
      }
    }
    per_op_mem_bytes[i] = total_bytes;
    std::sort(live_tensors.begin(), live_tensors.end());
    printf(
        "Node %d: %zu bytes (%.3f MB), utilization rate: %.3f%%, %zu live "
        "tensors: ",
        node_id, total_bytes, static_cast<float>(total_bytes) / (1 << 20),
        static_cast<float>(total_bytes) / arena_size * 100.0,
        live_tensors.size());
    PrintIntVector(live_tensors);
    printf("\n");
    top_usage.Add(node_id, total_bytes, live_tensors);
  }
  top_usage.Print();
  printf("===End of %s ===\n\n", name.c_str());
}
}  // namespace tflite