Method AddRunMeta

tensorflow/core/profiler/internal/tfprof_stats.cc:264–311

}

void TFStats::AddRunMeta(int64 step, std::unique_ptr<RunMetadata> run_meta) {
  if (!run_meta || !run_meta->has_step_stats()) {
    fprintf(stderr, "Invalid RunMetadata for step %lld\n", step);
    return;
  }
  if (steps_.find(step) == steps_.end()) {
    steps_.insert(step);
  }
  steps_.insert(step);

  bool has_gpu_scheduling = false;
  bool has_gpu_stream = false;

  for (const auto& dev_stat : run_meta->step_stats().dev_stats()) {
    string dev = absl::AsciiStrToLower(dev_stat.device());
    if (IsPlacedOnAccelerator(dev)) {
      has_gpu_scheduling = true;
      if (CountAsAcceleratorTime(dev)) {
        has_gpu_stream = true;
      }
    }
    for (const NodeExecStats& node_stat : dev_stat.node_stats()) {
      string name = node_stat.node_name();
      // Sometimes the node_name is suffixed with unnecessary information.
      auto split_pos = node_stat.node_name().find(":");
      if (split_pos != node_stat.node_name().npos) {
        name = node_stat.node_name().substr(0, split_pos);
      }
      auto node = nodes_map_.find(name);
      if (node == nodes_map_.end()) {
        NodeDef def;
        if (CreateRunMetadataNode(name, &def)) {
          size_t num_nodes = nodes_map_.size();
          nodes_map_[name] = std::unique_ptr<TFGraphNode>(
              new TFGraphNode(&def, num_nodes, &nodes_map_));
          nodes_map_.at(name)->AddStepStat(step, dev_stat.device(), node_stat);
        }
      } else {
        covered_nodes_.insert(node->second->id());
        node->second->AddStepStat(step, dev_stat.device(), node_stat);
      }
    }
  }

  if (has_gpu_scheduling && !has_gpu_stream) {
    miss_accelerator_stream_ = true;
  }
}

string TFStats::MaybeReportMissingTrace() const {
  string report = "";