Method Compute

tensorflow/core/graph/gradients.cc:336–399

}

Status SymbolicGradientBuilder::Compute() {
  // Initialize backprops.
  InitBackprop();

  // Backward propagation.
  gtl::InlinedVector<NodeOut, 8> dy;
  while (!ready_.empty()) {
    // n has collected all gradients.
    Node* n = ready_.front();
    ready_.pop_front();

    // "n" has num_x inputs and num_y outputs.
    const int num_x = n->num_inputs();
    const int num_y = n->num_outputs();

    auto iter = stop_nodes_.find(n->id());
    if (iter != stop_nodes_.end()) {
      // Stop backprop.
      // TODO(andydavis) Support stop nodes with more than one output.
      CHECK_EQ(1, num_y);
      continue;
    }

    // dy[i] is the sum of i-th output's backpropped gradients.
    dy.clear();
    dy.resize(num_y, {nullptr, 0});
    for (int i = 0; i < num_y; ++i) {
      dy[i] = SumGradients({n, i});
    }

    if (IsPrimitiveOpWithNoGrad(n->type_string())) {
      // No grad defined for this op: Backprop zeros along the in edges.
      for (const Edge* e : n->in_edges()) {
        if (e->IsControlEdge()) continue;
        BackpropZerosAlongEdge({e->src(), e->src_output()});
      }
      continue;
    }

    // Adds a gradient node with num_x + num_y inputs and num_x
    // outputs.
    // TODO(andydavis) Support primitive gradient ops.
    Node* grad = AddSymGrad(graph_, n, dy);
    for (const Edge* e : n->in_edges()) {
      if (e->IsControlEdge()) continue;
      graph_->AddEdge(e->src(), e->src_output(), grad, e->dst_input());
    }
    for (int i = 0; i < num_y; ++i) {
      graph_->AddEdge(dy[i].node, dy[i].index, grad, num_x + i);
    }

    // Backprops along the in edges.
    for (const Edge* e : n->in_edges()) {
      if (e->IsControlEdge()) continue;
      BackpropAlongEdge({grad, e->dst_input()}, {e->src(), e->src_output()});
    }
  }