apeforest commented on a change in pull request #14836: Refactor AGInfo and
Imperative
URL: https://github.com/apache/incubator-mxnet/pull/14836#discussion_r305026489
##########
File path: src/imperative/imperative.cc
##########
@@ -316,181 +312,223 @@ std::vector<NDArray*> Imperative::Backward(
info.outputs.back() = static_cast<real_t>(1.0);
}
}
+ return ograd_entries;
+}
- // Get gradient graph
- Symbol sym;
- sym.outputs = graph.outputs;
- std::vector<NodeEntry> xs;
- std::vector<NDArray*> x_grads;
- std::vector<OpReqType> x_reqs;
- if (variables.size()) {
- xs.reserve(variables.size());
- x_grads.reserve(variables.size());
- x_reqs.reserve(variables.size());
+struct Imperative::GradientVariableNodes {
+ std::vector<nnvm::NodeEntry> variable_nodes;
+ std::vector<NDArray*> gradients;
+ std::vector<OpReqType> op_req_types;
+};
+
+Imperative::GradientVariableNodes Imperative::CreateGradientVariableNodes(
+ const std::vector<NDArray *> &variables,
+ const std::vector<nnvm::NodeEntry> &outputs) {
+ GradientVariableNodes var_nodes;
+ if (!variables.empty()) {
+ var_nodes.variable_nodes.reserve(variables.size());
+ var_nodes.gradients.reserve(variables.size());
+ var_nodes.op_req_types.reserve(variables.size());
for (size_t i = 0; i < variables.size(); ++i) {
CHECK(!AGInfo::IsNone(*variables[i]) &&
AGInfo::IsVariable(variables[i]->entry_.node))
<< "Cannot differentiate with respect to the " << i+1 << "-th
variable"
<< " because it does not require gradient.";
- xs.emplace_back(variables[i]->entry_);
- x_grads.push_back(new NDArray());
- x_reqs.push_back(kWriteTo);
+ var_nodes.variable_nodes.emplace_back(variables[i]->entry_);
+ var_nodes.gradients.push_back(new NDArray());
+ var_nodes.op_req_types.push_back(kWriteTo);
}
} else {
- std::vector<NodePtr> args = sym.ListInputs(Symbol::kReadOnlyArgs);
- xs.reserve(args.size());
- x_grads.reserve(args.size());
- x_reqs.reserve(args.size());
- for (const auto& i : args) {
- AGInfo& info = AGInfo::Get(i);
- if (info.grad_req == kNullOp) continue;
- xs.emplace_back(NodeEntry{i, 0, 0});
- x_grads.push_back(&info.out_grads[0]);
- x_reqs.push_back(info.grad_req);
- info.fresh_out_grad = true;
+ nnvm::Symbol s;
+ s.outputs = outputs;
+ std::vector<nnvm::NodePtr> input_ro_nodes =
s.ListInputs(Symbol::kReadOnlyArgs);
+ var_nodes.variable_nodes.reserve(input_ro_nodes.size());
+ var_nodes.gradients.reserve(input_ro_nodes.size());
+ var_nodes.op_req_types.reserve(input_ro_nodes.size());
+ for (const auto& node : input_ro_nodes) {
+ AGInfo& info = AGInfo::Get(node);
+ if (info.grad_req != kNullOp) {
+ var_nodes.variable_nodes.emplace_back(node);
+ var_nodes.gradients.push_back(&info.out_grads[0]);
+ var_nodes.op_req_types.push_back(info.grad_req);
+ info.fresh_out_grad = true;
+ }
}
- CHECK_GT(xs.size(), 0)
+ CHECK_GT(var_nodes.variable_nodes.size(), 0)
<< "There are no inputs in computation graph that require gradients.";
}
+ return var_nodes;
+}
+
+
- Graph g_graph = pass::MXGradient(
- graph, graph.outputs, xs, ograd_entries,
+std::vector<NDArray*> Imperative::Backward(
+ const std::vector<NDArray*>& outputs,
+ const std::vector<NDArray*>& ograds,
+ const std::vector<NDArray*>& variables,
+ bool is_train, bool retain_graph,
+ bool create_graph) {
+ using namespace nnvm;
Review comment:
This namespace is redundant since in this function nnvm:: is still used
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