cjolivier01 commented on a change in pull request #9677: Refactor operators and
add MKLDNN
URL: https://github.com/apache/incubator-mxnet/pull/9677#discussion_r168576406
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File path: tests/cpp/include/test_core_op.h
##########
@@ -311,68 +339,166 @@ class CoreOpExecutor : public
test::op::OperatorDataInitializer<DType>
op_ = nnvm::Op::Get(op_name);
CHECK_NOTNULL(op_);
+ std::map<int, const NDArray *> index2array;
+ nnvm::NodePtr bwd_node_ptr;
+ if (backward_for_op) {
+ bwd_node_ptr = backward_for_op->CalcBackwardPass(&index2array);
+ }
+
// Set up forward
attrs_ = ParseAttrs(op_, args);
- const int num_inputs = op_->num_inputs;
+ int num_inputs = op_->num_inputs;
+ if (op_->get_num_inputs) {
+ num_inputs = op_->get_num_inputs(attrs_);
+ } else if (backward_for_op) {
+ if (bwd_node_ptr) {
+ num_inputs = static_cast<int>(bwd_node_ptr->inputs.size());
+ }
+ }
if (!inputs.empty()) {
CHECK_EQ(inputs.size(), static_cast<size_t>(num_inputs));
}
- int inferred_num_outputs, num_visible_outputs;
+ int inferred_num_outputs /*, num_visible_outputs*/;
- imperative::SetNumOutputs(op_, attrs_, num_inputs, &inferred_num_outputs,
- &num_visible_outputs);
+ if (op_->get_num_outputs) {
+ inferred_num_outputs = op_->get_num_outputs(attrs_);
+ } else {
+ inferred_num_outputs = op_->num_outputs;
+ }
// Generic, all shapes the same. Probably this will need to be adjusted
for more complex
// operators such as dot
- std::vector<TShape> shapes;
- for (size_t i = 0, n = std::max(num_visible_outputs, num_inputs); i < n;
++i) {
- shapes.emplace_back(i < input_shapes_.size() ? input_shapes_[i]
- :
input_shapes_[input_shapes_.size() - 1]);
+ std::vector<nnvm::TShape> input_shapes;
+ if (!input_shapes_.empty()) {
+ for (size_t i = 0, n = num_inputs; i < n; ++i) {
+ input_shapes.emplace_back(i < input_shapes_.size() ? input_shapes_[i]
+ :
input_shapes_[input_shapes_.size()
+ -
1]);
+ }
}
std::vector<NDArray *> inputs_p, outputs_p;
if (!outputs.empty()) {
- CHECK_EQ(outputs.size(), static_cast<size_t>(num_visible_outputs));
+ CHECK_EQ(outputs.size(), static_cast<size_t>(inferred_num_outputs));
}
inputs_.reserve(num_inputs);
inputs_p.reserve(num_inputs);
- outputs_.reserve(num_visible_outputs);
- outputs_p.reserve(num_visible_outputs);
+ outputs_.reserve(inferred_num_outputs);
+ outputs_p.reserve(inferred_num_outputs);
+
+ std::vector<int> input_types;
+ input_types.reserve(num_inputs);
+ std::vector<int> output_types;
+ output_types.reserve(inferred_num_outputs);
+
+ static auto& finfer_type = Op::GetAttr<nnvm::FInferType>("FInferType");
+ if (finfer_type.count(op_)) {
+ input_types.resize(num_inputs, -1);
+ input_types[0] = default_dtype(); // Set first input to default type
+ output_types.resize(inferred_num_outputs, -1);
+ finfer_type[op_](attrs_, &input_types, &output_types);
+ CHECK_EQ(input_types.size(), num_inputs);
+ CHECK_EQ(output_types.size(), inferred_num_outputs);
+ } else {
+ if (backward_for_op) {
+ if (bwd_node_ptr) {
+ CHECK_EQ(bwd_node_ptr->inputs.size(), num_inputs);
+ input_types.resize(bwd_node_ptr->inputs.size(), -1);
+ for (size_t i = 0; i < num_inputs; ++i) {
+ const int map_key = bwd_node_ptr->inputs[i].index;
+ CHECK(index2array.find(map_key) != index2array.end());
+ const int dtype = index2array[map_key]->dtype();
+ input_types[i] = dtype;
+ }
+ for (const auto &fwd_inp : backward_for_op->inputs()) {
+ const int dtype = fwd_inp.data().type_flag_;
+ output_types.emplace_back(dtype);
+ }
+ } else {
+ for (size_t x = 0; x < num_inputs; ++x) {
+ input_types.emplace_back(default_dtype());
+ }
+ for (const auto &fwd_inp : backward_for_op->inputs()) {
+ const int dtype = fwd_inp.data().type_flag_;
+ output_types.emplace_back(dtype);
+ }
+ }
+ } else {
+ CHECK(false); // above always true?
+ for (size_t x = 0; x < num_inputs; ++x) {
Review comment:
this is a notification for the developer that this is possible. it's test
code.
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