add batch norm test

tests/cpp/operator/mkldnn_operator_test.cc

@@ -347,6 +347,31 @@ OpAttrs GetDeconvBackwardOp(int kernel, int num_filters, int dim, int stride, in
  return attrs;
 }
 
+OpAttrs GetBNOp() {
+ OpAttrs attrs;
+ attrs.attrs.op = Op::Get("BatchNorm");
+ attrs.num_inputs = 5;
+ attrs.num_outputs = 3;
+ attrs.accept_dims.insert(4);
+ attrs.requests.insert(OpReqType::kWriteTo);
+ attrs.attrs.op->attr_parser(&attrs.attrs);
+ attrs.input_types = ArrayTypes::Normal |
+ ArrayTypes::MKLDNN;
+ attrs.output_types = ArrayTypes::Normal |
+ ArrayTypes::MKLDNN;
+ return attrs;
+}
+
+OpAttrs GetBNBackwardOp() {
+ OpAttrs attrs;
+ attrs.attrs.op = Op::Get("_backward_BatchNorm");
+ attrs.num_inputs = 8;
+ attrs.num_outputs = 3;
+ attrs.attrs.op->attr_parser(&attrs.attrs);
+ attrs.requests.insert(OpReqType::kWriteTo);
+ return attrs;
+}
+
 void AssertEqual(const std::vector<NDArray *> &in_arrs,
  const std::vector<NDArray *> &out_arrs,
  float rtol = 1e-5, float atol = 1e-8) {
@@ -710,7 +735,7 @@ void TestOpEx(const OpAttrs &forward_attrs, const OpAttrs &backwards_attrs) {
 
  // If the array is a view, we shouldn't write data to it.
  if (in_arr.arr.IsView())
-  continue;
+ continue;
 
  NDArrayAttrs orig(in_arr.arr.Copy(in_arr.arr.ctx()), "InPlace Copy");
  for (int i = 0; i < forward_attrs.num_inputs; i++)
@@ -735,6 +760,124 @@ void TestOpEx(const OpAttrs &forward_attrs, const OpAttrs &backwards_attrs) {
  }
 }
 
+
+void TestOpExBNBackward(const OpAttrs &forward_attrs,
+ const OpAttrs &backwards_attrs,
+ const OpReqType &req,
+ const std::vector<NDArray*> &inputs,
+ const std::vector<NDArray*> &outputs,
+ const NDArrayAttrs &in_arr,
+ NDArrayAttrs* out_arr) {
+ std::vector<NDArray*> backwards_input(backwards_attrs.num_inputs);
+
+ std::vector<NDArray> backwards_buffer(backwards_attrs.num_outputs);
+ std::vector<NDArray> backwards_buffer2(backwards_attrs.num_outputs);
+
+ std::vector<NDArray*> backwards_outputs(backwards_attrs.num_outputs);
+ std::vector<NDArray*> backwards_ex_outputs(backwards_attrs.num_outputs);
+ std::vector<OpReqType> backwards_req(backwards_attrs.num_outputs);
+
+ if (req == kWriteTo) {
+ backwards_input[0] = &(out_arr->arr); // output grad
+ backwards_input[1] = outputs[1]; // mean
+ backwards_input[2] = outputs[2]; // var
+ backwards_input[3] = inputs[0]; // data
+ backwards_input[4] = inputs[1]; // gamma
+ backwards_input[5] = inputs[2]; // beta
+ backwards_input[6] = inputs[3]; // moving mean
+ backwards_input[7] = inputs[4]; // moving var
+
+ for (size_t i = 0; i < backwards_attrs.num_outputs; i++) {
+ auto tmp_output = in_arr.arr;
+ backwards_buffer.emplace_back(tmp_output.Copy(Context()));
+ backwards_buffer2.emplace_back(tmp_output.Copy(Context()));
+ backwards_outputs[i] = &backwards_buffer.back();
+ backwards_ex_outputs[i] = &backwards_buffer2.back();
+ Engine::Get()->WaitForAll();
+ backwards_req[i] = kWriteTo;
+ }
+
+ std::cout << "Backwards: ";
+ PrintVerifyMsg(*out_arr, in_arr);
+ Imperative::Get()->InvokeOp(
+ Context(), backwards_attrs.attrs, backwards_input, backwards_outputs,
+ backwards_req, DispatchMode::kFCompute, mxnet::OpStatePtr());
+ Imperative::Get()->InvokeOp(
+ Context(), backwards_attrs.attrs, backwards_input, backwards_ex_outputs,
+ backwards_req, DispatchMode::kFComputeEx, mxnet::OpStatePtr());
+ Engine::Get()->WaitForAll();
+ AssertEqual(backwards_outputs, backwards_ex_outputs);
+ }
+}
+
+// compares output of fcompute with fcomputex
+void TestOpExBN(const OpAttrs &forward_attrs, const OpAttrs &backwards_attrs) {
+ std::vector<NDArray*> inputs(forward_attrs.num_inputs);
+ std::vector<NDArray*> inputs2(forward_attrs.num_inputs);
+ std::vector<NDArray> inputs_buffer(forward_attrs.num_inputs);
+ std::vector<NDArray> inputs2_buffer(forward_attrs.num_inputs);
+ std::vector<NDArray*> outputs(forward_attrs.num_outputs);
+ std::vector<NDArray*> ex_outputs(forward_attrs.num_outputs);
+ std::vector<OpReqType> req(forward_attrs.num_outputs);
+
+ TestArrayShapes tas = GetTestArrayShapes();
+ std::vector<mkldnn::memory::primitive_desc> pds = tas.pds;
+
+ std::vector<NDArrayAttrs> in_arrs = GetTestInputArrays(forward_attrs.input_types, false);
+ std::vector<std::vector<NDArrayAttrs>> out_arrs(forward_attrs.num_outputs);
+ std::vector<std::vector<NDArrayAttrs>> ex_out_arrs(forward_attrs.num_outputs);
+
+ if (forward_attrs.requests.find(OpReqType::kWriteTo) != forward_attrs.requests.end()) {
+ for (int i1 = 0; i1 < in_arrs.size(); i1++) {
+ auto in_arr = in_arrs[i1];
+
+ CHECK_NE(forward_attrs.accept_dims.size(), 0);
+ if (forward_attrs.accept_dims.find(in_arr.arr.shape().ndim()) ==
+ forward_attrs.accept_dims.end())
+ continue;
+ for (int i = 0; i < forward_attrs.num_outputs; i++) {
+ out_arrs[i] =
+ GetTestOutputArrays(in_arr.arr.shape(), pds, {1}, true, forward_attrs.output_types);
+ ex_out_arrs[i] =
+ GetTestOutputArrays(in_arr.arr.shape(), pds, {1}, true, forward_attrs.output_types);
+ }
+ for (size_t output_i = 0; output_i < out_arrs[0].size(); output_i++) {
+ inputs_buffer.clear();
+ inputs2_buffer.clear();
+
+ for (int i = 0; i < forward_attrs.num_inputs; i++) {
+ inputs_buffer.emplace_back(in_arr.arr.Copy(Context()));
+ inputs2_buffer.emplace_back(in_arr.arr.Copy(Context()));
+ Engine::Get()->WaitForAll();
+ inputs[i] = &inputs_buffer.back();
+ inputs2[i] = &inputs2_buffer.back();
+ }
+ for (int i = 0; i < forward_attrs.num_outputs; i++) {
+ req[i] = kWriteTo;
+ outputs[i] = &out_arrs[i][output_i].arr;
+ ex_outputs[i] = &ex_out_arrs[i][output_i].arr;
+ }
+ Imperative::Get()->set_is_training(true);
+
+ PrintVerifyMsg(in_arr, out_arrs[0][output_i]);
+ Imperative::Get()->InvokeOp(
+ Context(), forward_attrs.attrs, inputs, outputs, req,
+ DispatchMode::kFCompute, mxnet::OpStatePtr());
+ Imperative::Get()->InvokeOp(
+ Context(), forward_attrs.attrs, inputs2, ex_outputs, req,
+ DispatchMode::kFComputeEx, mxnet::OpStatePtr());
+ Engine::Get()->WaitForAll();
+ AssertEqual(outputs, ex_outputs);
+
+ if (!backwards_attrs.requests.empty()) {
+ TestOpExBNBackward(forward_attrs, backwards_attrs, OpReqType::kWriteTo,
+ inputs, outputs, in_arr, &out_arrs[0][output_i]);
+ }
+ }
+ }
+ }
+}
+
 // Computes second dimension of FC weight matrix based on input shape
 uint32_t GetFCWeightDim2(const nnvm::TShape arr) {
  uint32_t dim = 1;
@@ -1204,4 +1347,10 @@ TEST(IMPERATIVE, DeconvOp) {
  }
 }
 
+TEST(IMPERATIVE, BNOp) {
+ OpAttrs forward_attrs = GetBNOp();
+ OpAttrs backwards_attrs = GetBNBackwardOp();
+ TestOpExBN(forward_attrs, backwards_attrs);
+}
+
 #endif